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IEEE TRANSACTIONS ON ROBOTICS, VOL. 29, NO. 4, AUGUST 2013

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Underwater Reflex Navigation in Confined Environment Based on Electric Sense Frédéric Boyer, Vincent Lebastard, Christine Chevallereau, and Noël Servagent

Abstract—This paper shows how a sensor inspired by an electric fish could be used to help navigate in confined environments. Exploiting the morphology of the sensor, the physics of electric interactions, as well as taking inspiration from passive electrolocation in real fish, a set of reactive control laws encoding simple behaviors, such as avoiding any electrically contrasted object, or seeking a set of objects while avoiding others according to their electric properties, is proposed. These reflex behaviors are illustrated on simulations and experiments carried out on a setup dedicated to the study of electric sense. The approach does not require a model of the environment and is quite cheap to implement. Index Terms—Active sensing, artificial potentials, bioinspiration, electric sense, embodiment, obstacle avoidance, underwater navigation.

I. INTRODUCTION

1) Motivations: In spite of its high potential interest for applications such as deep-sea exploration or rescue missions in catastrophic conditions, underwater navigation in confined unstructured environments and turbid waters where vision is useless remains a challenge in robotics. Under the same conditions, echolocation by sonar is problematic because the multiple small particles, as well as the numerous obstacles, cause diffraction and interfering reflections of the signal. 2) Bioinspiration: In fact, several hundreds of fish species have already discovered an original sense well adapted to this situation: the electric sense. Discovered by Lissmann and Machin in the 1950s [9], the electric sense has been invented twice by nature: once in Africa and once in South America. The African fish Gnathonemus Petersii pictured in Fig. 1 is a typical electric fish. It polarizes its body with respect to an electric organ of discharge (EOD) located at the base of its tail. This polarization, which is of short duration, generates a dipolar-shaped electric field around the fish that is distorted by the objects present in its surroundings. Then, thanks to the many electroreceptors dis-

Manuscript received May 21, 2012; revised January 30, 2013; accepted March 22, 2013. Date of publication April 15, 2013; date of current version August 2, 2013. This paper was recommended for publication by Associate Editor I. H. Suh and Editor B. J. Nelson upon evaluation of the reviewers’ comments. F. Boyer and V. Lebastard are with the Institut de Recherche en Communication et Cybernétique de Nantes, École des Mines de Nantes, 44307 Nantes, France (e-mail: Frederic.Boyer@emn.fr; vincent.lebastard@emn.fr). C. Chevallereau is with the CNRS, Institut de Recherche en Communication et Cybernétique de Nantes, École des Mines de Nantes, 44000 Nantes, France (e-mail: Christine.Chevallereau@irccyn.ec-nantes.fr). N. Servagent is with SUBATECH, École des Mines de Nantes, 44307 Nantes, France (e-mail: Noel.Servagent@subatech.in2p3.fr). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TRO.2013.2255451

Fig. 1. (Top) African Mormyride fish Gnathonemus petersii. (Bottom) Top view of the fish basal electric field. (From [17].)

tributed along its body, the fish “measures” the distortions of the electric field and processes, with its brain, an image of its surroundings [2]. Named “electrolocation,” this sensorial ability has been extensively studied by neuroethologists who have shown that an electric fish can recognize objects’ shape, measure distances, sizes, as well as the electric properties of materials [11], [17]. In nature, the electric fish can easily navigate in the dark or turbid waters of confined unstructured environments such as the roots of the trees of flooded tropical forests, which are their natural habitat. Electric sense is well adapted to this niche, in particular, because of its omnidirectional character that makes it a sense naturally suited to the obstacle avoidance. Thus, understanding and imitating this sense with technology would offer the opportunity to enhance the navigation abilities of our current underwater robots. 3) Related Work in the Field: In this perspective, MacIver et al. have recently used a sensor based on the measurement of the electric voltage through electrodes in order to address the problem of electrolocation of small objects through particle filtering [15]. Their sensor—two-point electrodes between which the difference of potentials is measured—was sufficiently small so that it did not perturb the electric field produced by another pair of point (emitting) electrodes between which the voltage was imposed. In [13], another technological solution is proposed for the electric sense. This sensor is embedded in a realistic 3-D body. Each electrode can be polarized with respect to the others through a given vector of voltage U. The electric field distortions are then measured through the vector I of the currents flowing across the electrodes. We term this measurement mode U –I, the first letter standing for the emission (here, a vector of voltage

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U) and the second for the reception (here a vector of currents I), to distinguish it from the U –U mode of [15] and [16]. 4) Contributions of This Paper: In this paper, we address the problem of the underwater electronavigation in confined environments using this U −I sensor. The proposed approach is inspired by the observation of electric fish in nature. It exploits the interactions of the sensor body with the electric field deformations produced by the objects in its surrounding. It amounts to a set of reactive control loops whose parametrization allows one to achieve relevant behaviors for underwater robotics (as, for instance, the “attraction or repulsion by all objects”) in a robust manner with respect to the scene complexity. Remarkably, the approach does not require a model and allows us to control efficiently a probe in conditions, where neither vision nor sonar could work. This is achieved by exploiting intensively the intrinsic physics of electric sense, as well as the morphology of the sensor (slenderness, symmetry). In this regard, the approach is a nice implementation of the concept of morphological computation [12]. Finally, we hope that these first encouraging results will open new perspectives in constrained underwater navigation. 5) Organization: This paper is structured as follows. First, we will briefly present the sensor technology (see Section II) as well as the test-bed (see Section III) on which the experiments were carried out. In Section IV, we give a model of the electric measurements. This model is used in Section V to propose a set of reactive laws that do not require any model. The solution is then implemented on numerical simulations in Section VI and, finally, on the experimental test-bed in Section VII. Finally, this paper ends with some concluding remarks and perspectives in Section VII.

Fig. 2. (Top) Dipolar electric field generated by a three-electrode probe. (Bottom) Principle of the U –I measurement mode for a probe with an arbitrary number of receptor electrodes.

II. SENSOR Our sensor is described in detail elsewhere [13]. This account serves as a basic introduction for the purposes of this paper. A. Sensor Principle Our first generation of sensors are named “slender probes” because of their high aspect ratio (length/thickness) morphology. Fig. 3 shows a schematic view of the most simple of these probes: the three-electrode probe. It consists of an insulating axisymmetric shell ending by two metallic hemispheres. These hemispheres are constituted of three metallic electrodes e0 , e1 , and e2 . One is located in the tail (e0 ), and the two others e1 and e2 divide the head into a pair of two identical left-right measurement electrodes. Thanks to a voltage generator G within the sensor, the potential on (e1 , e2 ) defines the common ground with respect to which e0 is put under the controlled voltage U [see (2)]. Once the sensor immersed in a fluid, this active device produces a field of current lines flowing through the fluid surrounding the sensor from the emitting electrode e0 to the receivers e1 and e2 . Thanks to a circuit within the sensor, the currents flowing across e1 and e2 are measured and gathered into the vector I = (I1 , I2 )T . When an object is placed in the vicinity of the sensor, it generates perturbations of the pattern of electric lines that can be measured through I. This principle can

Fig. 3. (Top) Schematic view of a three-electrode sensor organized in two polarizable rings, one of them (located in the head) being divided in two half rings allowing two lateral current measurements. (Bottom) Seven-electrode sensor organized in four polarizable rings, three of them (the receivers) being divided in two half rings.

be adapted to a self-propelled mobile body such as an underwater vehicle. More complex designs based on more electrodes have been manufactured as, for instance, the seven-electrode sensor pictured in Fig. 3. This sensor has two additional ringshaped receivers located between the head and the tail. Its total length is 22 cm, for a diameter of 2 cm (aspect ratio 10).

BOYER et al.: UNDERWATER REFLEX NAVIGATION IN CONFINED ENVIRONMENT BASED ON ELECTRIC SENSE

Fig. 4. (Left) Electrolocation test bench. (Right) Example of a set of simple shaped objects (here ellipsoids) of various sizes and material (plastic, metal).

Using standard electronics described in [13], this sensor shows a sensitivity of about ±0.2%. In all the subsequent developments, we have used this sensor but exploited the two head electrodes only in order to recover the features of a three-electrode probe. In Fig. 3, the vertical beam attached to each of the probes represents a stick through which the motion probe is controlled as detailed in the next section. III. ELECTROLOCATION TEST BED A. Tank and Cartesian Robot In order to test our electrolocation sensors and algorithms in controlled and reproducible conditions, an automated test bench consisting in a tank of 1 m3 side with insulating walls and a three-axis Cartesian robot has been built (see Fig. 4). The robot is fixed on top of the aquarium, which allows the probes to be positioned in translation along x and y with a precision of 1/10 mm. The orientation in the (x, y) plane can be adjusted to an accuracy of 0.023◦ using a yaw-rotation stage. The probe tested is positioned in the aquarium at adjustable height using a rigid glass epoxy fiber tube ( 14 mm). This vertical insulating tube allows the passage of electrical cables dedicated to the signals coming from the electrodes to readout electronics (analog chain + ADC board) without compromising the measurements. Its verticality is ensured by a micrometric adjustment base. Our setup allows the trajectories to be controlled over a significant range of speeds relative to the tank size. The maximum speed is 300 mm/s ( 1 km/h) for both x and y translations and 80◦ /s (13.5 r/min) for rotation. B. Monitoring and Control of the Robot The three-axis robot is motion-controlled using Simulink software with the dSpace system. The electrolocation signals coming from the electrodes of the probe are first processed through an analog chain (amplification and filtering) and then converted by a 16-bit ADC, DS2004 card, with a resolution of 0.3 mV/bit. This card allows us to convert 16 channels simultaneously with a maximum speed of 1.25 MHz. C. Objects and Scenes To investigate navigation algorithms using electric sense, a set of test objects of simple shapes has been fabricated from conductive and insulating materials (see Fig. 4). By insulating (or conductive), we mean a material with a conductivity

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γ such that γ > γ0 (or γ < γ0 ), γ0 being the conductivity of the ambient fluid. In practice, plastics are insulating materials, while metals are conductive materials. Defining small objects as objects whose characteristic size (s) is of the order of that of the electrodes ( 2 cm), these objects are all of small size (2 s 5 cm). We can also add or remove insulating walls to the scene. Each scene is constituted of a combination of these objects and removable walls arbitrarily configured within the four fixed (insulating) walls of the tank. The objects are static. The fluid filling the tank is ordinary tap water of our lab, whose conductivity at 25 ◦ C ranges from γ0 = 360 to γ0 = 410 μS/cm. Under these experimental conditions, the sensor has a range of the order of its body length. IV. MODELING A. Model of Locomotion In all the following, we consider the case of a static scene in which the sensor moves. Due to the constraints imposed by the gantry, the sensor moves in a horizontal plane (equatorial plane of the sensor) in which the objects are placed. Furthermore, the controlled motion of the probe is that of a nonholonomic unicycle moving with an axial linear velocity (aligned along the probe axis and oriented positively toward the head) V and a yaw angular one Ω orthogonal to the plane of the scene. This restriction of the sensor’s motion corresponds to the planar motion of a U-boat, which like a swimming fish cannot move laterally while stabilizing its roll velocity to zero. With this assumption, the locomotion model reduces to the kinematic model ⎞ ⎛ ⎞ ⎛ ẋ cos θ − sin θ V ⎝ ẏ ⎠ = ⎝ sin θ cos θ ⎠ (1) Ω 0 1 θ̇ where (x, y, θ)T is the vector of Cartesian pose of the sensor in a fixed frame attached to the plane of the scene. Finally, in the following, (V, Ω) define the control inputs of the sensor whose motions are modeled through (1). B. Model of Perception Even if the reactive control approach does not use any model, the modeling of the electric measurements is crucial, since it gives insights on which the proposed approach is based. As stated in [1], the electric state of the scene surrounding the sensor can be completely parameterized by the electric potential φ solution at each time of the Laplace equations Δφ = 0 with boundaries conditions imposed on the sensor and the objects. Once completed with the constitutive Ohm’s law j = −γ0 ∇φ, which relates the currents flowing in the water j to the electric field −∇φ, Laplace equations are solved in [1] through the method of reflections, as it is originally developed within the community investigating diphasic fluid dynamics at low Reynolds numbers [5]. In this approach, the electric interactions between the sensor and any object, which are parameterized by the electric potential field φ, appear as a superimposition of

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Fig. 8. (Left) Electric lines emanating and returning to an active dipole plunged in a tank with circular insulating boundaries. (Right) Several fish trajectories recorded by a camera. At each of the trials, the fish seeks the active dipole by following its electric lines. (From [14].)

conductive or insulating, while the latter allows us to determine if it is on the left or on the right-hand side of the sensor. V. REACTIVE CONTROL A. Bioinspiration

Fig. 7. BEM (solid lines) and analytical model of Appendix A (dashed line) of the [(a) and (b)] axial and [(c) and (d)] lateral perturbative currents for a conductive (red) and an insulating (blue) sphere (of radius a = 1 cm). The sphere is translated parallel to the sensor axis at a distance do = 5 cm [(a) and (c)] and along an arc of circle centered on the sensor nose at a distance ρo = 5 cm [(b) and (d)]. Since the contrast factor of an insulating sphere is equal to −1/2 while it is equal to +1 for a conductive one, the magnitude of currents are two times higher for a conductive sphere than for an insulating one of same geometry, while their signs are opposite. TABLE I PROPERTIES OF THE PERTURBATIVE AXIAL AND LATERAL CURRENTS δIa x AND δIla t MEASURED BY THE HEAD ELECTRODES WHEN THE OBJECT IS IN THE FRONT PART OF THE SENSOR

currents measured by the front head electrodes δIax and δIlat are recorded. Observing the plots in Fig. 7, we can easily see that δIax and δIlat obey the properties indicated in Table I. In particular, it is worth noting that δIax and δIlat have a complementary role, since the former can be used to determine if the object is

The proposed control strategy is inspired by the observation of nature. When an electric fish is placed alone in an empty tank to which an active (AC) dipole is attached, the fish peacefully navigates without any apparent objective, while the dipole is switched OFF [3], [14]. When the active dipole is switched ON, generating an exogenous electric field in the tank, the fish immediately changes its behavior and begins to seek the dipole actively.1 Furthermore in order to seek the dipole, the fish does not go in straight line but follows the electric lines of the active dipole (see Fig. 8). Although well studied by biologists as an illustration of passive electric sensing, this strategy of navigation has not been observed for active electric sensing [2]. However, we are going to see that this strategy, which is a case of electrotaxis,2 can be efficiently extended to the case of active electrolocation. In this case, the active dipole field of biological experiments should be replaced by the secondary dipoles re-emitted (or equivalently “reflected”) by the objects once polarized by the sensor. This idea plays a key role in the proposed reactive control approach. Before entering into the details of the proposed approach, let us point out that it mixes up and combines the features of several different species of electric fish. In fact, the basal field is in our case an AC field as in South American fish of Gymnotide family, while the shape of this basal field is dipolar as in African fish of the Mormyride family. Moreover, the strategy used for navigation is inspired by passive electrolocation and adapted to active electrolocation. Finally, this should not hide the fact that in nature, active electrolocation is a much more complex process which involves higher levels of cognition. For instance, after a training phase, fish can easily recognize different shapes (cubes, pyramids) made of the same material. In this case, totally different mechanisms should be mobilized [11], [18]. 1 Here, “actively” does not mean that the fish is electrically active; in fact, the fish practices passive sensing, i.e., it just senses the exogenous field, while its EOD is inactive. 2 In short, electrotaxis can be defined as the movement of organisms or cells in response to an electric current or field.

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B. Reactive Control Laws and Behaviors

In order to extend the previous bioinspired strategy to the active case, we first remove the basal component I(0) from the total currents in order to only consider the perturbative component δI. As a result, this operation allows the sensor to measure the presence of the objects in its surroundings as if they were active unsteady secondary3 dipoles.4 Then, since the sensor goes forward in a steady scene, it discovers its environment with its front head electrodes. As a consequence, Table I suggests applying the following control law: V = C and Ω = KδIlat

TABLE II TABLE OF THE FOUR BEHAVIORS (WITH OUR CONVENTIONS OF ORIENTATION: WHEN Ω > 0, THE SENSOR TURNS ON THE LEFT AND WHEN Ω < 0, THE SENSOR TURNS ON THE RIGHT)

(8)

where K is a gain, and C denotes a constant positive value such that the sensor goes forward headlong. Now, from Table I, and with our convention on the sign of the currents and angular velocities, taking K > 0 in (8) ensures that when a conductive object is on the right (respectively, on the left), the sensor turns to the right (respectively, to the left). When a conductive object is in front of the sensor, the sensor goes forward without changing its orientation. This control law forces the sensor to be attracted to any conductive object. On the other hand, if the object is insulating and on the right (respectively, the left), this control law makes it react as if there was a symmetric conductive object on the left (respectively, the right). Thus, the feedback law (8) with K > 0 ensures that the sensor is repulsed by an insulating object. This first law extends the passive attraction to living objects observed in real fish when they are seeking their prey to the case of electrolocation of conductive objects. In particular, since the law (8) steers the sensor in order to nullify the head lateral current, and because this lateral current is proportional to the lateral flux of ∇φ1 across the head electrodes, this law will ensure the sensor to try to align its body along the secondary field ∇φ1 . In short, with (8), the sensor seeks to track the electric field (or current) lines of the secondary field re-emitted by the object after its polarization by ∇φ0 , therefore achieving the generalization of the electrotaxis of Section V-B to the case of active electrolocation. If we want to invert the behavior, i.e., make the sensor attracted by insulating objects and repulsed by conductive ones, we just have to change the sign of K in (8) and take K < 0. Furthermore, if we want to obtain the same behaviors for conductive or insulating objects, it suffices to use the complementarities of the axial and lateral currents of Table I and to multiply K by the sign of δIax , which ensures the sensor, for K > 0, to be attracted by any object and conversely, for K < 0 to be repulsed by any object that is electrically nontransparent (i.e., with a conductivity different from that of the ambient medium). Finally, in order to limit the amplitude of the control input Ω while applying the preceding principles, we will use the following behaviors and corresponding control laws: 1) Behavior 1 (B1): If K = k/δIax , with k > 0, then the sensorimotor loop (8) ensures the attraction of any object 3 “Secondary” is here opposed to the primary dipole generated by the active sensor put under voltage. 4 This unsteady character will be explained in the next section.

of electric conductivity different from that of the ambient medium. 2) Behavior 2 (B2): If K = k/δIax , with k < 0, then the sensorimotor loop (8) ensures the repulsion of any object of electric conductivity different from that of the ambient medium. 3) Behavior 3 (B3): If K = k/ | δIax |, with k > 0, then the sensorimotor loop (8) ensures the attraction of conductive objects and the repulsion of insulating ones. 4) Behavior 4 (B4) : If K = k/ | δIax |, with k < 0, then the sensorimotor loop (8) ensures the attraction of insulating objects and the repulsion of conductive ones. These behaviors (and the corresponding values of K) can be inferred from Table II to which we should add that when δIlat = 0, then Ω = 0. This second table can be deduced from Table I (summarized by the first column of Table II) and by remarking that with the orientation of Ω given by Fig. 3: When Ω > 0, the sensor turns on the left, and when Ω < 0, the sensor turns on the right. Alternatively, these behaviors can be studied in detail with an analytical model (given in Appendix A) of the three-electrode sensor in presence of a simple shaped object (as a small sphere). In the case of complex scenes (several objects of arbitrary shape), such a model is not accessible in a closed analytical form. Thus, in the next two sections, these behaviors are studied in the case of arbitrary scenes through their implementation on experiments and simulations. On another hand, the law (8) is studied in Appendix B in the case of a single spherical object and for the two first behaviors “attracted or repulsed by all objects.” VI. INTERPRETATION OF THE STRATEGY AS A METHOD OF “REAL (NATURAL) POTENTIALS” In order to illustrate the previous control law, we first simulate it under ideal unbounded conditions when it is applied to the three-electrode sensor of Fig. 3 in presence of a small spherical object. The simulator is a fast simulator based on the reduced models of [1] preliminarily validated by our reference BEM code. The numerical parameters of the law are set to V = 0.1 m/s, and | k |= 50.V = 5. When k is defined proportionally to V , it ensures that the sensor follows a path independent of V . The scene (see Fig. 9) reduces to a single

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Fig. 10. Sensor tracks the electric field lines and crosses the isopotential surfaces of increasing values. This ensures δIla t → 0, while δIa x increases if the object is conductive or decreases if the object is insulating.

Fig. 9. Snapshots of the portrait of electric lines in a simple scene. (a) Portrait of the whole electric field at t = 0 s. (b) Basal field is removed and t = 0 s. (c) Idem but at t = 6 s: Note how the sensor aligns itself with the electric field lines. (d) Sensor touches the sphere (t = 11s).

conductive sphere (red) submerged in fluid of conductivity γ0 = 380 μS/cm. Under these conditions, we simulate the behavior “attracted by all the objects5 ” by applying the control law (8) with K = k/δIax and k > 0. This law allows the sensor to seek the closer object or, more generally, the most influential object within range, independently of whether it is conductive or insulating. As expected, the sensor tracks the electric lines of the polarized object and ends by touching it. Fig. 9(a) displays the electric lines of the total electric field ∇φ ∇φ0 + ∇φ1 at the initial time of the simulation, where ∇φ0 is the basal field, and ∇φ1 is the secondary field re-emitted by the object once polarized by the sensor (i.e., by ∇φ0 applied at the center of the object). Because the basal electric field is strongly attenuated by the polarization of the object, the secondary field ∇φ1 is negligible compared with its primary counterpart ∇φ0 , and Fig. 9(a) essentially displays the basal field generated by the active sensor (note that we recover the dipolar portrait of Fig. 2). Fig. 9(b)–(d) only displays the perturbative component ∇φ1 of the field at three later times, i.e., the component which generates our model of δIlat [according to (7)]. More precisely, ∇φ1 is the electric field of the induced dipole p = P.(−∇φ0 )(xc ) (see Fig. 10), with P being the polarizability tensor of the object and xc the object center [6]. The exciting field ∇φ0 (xc ) being rigidly attached to the moving sensor p is generally time varying. Fig. 9(c) clearly shows that the control law (8) ensures the sensor to go forward while progressively aligning its body with the electric lines of the unsteady dipole p. Note that when this alignment is achieved, the sensor axis is aligned with the induced dipole p while pointing its head 5 One

should add electrically contrasted w.r.t. the ambient fluid.

toward the center6 of the object (see Fig. 10). Now exploiting basic properties of harmonic functions [6], since the electric lines are orthogonal to the isopotential surfaces, we can say that equivalently, the sensor tries to remain orthogonal to the isopotential surfaces of the induced dipole centered on the object. As a result, in its attractive version, the control law (8) ensures the sensor to seek an isopotential of extremal value. Thus, since this extremum is centered on the object, the sensor does seek the object until it touches it [see Fig. 9(d)]. On the contrary, in its repulsive version, the law ensures the sensor to turn its back on the extremum of the potential function and, finally, to avoid the object. In the general case, where the scene is composed of several objects of any geometry (not forcedly small), the perturbation field ∇(δφ) (with δφ = φ − φ0 ) induced by the presence of the polarized objects, because it is solenoidal (i.e., its divergence is zero), δφ gets its extremum values on the objects boundaries [6]. As a result, being only related to the electric lines of the perturbative field, the control law (8) will ensure the same behaviors related to any set of objects electrically contrasted in the scene. Finally, the approach turns out to be a natural (real) version of the so-called method of the artificial (virtual) potentials as it is used in mobile robotics for obstacle avoidance [4], [7], [8]. However, the approach here proposed is not used for path planning but is rather a memory-less reflexive approach. Furthermore, the potentials here invoked are not artificially emulated from different sensorial inputs but are actually physical (natural) potentials generated around the polarized objects by the sensor basal electric field. Furthermore, being harmonic functions, these natural potentials cannot make a robot stuck in local minima [4]. Finally, since any object is represented by a contrast of conductivity between its material and water, one can more generally interpret the different versions of (8) as behaviors related to the spatial distribution of conductivity in the scene that the sensor lights up with its basal field. In fact, this interpretation is simple and useful for understanding the different behaviors. All the tests subsequently listed can be 6 In fact, the centroid of the distribution of charges is induced by the polarization of the object.

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easily interpreted from this point of view. In this perspective, the control law (8) can be interpreted as an implementation of the so-called gradient descent algorithm along a relief whose dependence on the local conductivity of the scene is fixed by the choice of the gain K. In particular, when any object generates a basin of attraction independently of its electric nature, the behavior corresponds to the “attraction by all objects.” Conversely, in the case of the “repulsion by any object,” any object generates a local maximum on the relief, while for the two other laws, local maxima and minima exist depending on the electric nature (conductive or insulating) of the objects. In this context, the controlled angular velocity steers the sensor axis along the steepest slopes of these reliefs, while the forward linear velocity pushes the sensor toward their local minima. Finally, when the magnitude of the signal produced by the scene (δI) is of the order of the noise (essentially produced by electronics), the noise adds to Ω an erratic component which increases with k. This naturally occurs when a perturbative object is on the detection limit. In this case, the noise dimensions the range of the sensor [11], [13]. The signal can also drop in case of ambiguities where the sensor in the scene has some symmetries that cancel out the lateral currents. This is the case of two identical objects symmetrically disposed on the two sides of the sensor7 or when the sensor approaches a wall perpendicularly. In these cases, the noise has a virtuous role, since it allows us to disambiguate these pathologic situations. Finally, all the experiments of this paper are carried out in the noisy conditions of our experimental test bed whose noises are characterized in [13]. VII. EXPERIMENTAL RESULTS Here, we report some of the experimental results obtained by applying the control law ensuring the behavior: “attracted by conductive objects and repulsed by insulating ones” [19]. This corresponds to seek the regions of highest conductivity or equivalently to maximize δIax , while δIlat = 0. The experimental conditions are those described in Section III with the seven-electrode sensor of Fig. 3, on which we only use the currents measured by the front head electrode δIax and δIlat . The conductivity of water is about 380 μS, i.e., higher than that of glass and plastic, which are considered to be insulators, and lower with respect to objects made of metal, which are then considered to be conductors. We progress step by step, starting from the case of the tank without any object, to the case of complex scenes where several objects are immersed in different configurations in the tank. As in the previous section, we impose V = 0.1 m/s, and | k |= 50.V = 5. A. Large Insulating Object Immersed in the Tank In order to illustrate the effect of large (insulating) objects, we placed a removable plexiglass wall of 0.38 m length in the tank. Its position is shown in Fig. 11(a). From the initial position A to its B position, the sensor seeks the most conductive region within its range. The robot subsequently initiates a cyclic 7 Pursuing the analogy of the gradient descent algorithm, in this case, the sensor in on a col.

Fig. 11. Experimental results. Control law “attracted by the conductors and repulsed by the insulators” for a tank with an insulating wall in its middle. (a) Sensor path for initial position A. (b) Time evolution of (top) axial and (bottom) lateral currents.

trajectory [see Fig. 11(a)] along which the current remains maximum. According to the interpretation of Section VI, the sensor navigates in the “closed valley” between the boundaries of the tank and the “local mountain” generated by the presence of the removable insulating wall where δIax is maximum. At points C and D, the axial current [see Fig. 11(b)] is the weakest of the closed sensor path. Indeed, at these positions, the sensor passes through the regions which are the most confined by the insulating walls. Note that at C, the sensor is closer to the removable wall than to the tank’s walls. At this point, the edge of the removable wall is being sensed; therefore, it is less influential than at B, where both walls have similar effects on the measures. Indeed, in this case, the sensor is approximately equidistant from the removable and fixed walls. B. Two Insulating Walls, One Drilled With a Hole This scene is composed of two removable plexiglass walls, one of which is drilled with a hole (of diameter 10 cm) whose center is in the equatorial plane of the sensor. The geometry of the scene is shown in Fig. 12(a) (hole in red). From the initial position A to B, the sensor seeks the maximum of sensed axial

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Fig. 12. Experimental results. Control law “attracted by the conductors and repulsed by the insulators” for two insulating walls with one with a hole. (a) Sensor path for initial position A. (b) Time evolution of (top) axial and (bottom) lateral currents.

Fig. 13. Experimental results. Control law “attracted by the conductors and repulsed by the insulators” for a tank with several objects: three insulating spheres and a conductive one. (a) Sensor path for initial position A. (b) Time evolution of (top) axial and (bottom) lateral currents.

currents. After C, the sensor enters a region confined by the insulators which causes a decrease in the axial currents [see Fig. 12(b)]. At D, the sensor rotates in order to escape from the dead end it has entered. At E, the lateral current changes sign, indicating the detection of the hole in the drilled wall. Just after E, it enters the hole. As it enters the hole, the strong restriction of the conductive space around the sensor causes a sudden drop in δIax . When the sensor emerges from the hole on the other side of the wall, the ambient conductivity increases, as well as the measured axial currents. Note that in this test, the sensor navigates in a globally resistive scene (δIax < 0) but is still able to detect the hole, which appears as an attractive conductive object to the sensor.

From C to D, the sensor seeks the most conductive region. From D to F , the sensor locks itself in a corner. Point E is the position at which the effect of the corner on the sensor is least. At point F , the lateral current increases [see Fig. 13(b)], since the sensor senses the insulating sphere on its left side. From F to G, the sensor seeks the most conductive region of the scene. After G, the sensor senses the conductive object (δIax > 0) and, finally, touches it. Note that due to the softness of the reactive control law (k = 5), the front electrode touches the sphere, while the sensor axis is not aligned on the sphere center (see Appendix B). Finally, when the sensor touches the sphere, a short circuit occurs and the currents blow up.

C. Several Insulating and Conducting Objects Immersed in the Tank At its initial position A, the sensor is immersed in a resistive very confined scene (insulating sphere placed in left top quadrant of the tank with a conductive sphere close to the center) [see Fig. 13(a)]. At B, the sensor comes closer to the insulating sphere and the current strongly decreases [see Fig. 13(b)]. From B to C, the sensor gets out of the confined resistive region.

VIII. CONCLUSION AND PERSPECTIVES This paper has addressed the problem of reflex underwater navigation in confined environments. It is based on a sensor recently described in [13]. This sensor is inspired by a natural mode of perception named electric sense. In fish, electric sense is based on the measurement of the distortions by the surrounding of a self-generated electric field. These distortions are measured by a distribution of transcutaneous electroreceptors

954

IEEE TRANSACTIONS ON ROBOTICS, VOL. 29, NO. 4, AUGUST 2013

δIlat

A sin α = ρ5 =

Fig. 14.

Parameters of a simple scene in the case of a three-electrode sensor.

which can be considered to be a retina or a skin, depending on whether one point out the visual or haptic modality of the electric sense [2]. The approach is reactive and based on the physical properties of the electric sense. The navigation problem is conceived as a low-level reflex of the robot. Starting from a bioinspired approach, we adapted the navigation strategy of fish, which passively follows the electric lines of active dipoles to seek their prey, to the case of active electrolocation. To that end, the active fixed dipoles of the passive case have been successfully replaced by the secondary unsteady dipoles induced by the active polarization of its surroundings by the moving sensor. Thanks to recent results in electric modeling [1] and exploiting the morphology of the sensor (in particular its symmetries), it was possible to implement simple sensor-based control loops on currents. These control loops achieve relevant behaviors for robotics as seeking conductive objects or avoiding all electrically contrasted objects. This approach requires no model and exploits the haptic modality of electric sense. From this point of view, electric sense can be considered as touch without contact [10]. What is interacting here with the surroundings is not the material body (as in haptic) but the electric field generated by the sensor (or the fish), which can be interpreted as an immaterial extension of the sensor body. Pushing this view point further, electric sense allowed one to replace locomotion by manipulation (of this electric body). For example, when seeking conductive objects, the sensor generates a web of electric lines whose lines converge toward the objects. Thus, as a man groping for a door in the dark only moves his body toward the door’s handle after he has grasped it, the sensor pulls its body along these lines in order to find the objects. In the context of an EU funded project named Angels, we have implemented these behaviors on a real autonomous underwater robot. In future, we want to combine this reactive navigation with model-based shape recognition, map construction, or with control laws of higher level. Such a law could be based on path planning and the reactive law of this study would then be activated as soon as an obstacle is detected (e.g., with a threshold on the currents). APPENDIX A CLOSED FORM OF THE MODEL OF PERTURBATIVE CURRENTS FOR A THREE-ELECTRODE PROBE IN PRESENCE OF A SPHERE In this Appendix, we consider the three-electrode probe of Fig. 3 immersed in a scene composed of a single small sphere whose location with respect to the sensor is defined by the polar coordinates (ρ, α), as displayed in Fig. 14. In this case, δIlat can be computed by hand from the general model of [1] and written in closed form as

2g 3 − 3lρ2 cosα − 2ρ3 g3

I (0)

A sin α f (ρ, α)I (0) ρ5

(9)

where A = (f1 χa3 A)/(4π), a is the sphere radius, χ is its contrast factor, A is the cross-sectional area offered by the electrodes e1 (or e2 ) to the lateral field ∇φ1 , l is the distance between the two hemispherical tips (total length of the sensor), g(ρ, α) = (l2 + ρ2 + 2ρlcosα)1/2 is the distance between the sphere and the tail electrode, and f1 is a shape factor related to the area of the wetted surface of e1 and e2 . The dimensionless function f of (9) is strictly positive for any value of the parameters in the range of the sensor (ρ l). The current δIlat has two roots: α = 0 or π, i.e., when the sensor axis is directed toward the center of the sphere with its head (α = 0) or its tail (α = π) facing it (see Fig. 7). In the same way, applying the general modeling approach of [1] to the three-electrode sensor, we find the model of the axial currents, which is given by B ρ4 + g 4 − ρ2 g(ρ + lcosα) δIax = 4 I (0) ρ g4 =

B h(ρ, α)I (0) ρ4

(10)

where B = (f2 χa3 R)/2, R is the radius of the probe, and f2 is a shape factor related to e1 or e2 and deduced from a preliminary calibration phase. Let us remark that for any (ρ, α), the dimensionless function h of (10) satisfies h > 0. Hence, the sign of δIax is determined by that of χ, i.e., by the electric nature of the material immersed in the ambient fluid. As a result, if one adds a resistive object (χ < 0) to the scene, Iax decreases (δIax < 0), while if one adds a conductive object (χ > 0), Iax increases and δIax > 0, as displayed in Fig. 7. Finally, let us note that this model is based on the approximation of far field, i.e., it works when the sphere is not too close to the sensor (“close” meaning at a distance of about the sensor diameter). However, as shown in [1], it continues to be accurate even in the near field conditions. Moreover, as shown in this paper, the reactive law works till the sensor touches the object, in which case, the model can dramatically change (e.g., a short circuit occurs when the object is conductive). APPENDIX B ANALYTICAL STUDY OF THE STABILITY OF THE CONTROL LAW (8) In this Appendix, we study the reactive control law (8). We start with the case where (8) ensures the behavior “attracted to any object,” i.e., with K = k/δIax and k > 0. This is achieved while the scene around the sensor is reduced to a single small sphere. In this case, a straightforward analysis allows deriving the kinematics which relate the parameters (ρ, α) of Appendix A to the control variables (V, Ω): α −cosα − l sin V ρ̇ 2 . (11) = sinα l cosα − − 1 Ω α̇ ρ 2ρ

Positioning, 2011, 2, 65-77 doi:10.4236/pos.2011.22007 Published Online May 2011 (http://www.SciRP.org/journal/pos)

1 Machine Perception Through Natural Intelligence Rostyslav Sklyar Verchratskogo st. 15-1, Lviv 79010 Ukraine Email: sklyar@tsp.lviv.ua Received December 30th , 2010; revised March 1st , 2011; accepted April 20th, 2011.

ABSTRACT

2 4

The sensing organs are exponentially better than any of analogous artificial ones. That is why using them in full scale is a perspective trend to the efficient (advanced) machine perception. On the other hand, limitations of sensing organs could be replaced by the perfect artificial ones with the subsequent training the nervous system on their output signals. An attempt to lay down the foundations of biosensing by natural sensors and in addition to them by the artificial transducers of physical quantities, also with their expansion into space arrays and external/implantable functioning in relation to the nervous system is performed. The advances in nanotechnology are opening the way to achieving direct electrical contact of nanoelectronic structures with electrically and electrochemically active neurocellular structures. The transmission of the sensors’ signals to a processing unit has been maintaining by an electromagnetic transistor/memristor (externally) and superconducting transducer of ionic currents (implantable). The arrays of the advanced sensors give us information about the space and direction dynamics of the signals' spreading.The measuring method and necessary performance data of the sensor for the robot’s orientation in the ambient magnetic field with living being-machine interaction in order to obtain input and output signals from brain and motor nerves to the measurement system and vice versa are introduced. The range of applied sensors differs from an induction sensor to superconducting induction magnetometer. The analytical expressions for arrangements of the head sensors in differential and vector (3D) relative positions are deduced. Sensitivity of the perception method makes it possible to recognize the linear translation of 10!2 m and disposal in space of 10!3 m3. Interaction between living beings and robotic equipment is given analytical treatment. Keywords: Magnetic Field, Induction Sensor, SuFET, Nerve Impulses, Interface, Gradiometer, Sensing Area

1. Introduction. Artificial Sensors with the Human Machine Interface Electronic Nose is a smart instrument that is designed to detect and discriminate among complex odours using an array of sensors. The array of sensors consists of a number of broadly tuned (non-specific) sensors that are treated with a variety of odour-sensitive biological or chemical materials [1]. This instrument provides a rapid, simple and noninvasive sampling technique, for the detection and identification of a range of volatile compounds. The key function of an electronic nose is to mimic human olfactory system. Typically an electronic nose consists of three elements: a sensor array which is exposed to the volatiles, conversion of the sensor signals to a readable format and software analysis of the data to produce characteristic outputs related to the odour encountered. The main parts of a typical biosensor are shown in Figure 1. The artificial tactile sensor integrates a micro electro-mechanical system (MEMS) array having a number

of sensing elements (16 channels in about 20 mm2) similar to the innervation density of mechanoreceptors in the hand (about 1 unit/ mm2). The technological approach is based on a 3D MEMS core unit with a soft and compliant packaging. The microsensor can be integrated with a packaging architecture resulting in a robust and compliant tactile sensor for application in artificial hands, while sensitive enough to detect slip events, showing that silicon based tactile sensors can go beyond laboratory practice [2]. The tactile sensor array, depicted in Figure 2, had 16 channels as total tactile sensor outputs. The measurement of magnetic fields (MFs) is an important task for the majority of autonomous missions. The distribution of permanent and the value of periodical MFs give the data about placement of ferromagnetic objects and sources of EM radiation respectively. On the other hand, these signals will be a reference point and guiding line for a walking robot (Figure 3). Detection of some magnetic anomalies of the Earth’s MF and their variations is provided by fluxgate sensors

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Figure 1. (A) The main parts of a typical biosensor, (B) A sensor array, each polymer changes its size and therefore its resistance, by a different amount and making a pattern of the change. Copyright 2009 IFSA.

Figure 2 (a) 3D design of the tactile sensor array. (b) Top: the 2 × 2 MEMS array compared with human finger; bottom: a FIB image of the MEMS sensor. (c) Top view of the sensor array. Copyright 2008 IEEE.

Figure 3 The walking robot in MF environment. a) x-y plane defines the varying DC natural MF; b) around z axis spreads AC industrial MF interference.

[3]. When the spectrum of EM signals in the environment lies in a wide frequency range (10 Hz - 500 kHz),

the application of highly-sensitive induction sensors (ISs) is necessary. The application of a low-Tc SQUID device is able to embrace both of the said frequency ranges with maximum sensitivity. It is made of non-magnetic material and the principle of operation is to detect two magnetic signals at different distances from the source and arrange for them to be in opposition around a local supercooled circuit. This ‘gradiometer’ approach eliminates most of the noise—caused by spurious MFs originating from, for example, electrical devices or natural (geomagnetic sources. A typical unshielded laboratory has a noise level in the 0 - 10 Hz frequency region of about 10!7 T and geomagnetic noise in the same frequency range is of the order of 10!10 T. Any small field changes the direction of the MF vector in the space and this produces a distortion in the waveform of the signal in the detection coils. The very expensive use of the SQUID magnetometer up until now has produced many advances in the understanding of MFs from weak sources. The advance of much cheaper room—temperature sensor technologies offers the prospect of much greater use of MF monitoring [4]. As a general rule, scientific instrumentation should impact mass constraints as little as possible; however, robot-mounted instruments in particular must be lightweight to survive deployment under highstress conditions when used in connection with spacecraft or underwater. This weight constraint can limit both the size of the sensor and its placement on moving part. Also, autonomous systems require minimal consumption power. Both factors translate into severe constraints on the capability of the instrument to measure weak EMFs encountered in the environment. In addition, outer space and underwater conditions require endurance against variations in the wide range of temperature, humidity, and atmospheric pressure. The human machine interface using electromyjogram (EMG) signal to artificially control the limb movement. Each muscle fiber has a potential and motor unit action potential generated by construction of muscle is studied and corresponding actuation is provided to robotics arm. In this system a relatively more flexible and robust communication technique generates commands from EMG signals (Figure 4). Such an autonomous mobile humanoid robot will be able to assist in a workshop environment and interact with a human [5]. Three aspects to reach this target are considered. First, how the brain of the exoskeleton robot is constructed to be able to manipulate objects like humans do. Second exploration of appropriate binominals that can be used as an actuating quantity to move the Robotic arm; and finally how programming of manipulation tasks is realized. Implantable neural probes are generally preferred to

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Physiological Signals is sketched in Figure 9. The measurement system consists of a transducer to pick the human physiological signal, amplifier system, hardware band-pass filter, fifty hertz notch filter, DSC and a personal computer as the output display device. An example of further processing of the sensing signals is analogous to the combination of heart rate and body position information recorded or monitored simultaneously can provide a valuable set of physiological and behavioral data related to the physical activity and cardiac activity. The added knowledge of the subject’s body position and activity intensity provides very valuable information and creates a history for monitoring, diagnostics, or health alerts. A discrete and minimally obtrusive wearable device allowing synchronous measurement of these physiological parameters can further offer enhanced mobility, for the very first time, to the collection and monitoring of these datasets for both research and practical applications [18]. The acceleration and bio-potential signals taken from the multi-functional sensor are first amplified through a custom signal conditioning circuit. The resulting signals are then inputted into the analog-digital converter (ADC) used to digitize the output signals. Figure 10 shows an overview diagram of the accelerometer data collection and processing system. This prototype device is able to detect different body positions such as standing, sitting, and lying on different sides of the body. It also processes motion sensor data by calculating the variance of the sample data, in order to determine movement intensity.

3. IS as the Organ of Sixth Feeling Taking into account the said restrictions, it is undesirable to use in the robot a fluxgate sensor due to its active type of action. Also is impossible to exploit a SQUID device since it needs a liquid helium container. SQUID systems being almost ideal for the detection of very small amounts of flux from small samples, but less well-suited to the detection of low flux densities (i.e. where very low MFs are encountered). The high coil inductance of the induction system, on the other hand, can couple to a large quantity of flux [19]. It appears that these difficulties frequently reduce, in principle, the performance of SQUID magnetometers to a level below that demonstrated in this article, using an ambient-temperature induction system. This IS is also compact, robust, operates

at room temperature, exhibits a wide dynamic range, and may be easily integrated into differential or multiple sensor gradiometric configurations which are feasible in a multi-limb walking robot. A new method to assess a body inclination/azimuth using 3-D Earth’s MF sensor, even in dynamic conditions was proposed [19]. Indeed, a magnetometer is insensitive to acceleration while responding to a change of orientation in the 3-D space. However, in the general case, a 3-D magnetometer is not sufcient to track a 3-D orientation. Magnetometers are more commonly employed to either determine a compass heading information or to compensate a rate gyroscope integration drift. The main point is that by restricting ourselves to particular movements with one or two degrees of freedom (DOFs), a magnetometer is sufcient to estimate 2 DOFs: the movement is constrained in one plane which can rotate around one xed (or not) axis. For example, during walking, we can be interested in rotations in the saggital plane (e.g., knee exion/extension) and azimuth direction of walk. Although limited to two DOFs, it is important to note that we can address a movement in 3-D space. Figure 11 presents the different normalized accelerations measured in t!hme sensor frame: the calibrated measured ! " acceleration v acc : dark "! solid line, the predicted gravitational component g B : !dotted line, and the computed kinematical component a B : light solid curve.

3.1. Placing of the PCs on the Limbs PC of a walking robot is connected in parallel with the drain of a SuFET cryogenic device or an ordinary OA which are placed in the body. A PC realizing the oscillatory!forward movement along both AC industrial interferences and quasi-DC natural (Earth) environmental MFs. These fields are distributed on a surface and in space roughly according to Figure 3. The movement in quasi-DC MF HDC with the defined speed v and oscillating frequency ! with a magnitude "" gives e.m.f. from PC:

EPC & SN #$eff $0 H DC sin % ' (%

(1)

where S=µeff # d2 N/4 with µ0- the permeability of free space, µ0=4#·10-7 henry/meter; µeff- the effective relative permeability of a high-µ metal core; d- the average diameter of a PC; N-total turn number of the solenoid; "-

Figure 9. Block diagram of DSP based real time embedded system for acquisition of bio-signals. Copyright 2009 IFSA. Copyright © 2011 SciRes.!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!POS

Machine Perception Through Natural Intelligence

the frequency !limb of limbs' oscillations produce an e.m.f. in PC: EPC " BPC !lim b SN

Figure 11. Separation of the gravitational and kinematical components in body frame. Accelerations during a sitto-stand transition: measured acceleration (solid line), predicted gravitational component (dotted line), and computed kinematical component (dashed line). xb-axis (top) denotes the craniocaudal axis, pointing upward; zb-axis (bottom) is the anteroposterior axis, pointing forward. Copyright 2007 IEEE.

an angle between PC’s magnetic axis and the vector of HDC. Further the amplifying/processing circuit depends on the measuring conditions and can vary from the simplest of the ordinary IS modifications to the superconducting one [21]. 1) The design of the MF transducer Some combined device, that includes all the best features of the said MF sensors/transducers seems to be the prefered trend for further development as a perception organ [21]. The SuFET is implemented into a wide-band IS device in order to acquire the sensitivity threshold below 1fT/!Hz in the frequency range from small values of Hertz to tens of MHz (0.1 Hz!"!107 Hz). The proposed superconducting induction magnetometer (SIM) circuit consists of both a room-temperature or a cooled (up to superconductive) PC and a SuFET. Moreover, it gives the opportunity of repudiating both windings and electronics of feedback loops that are used in the known magnetometers. Magnetic induction BPC of AC MF with

(2)

where S- a cross-section of PC, N- its number of turns. All AC MFs with the frequencies !, high than !limb can be rejected by the passive HF filter [22].On the other hand, the value of EPC can be determined from the output voltage Uout of the specific kind of IS [21] with its known transfer function G according to the formula: U out " GEPC

(3)

As a result, an output signal receiving spontaneously, during two-dimensional travel of a walking robot in a quasi-DC MF. Moreover, by picking up the signals from both horizontal and vertical parts of the limbs, the robot derives its’ directional information from the axial course of the field lines and their inclination (defined as the angle between the direction of the field lines and the horizontal) in space. Executing the oscillations of PC with parameters (number of turns N = 2!104 and a crosssection area S = "!10#4 m2) [13] in the earth MF B0 = 50 mkT [23] with the frequency 2 Hz and " = 30, $" = 30 arouse e.m.f. with a magnitude 50 mV according to Equation. 1. This e.m.f. will be on average equal to 5 mkV for the possible variations of the said MF with magnitude 1 - 10 nT. With these data it is possible to calculate the value of Uout for all the known five of ISs [13] with given parameters of their electrical circuit. So then (see Table 1 and Figure 4): a) for the basic transducer's variant with PC's resistance R = 2.4 kOhm, inductance L = 30 H and capacitance C = 50 pF the values of Uout shown in the second column of Table 1; b) the voltage feedback resistance Rfb = 27 kOhm is introduced into the circuit; c) the magnetic flux feedback turns Nfb = 1000 are introduced into the device; d) for SuFET the constant partial of gate voltage equal to 0.1 V and !T, which closely relates to the small signal transconductance, are defined [13].

3.2. Using an Ambient DC and AC MFs for Robot’s Perception An output signal of the sensor will be involved into the differential (gradiometric) operation between the robot’s limbs. After envelope detection of the quantity Uout, can be presented by changing the corresponding quasi-DC MF by the robot’s movement as defined in Figure 12. In Table 1.The dependence of signal’s value Uout from the varying MF BPC. BPC\Uout

50 mkT

10 V

70 mV

1.4 V

3 mcV + 0.1 V

5 nT

1 mV

7 mcV

0.14 mV

0.3 pV + 0.1 V

Machine Perception Through Natural Intelligence

variation of the MF, has been described [24]. The finite distance d between the magnetic sensors for detecting the field difference is used to get an expression for the estimate of the exact magnetic gradient, adjusted by the function of the field distance, !Bz = Bz (z + (1/2)d) ! Bz(z ! (1/2)d), as follows: $Bz

Figure 12. The variations of measured MF strength HDC, HAC: 1-oscillations of a PC modulated by variations of external natural MF; 2-an envelope of sensor’s output voltage UDC as the appropriate quasi-DC MF along the walking way; 3-changing of an AC industrial MF interference into the travel space; 4-the integral output voltage UAC which determines changing of the interference's power by a distance.

a similar manner, way an AC MF partial can be shown. The linear travel of the robot can be derived from the known distance between any two limbs and measured during the movement gradient of MF !H, which is presumed constant. Otherwise, the robot can be walking in the direction of minimal or maximal MF strength H according to the said gradient (Figure 13). In both cases the precision of movement will be defined by the sensors’ sensitivity, which lies in a range of the orders from pT/"Hz to fT/"Hz. 1) Determination of the earth’s MF gradient A portable single axis magnetic gradiometer, which is a relative instrument because it measures the spatial

9 Figure 13. 3D Orientation of the walking robot in an environmental MF: a) distribution of the detected gradients of DC natural MF over the x-y plane; b) distribution of DC MF interference in the space around z axis as a difference between the limbs.

' md 2 #Bz &5 0 6 d 4" z

(4)

where z is the distance from the dipole to the center of the gradiometer probe and m the magnetic moment of the dipole. In the following case, the oscillations of the limbs are taking place according to the law closer to harmonical. Then the magnitude value of magnetic induction, which produced by oscillations of PC in the ambient DC MF Bz is: BPC ) Bz * sin ( * #(

(5)

where ! is an angle between PC's direction of detection and vector of B, !! is a range of a value of this angle variation. Having substituted Equations. 1, 3, 5 into Equation. 4, we have the dependence between signals from PCs of any two limbs and the relevant magnetic gradient occurs: $Bz

U out .0+ z & 1 2 , ! + z ! 1 2 , /1 ' md 2 &5 0 6 G-lim b SN sin ( * #( 4" z

(6)

A first order radial gradiometer consists of two axially displaced magnetometer loops, wound in an opposing way from a common wire that is connected to IS. Two radial magnetometers with opposite polarity can be connected together to form planar gradiometers [24]. Such devices detect the tangential gradient of the radial field, and two gradiometers are usually used at each site to detect two orthogonal planar gradients. The planar gradiometer behaviour is qualitatively different from that of the radial gradiometer. 2) Measuring of the MF signal in a triaxial arrangement A flux transformer detects more environmental noise if its baseline is long (or if it is a magnetometer). Thus long baseline gradiometers detect not only stronger signals from deep sources, but also larger environmental noise. The noise parameters for different baselines were measured and are shown graphically [25]. Gradiometers can also be configured to detect the radial gradient of the tangential MF. Two orthogonal ‘tangential radial gradiometers’ and one ‘radial gradiometer’ can be combined to form a first-order gradiometer equivalent of the vector magnetometer. The vector of the industrial or household man-made AC MF (noise) # can be measured during the

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complete pass of the robot’s walking. Placing of the PC’s triplets (the three orthogonal components at each location) on the respective limbs give the necessary data for the triaxial MF determination according to the geometrical summation. In such a case, the frequency of the limb’s oscillations is much lower than ambient MF noise. That is why, they do not influence the measured components and, moreover, these oscillations can be additionally suppressed by a passive LF filter [26]. The value of MF induction along a single component will be calculated similarly to Equation. 1 and Equation. 2 by the formula: ! SNU out BPC " (7) G The dependence of volumetric error on the baseline for environmental noise is shown [25].

3.3. Experimental Validation of the Method

In the Figure 6 are shown the oscillations of an electromotive force 2-3 of the described PC with respect to the basic voltage 1, which are conform to MF B0 with an onward movement speed of IS 1 m/s. The signal-wave envelope 4 is a result of the informational-measuring process and confirming about the space distribution of the MF variations, which are bases for the advanced perception-guide abilities. The levels of the IS’s output signals and boundaries of their changes in reality for all the described devices’ variants are shown in Figure 14. In order to improve reliability of the method and increase the working frequency intensity signal filtration outside, a working frequency is introduced. Interference suppression is achieved by introducing n-unit RC-filters into the input stages of preamplifier intensification. The equation for LF filtration is of the sixth order relative to RC: - 1 & 6 2 * 2 '(+ # RC!L $ ) 32 # RC!L $ ' 16 " 0 % , L .

tively “cut-out” from the rest of oscillations, both of EM and mechanical origin [26].

3.4. MF Feeling with a Living Being-Machine Interface The design provides a means to update operating-, monitoring parameters, operational thresholds, and sensor and RF link specific firmware modules “over-the-air”. It is composed of two main components - a sensor- wireless hardware interface and system integration framework, which facilitates the defining of interaction between sensors/actuators based on process needs. The intelligence necessary to process the sensor signals, monitor the functions against defined operational templates, and enable swapping of sensor and RF link, resides on the microcontroller of the hardware interface [27]. The basic scenario is shown in Figure 16. As a result living beings control drives by previously translated biosignals. In the other variant, biosignals from organs of the senses or brain transduce directly into intelligent or robotic systems which, in such a way, pick up environmental information. Let us consider the connection between the input (biosignal Inerve) and output (MF B) variables of the autonomous system in the wide frequency range "B:[28]

(8)

where !L and "L are the interference suppression factors. The experimental curve, given in Figure 15 confirms the computed values of suppression a transfer function G for a two-unit RC-filter. It is also possible to apply a twin-T RC-filter, but only for the HF- fH, relative to the working one fwork [22]. The response curve in the frequency range that is of importance for orientation, would be virtually flat GF, when R and C are related to each other as follows: 1 " f work (9) 2/ RC As a result, the working MF frequency will be effec-

Figure 14. The magnitude changes of orientate on MF frequency during the walking path.

Figure 15. The simultaneous effect of both LF and HF filters on interferences in wide frequency range.

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Figure 18. (A) Cross section and (B) illustrated diagram of the !ight muscles powering the up- and downstroke of Manduca sexta wings. The tips of the flexible probe in (A) target the !ight powering muscles dl and dv (B). SEM image of the flexible-probe tip with expanded image of the ground and actuation pads can be seen in (C). The hole at the tip is opened for muscle growth. Copyright 2009 IEEE.

Figure 19. Actuation probes were also used to record muscle potentials as an indication of efficient tissue–probe coupling. Potentials recorded from the dorsolongitudinal muscles (multiple-cell recording with 50 – 100 times ampli"cation) have spiking frequency similar to wing-flapping rate (~23 Hz). Observed spikes disappeared immediately when wing flapping stopped. Copyright 2009 IEEE.

Multiprocessor data fusion is in effect intrinsically performed by animals and human beings to achieve a more accurate assessment of the processing environment. The aim of signal processing by the combined artificial-living being multiprocessor system is to acquire complete information, such as a decision or the measurement of quantity, using a selected set of input data stemming to a multiprocessor system- digital data are coming to artificial processor and the rest of information consumes by a neural system of living being. Thereby, a big amount of available information is managed using sophisticated data processing for the achievement of a high level of precision and reliability.

It is possible to substitute the microcomputer in an object-oriented problem solution scheme by the natural processing organ- brain or spinal cord. As a result, the software component will be eliminated and the most general characterization of the processing problem in one-coordinate dimensional calculations could be acquired naturally, according to the feedback reaction on the input exposure for calibration, error correction, scaling up or down, range extension, sampling, resolution, etc. Multisensor data fusion is in effect intrinsically performed by animals and human beings to achieve a more accurate assessment of the surrounding environment. The described intelligent system is introducing a new multisensor-brain interface [9]. High-channel count recording microsystems are needed to record from a growing number of sites in the central nervous system. These devices are intended to provide high investigation accuracy in neuroscience experiments, and retrieve the control signals in prosthetic applications [7]. They are mainly composed of a microelectrode array assembled and enclosed with integrated circuits (ICs), implementing the active part. Figure 20 shows the output sequence responses of the ASICs when the constructed test signals are attenuated and used as input signals to the multichip interface. Coverage can be achieved by designing some kind of density control mechanism, that is, scheduling the sensors to work alternatively to minimize the power wastage due to the overlap of active nodes’ sensing areas [31]. The sensing area of a node is a disk of a given radius (sensing range). The sensing energy consumption is proportional to the area of sensing disks by a factor of !1, or the power consumption per unit. Then, the sensing energy consumption per (unit) area (SECPA) is E = !1"D, and for model we have: E # !1" 2 $ 1.57 % !1

(5)

If every four equal neighboring disks in model are tangent, then there is a gap between them (see Figure 21). That uncovered space may be covered by one extra disk. If the energy consumption of a disk of radius r is proportional to r2, n ! 2, then one could save energy by decreasing the radius of an extra disk. In order to decrease the radius r of an extra disk and to preserve the coverage, the neighboring disks of radius R must overlap. Though this overlapping may be small, the radius r may decrease gradually. We seek an optimal radius r for the extra disk, and this determines the extent to which the disks of radius R overlap. This case is intermediate between the previous models, and we will illustrate its advantage over them. The tile is a square with vertices in the centers of four neighboring disks of radius R (see Figure 4, 6). Note that in that case, the diagonal disks do not intersect, but all

Machine Perception Through Natural Intelligence

Figure 20. Measured outputs of the different stages of the multichip interface. output signal of the conditioning circuits (top), reconstructed output of the ADC (middle), and reconstructed output of the digital ASIC (bottom). only detected waveforms are captured and transmitted. Copyright 2009 IEEE.

5. Results

these four disks are the neighbors of an extra disk of radius r. As a result, we have the ratio of the total area Sf = 6!R2/5 of the parts of disks inside the tile divided by the area Sp= 16 R2/5 of the tile, and the coverage density D = 3!/8 " 1788, and E " 1.178 !1.

1) It is possible to apply a twin-T RC-filter for combating such interferences and noises, but only for the FR on the high or decreasing part of the transfer function (TF). The improvement of metrological performance data from the induction MF transducer by suppressing the considerable interferences in the low part of its frequency range where TF is rising was proposed. Suppression of such interferences is achieved by incorporating n-unit RC-filters into the input circuits of the transducer with a negative magnetic flux feedback loop; 2) A magnetometer-based motion capture system that can address several usual human movements has been presented. This approach is well suited to planar movements which are classically performed by humans. Furthermore, in an advantageous way, this angle information with an accelerometer to separate the gravitational and kinematical components in the accelerometric signal can be combined; 3) Recent developments in bioengineering, nanotechnology, and soft computing make it possible to create a new generation of BCI. There are developing opportunities for combining natural and artificial sensing abilities in the synthesized system. It is generally commented that there exists an overwhelming variety of nanosensors. Taken as a whole, nanosensors are providing comparable

Table. The similar fragments which are highlighted in two attached articles. #

R Sklyar “Machine Perception Through Natural Intelligence”

Frédéric Boyer, Vincent Lebastard, Christine Chevallereau, and Noël Servagent “Underwater Refex Navigation in Confned Environment Based on Electric Sense”

Title- ... Perception Through Natural Intelligence

Title- .. Navigation ... Based on Electric Sense

abstract-

An attempt to lay down the foundations of biosensing by natural sensors and in addition to them by the artificial transducers of physical quantities, also with their expansion into space arrays and external/implantable functioning in relation to the nervous system is performed.

abstract- Exploiting the morphology of the sensor, the physics of electric

Both factors translate into severe constraints on the capability of the instrument to measure weak EMFs encountered in the environment. In addition, outer space and underwater conditions require endurance against variations in the wide range of temperature, humidity, and atmospheric pressure. [p. 66]

In spite of its high potential interest for applications such as deep-sea exploration or rescue missions in catastrophic conditions, underwater navigation in confned unstructured environments and turbid waters where vision is useless remains a challenge in robotics. [p. 945]

The arrays of the advanced sensors give us information about the space and direction dynamics of the signals' spreading. The measuring method and necessary performance data of the sensor for the robot’s orientation in the ambient magnetic field (MF) with living being-machine interaction in order to obtain input and output signals from brain and motor nerves to the measurement system and vice versa are introduced. [abstract]

Electric sense is well adapted to this niche, in particular, because of its omnidirectional character that makes it a sense naturally suited to the obstacle avoidance. Thus, understanding and imitating this sense with technology would offer the opportunity to enhance the navigation abilities of our current underwater robots. [p. 945]

Some combined device, that includes all the best features of the said Mf sensors/transducers seems to be the prefered trend for further development as a perception organ. [p. 70]

In this paper, we address the problem of the underwater electronavigation in confned environments using this U-I sensor. [p. 946]

Having substituted Equations. 1, 3, 5 into Equation. 4, we have the dependence between signals from pickup coils (PCs) of any two limbs and the relevant magnetic gradient occurs: ∇φ, (Laplace equation) [p. 71]

IV. B. Model of Perception As stated in [1], the electric state of the scene surrounding the sensor can be completely parameterized by the electric potential φ solution at each time of the Laplace equations Δφ = 0 with boundaries conditions imposed on the sensor and the objects. [p. 947]

The signal-wave envelope is a result of the informational-measuring process In this case, the active dipole feld of biological experiments should be and confirming about the space distribution of the MF variations, which are replaced by the secondary dipoles re-emitted (or equivalently “refected”) by the objects once polarized by the sensor. This idea plays a key role in the bases for the advanced perception-guide abilities. [p. 72]

interactions, as well as taking inspiration from passive electrolocation in real fsh.

proposed reactive control approach. [p. 949]

A PC realizing the oscillatory−forward movement along both AC industrial interferences and quasi-DC natural (Earth) environmental MFs. The movement in quasi-DC MF H DC with the defined speed v and oscillating frequency ω with a magnitude Δα gives e.m.f. from PC: Eq. [p. 69]

As a result, this operation allows the sensor to measure the presence of the objects in its surroundings as if they were active unsteady secondary (opposed to the primary dipole generated by the active sensor put under voltage) dipoles. {p. 950}

Fig. 13 3D Orientation of the walking robot in an environmental MF [p. 71]

Fig. 10. Sensor tracks the electric feld lines and crosses the isopotential surfaces of increasing values. [p. 951]

The finite distance between the magnetic sensors for detecting the field In this perspective, the control law (8) can be interpreted as an implementation of the so-called gradient descent algorithm along a relief difference is used to get an expression for the estimate of the exact magnetic gradient, adjusted by the function of the field distance: Eq.[p. 71] whose dependence on the local conductivity of the scene is fxed by the

choice of the gain K. [p. 952]

There are developing opportunities for combining natural and artificial This sensor is inspired by a natural mode of perception named electric sense. In fsh, electric sense is based on the measurement of the distortions by the sensing abilities in the synthesized system. [p. 75] Multiprocessor data fusion is in effect intrinsically performed by animals and surrounding of a self-generated electric feld. [p. 953] human beings to achieve amore accurate assessment of the processing environment. [p. 74]

The distribution of permanent and the value of periodical MFs give the data about placement of ferromagnetic objects and sources of EM radiation respectively. On the other hand, these signals will be a reference point and guiding line for a walking robot. [p. 65]

Starting from a bioinspired approach, we adapted the navigation strategy of fsh, which passively follows the electric lines of active dipoles to seek their prey, to the case of active electrolocation. [p. 954]

Nov. 7, 2013 Plagiarized paper DOI 10.1109/TRO.2013.2255451

Dear President and CEO Peter Staecker, The method of EM orientation in different environments which is further developed in IEEE Transactions on Robotics

(vol. 29, no. 4, 2013) with a title of the article

'Underwater Refex Navigation in Confned Environment Based on Electric Sense' by Frederic Boyer, Vincent Lebastard, Christine Chevallereau, and Noel Servagent had been formulated ten years ago and presented at CLAWAR 2004 Conf (7th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines) and accepted for an oral presentation (Session 3, SENSORS - see attached Final Programme), and publication. Further, it was accepted for The 3rd International Conference

Bioinformatics,

(BIOTECHNO' 2011,

Biocomputational

Systems

and

Biotechnologies

22-27 May 2011 - Venice, Italy) as a paper titled Machine

Perception Through Natural Intelligence. It was also submitter to IEEE Sensors J. as a paper titled Machine Olfaction Through Natural Intelligence. This method was finally published in my paper titled Machine Perception Through Natural Intelligence, Positioning, 2011, 2, pp. 65-77. In both papers the method of orientation in an ambient electromagnetic field was described. My paper is focusing on a magnetic partial of

the field. In a central point of their investigation is an electrical partial. Enclosed please find the table of comparison for an evident understanding. Unfortunately neither Editor-in-Chief S. Hutchinson, nor IEEE RAS VicePresident for Publication Activities Alessandro De Luca do not considered this case properly. That is why I have contacted the IEEE Ombudsman's Office with all the said evidences on October 3. But I am still have not any reply from one of their Member Relations Administrators, as promised.

Yours sincerely,

Rostyslav SKLYAR, Dr. (Eng)

From: "Rostyslav SKLYAR, Dr. (Eng)" <sklyar@tsp.lviv.ua> Date: September 4, 2013 7:45:34 PM GMT+03:00 To: seth@illinois.edu Subject: plagiarized paper DOI 10.1109/TRO.2013.2255451 [2] Continued message. But, before I proceed with this, I'd like to ask you for some clarification. In particular, are you accusing these authors of having read your previous work, and then republishing it as if it were their own? If so, this is a very serious charge, so please consider carefully if this is your intention. On the other hand, perhaps you are merely accusing the authors of having failed to know about your research (i.e., perhaps they have, on their own, come up with the ideas in their paper). In this case, you would be accusing them of a kind of negligence, in having failed to adequately review the existing literature. From: "Rostyslav SKLYAR, Dr. (Eng)" <sklyar@tsp.lviv.ua> Date: September 5, 2013 3:22:39 PM GMT+03:00 To: "S. Hutchinson" <ieee-tro@illinois.edu> Subject: Re: plagiarized paper DOI 10.1109/TRO.2013.2255451 [1] Hello Seth, Thank you for your prompt reply! Yes, I am seriously accusing these authors of having read my previous work(s), and then republishing it as if it were their own. Because, as you may noticed/concluded from my letter, this method had been disseminating by myself worldwide during a decade. Moreover, the last paper was published in a peer-reviewed open access journal. Of course, I am appreciating your requirement to find out as more as possible of the necessary evidences. Apart from the fact that I have published in an open access journal, the most specific and solid evidence to support my charges is the said fact that it was presented at CLAWAR, which is the leading robotics conference in Europe, where these authors are living and working. Also some possible "wikileaks" from reviewing processes during such a long time in the different editorials should be taken into account. Please don't worry, you'll need not to go through all of the papers looking for evidence of plagiarism. These explicit, in seems to me, evidences I have highlighted for you and explained in Table. From: "S. Hutchinson" <ieee-tro@illinois.edu> Date: October 1, 2013 1:53:49 PM GMT+03:00 To: "Rostyslav SKLYAR, Dr. (Eng)" <sklyar@tsp.lviv.ua>, "S. Hutchinson" <ieeetro@illinois.edu> Subject: Re: plagiarized paper DOI 10.1109/TRO.2013.2255451 [1] I have spent some time today looking at your previous e-mails, and evaluating your accusation of plagiarism. I have looked carefully at the table that you sent, and examined several of the papers you have previously published. Honestly, I see no evidence here of plagiarism. Furthermore, what you claim as solid evidence (that your paper was presented at CLAWAR) is not at all compelling. CLAWAR is a conference that is known, but it is certainly not the leading robotics conference in Europe, and the quality of papers there is not uniformly recognized as outstanding. Furthermore, your CLAWAR paper was published nearly ten years ago. I find it to be not credible that the accused authors went back nearly a decade into the CLAWAR proceedings, found your work, and decided to steal from it. Other publications of yours that you have cited were not published in venues that are well known (or well read) by the robotics community. As for your completely unsubstantiated insinuation that there are leaks in the review process, I will tell you very directly, making such accusations without evidence is unacceptable, and as an

Editor-in-Chief, I take personal offense at the remarks, on behalf of the large research community that puts in much time and effort performing reviewing work so that peer review will remain an important aspect of our research paradigm. I have simply disregarded these remarks, as they are, in the absence of evidence, lacking in any merit. It is certainly possible that I may have missed some relevant piece of evidence. But, I asked you multiple times to provide for me a concise and specific set of evidence to support your claims, and you were not willing to do so. For my part, I have invested in this matter all of the time that I can spare for it. I suspect that this is not the outcome you had desired. In that case, you have a number of options that are available to you. You may pursue the matter with the incoming Editor-in-Chief of the Transactions on Robotics (Professor Frank Park); you may pursue the matter with the Vice President for Publications of the Robotics and Automation Society; or you may choose to take the matter directly to IEEE. In any of those cases, if I am asked for my opinion, I will comment that I have found your accusations to be lacking substantive and credible evidence. I do wish you the best in your research, and I'm sorry that this is not what you had hoped to hear, seth Hello Seth, I am also forced to disregard your remarks, in seems to me they are truly laughful and malicious. Moreover, they are exactly "completely unsubstantiated" - without ANY reference to the table (of course, you still insisting that the cited paper does not "well known" and should be neglected). However, I am grateful for "a number of options". Also please don't worry, I am having deals with the multiple of plagiarizing cases. That is why I am hearing even worse slanders! From: Alessandro De Luca <deluca@dis.uniroma1.it> Date: October 3, 2013 4:04:28 PM GMT+03:00 To: "Rostyslav SKLYAR, Dr. (Eng)" <sklyar@tsp.lviv.ua> Subject: Re: plagiarized paper DOI 10.1109/TRO.2013.2255451 [1] Dear Dr. Sklyar Thanks for contacting me about the issue of a potential plagiarism made by the authors of a paper that was recently published in our IEEE Transactions on Robotics (T-RO). As you may know, IEEE and the Robotics and Automation Society (RAS) consider very seriously the issue of unethical behavior in publications, such as plagiarism, parallel simultaneous submissions, and self-plagiarism. Over the years, IEEE (and RAS) has developed considerable experience on plagiarism cases. Attached please find a description of the existing five levels of plagiarism and the associated corrective actions, as taken from the IEEE PSPB Operations Manual. I have looked carefully at the T-RO paper and at the three papers authored by yourself (two in conference proceedings ---Clawar 2004 and Biotechno 2011, and one in a journal ---Positioning 2011) that you sent to me, as attachments to the mail below and to a single following one. Also, I have read the table that you have prepared with a detailed comparison of fragments of texts. My conclusion is that there is no evidence of plagiarism at any level. If you find that the authors of the T-RO paper should have cited your papers as relevant to their work, I would encourage you to contact them directly and discuss in a professional, collaborative way any analogy and/or difference. Alessandro De Luca IEEE RAS Vice-President for Publication Activities (2012-13) Dipartimento di Ingegneria informatica, automatica e gestionale Antonio Ruberti -- DIAG

Sapienza Universita' di Roma Via Ariosto 25 00185 Roma, Italy tel: +39 06 77274052 fax: +39 06 77274033 email: deluca@diag.uniroma1.it url: http://www.diag.uniroma1.it/~deluca On Oct 2, 2013, at 9:59 PM, Rostyslav SKLYAR, Dr. (Eng) wrote: Dear Vice-President De Luca, From: ombudsman@ieee.org (Your inquiry has been received) Date: October 3, 2013 5:31:28 PM GMT+03:00 Subject: Re: plagiarized paper DOI 10.1109/TRO.2013.2255451 [1] Thank you for contacting the IEEE Ombudsman's Office. Your inquiry is very important to us and you will be contacted by one of our Member Relations Administrators. Fax: +1 732 981 9515 IEEE Corporate Office (NY) +1 212 752 4929President and CEO: Dr. Peter W. Staecker [www.ieee.org/about/corporate/board_board.html] [www.ieee.org/about/contact_center/index.html?WT.mc_id=hpf_contact] Copyrights and Permissions 1 732 562 1746 From: Anthony VenGraitis <a.vengraitis@ieee.org> Date: December 5, 2013 6:34:04 PM GMT+02:00 To: sklyar@tsp.lviv.ua Subject: Fwd: plagiarized paper DOI 10.1109/TRO.2013.2255451 Dr. Skylar, I am writing to let you know that we have received your email and the details of your complaint. We will contact senior volunteers in the IEEE Robotics and Automation Society, who will be responsible for reviewing the matter and recommending the appropriate actions. We hope you will feel free to let us know if you should have any questions and/or comments along the way. Best regards Tony -------- Original Message -------Subject: plagiarized paper DOI 10.1109/TRO.2013.2255451 Date: Wed, 4 Dec 2013 21:27:11 +0200 From: Rostyslav SKLYAR, Dr. (Eng) <sklyar@tsp.lviv.ua> To: gsetti@ing.unife.it Dear Director & Vice President Prof. J. Roberto de Marca, Having submitted the relevant material about plagiarizing in IEEE Transactions on Robotics, I still have not any reply during two months from the IEEE Ombudsman's Office, as promised. ----------------------------------------------------------------Gianluca Setti, PhD, FIEEE ENDIF University of Ferrara Via Saragat 1, 44100

Phone: (+39)-0532-974997 Fax : (+39)-0532-974870 Email: gianluca.setti@unife.it

Ferrara, Italy ARCES University of Bologna Via Toffano 2/2, 40125 Bologna, Italy

Phone: (+39)-051-2095405 Fax : (+39)-051-2095410 Email: gsetti@arces.unibo.it

WWW:http://www-micro.deis.unibo.it/cgi-bin/user?setti "There is nothing more practical than a good theory" - James C. Maxwell "Most people say that it is the intellect which makes a great scientist. They are wrong: it is character" - Albert Einstein ----------------------------------------------------------------Gianluca Setti, PhD, FIEEE ENDIF University of Ferrara Via Saragat 1, 44100 Ferrara, Italy

Phone: (+39)-0532-974997 Fax : (+39)-0532-974870 Email: gianluca.setti@unife.it

ARCES University of Bologna Via Toffano 2/2, 40125 Bologna, Italy

Phone: (+39)-051-2095405 Fax : (+39)-051-2095410 Email: gsetti@arces.unibo.it

WWW:http://www-micro.deis.unibo.it/cgi-bin/user?setti -Anthony VenGraitis, IPR Specialist IEEE Intellectual Property Rights Office 445 Hoes Lane Piscataway, NJ 08854 Phone: +1 732 562 5534 Fax: +1 732 562 1746 Email: a.vengraitis@ieee.org Web: http://www.ieee.org/copyright From: Anthony VenGraitis <a.vengraitis@ieee.org> Date: January 29, 2014 9:25:01 PM GMT+02:00 Subject: Re: Fwd: plagiarized paper DOI 10.1109/TRO.2013.2255451 I will follow up with the PSPB Chair and the Publishing Conduct Committee who are reviewing your complaint. Best regards Tony Date: December 11, 2014 3:03:47 PM GMT+02:00 To: Maria.Morillo-Gutierrez@ec.europa.eu Subject: plagiarized paper under Contract 231845 [1] Dear Director AMOR,

The paper 'Electric Sensor-Based Control of Underwater Robot Groups' (Ch. Chevallereau, M.R. Benachenhou, V. Lebastard, and F. Boyer, IEEE TRANSACTIONS ON ROBOTICS, Vol. 30, No. 3, 2014, DOI: 10.1109/TRO.2013.2295890) which was supported by the ANGELS project funded by the European Commission, Information Society and Media, Future and Emerging Technologies under Contract 231845 is based on the plagiarized results, as described below. The method of electromagnetic orientation in different environments which is further developed in IEEE Transactions on Robotics (vol. 29, no. 4, 2013) with a title of the article 'Underwater Reflex Navigation in Confined Environment Based on Electric Sense' by Frederic Boyer, Vincent Lebastard, Christine Chevallereau, and Noel Servagent had been formulated ten years ago and presented at CLAWAR 2004 Conf (7th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines) and accepted for an oral presentation (Session 3, SENSORS - see attached Final Programme), and publication (see the letter of acceptance and paper PDF titled "Induction Magnetic Field Sensor as an Organ of Robot Vision" into the proccedings). Further, it was accepted for The 3rd International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies (BIOTECHNO' 2011, 22-27 May 2011 - Venice, Italy) as a paper titled Machine Perception Through Natural Intelligence (see attached). It was also submitter to IEEE Sensors J. as a paper titled 'Machine Olfaction Through Natural Intelligence'. This method was finally published in my paper titled 'Machine Perception Through Natural Intelligence', Positioning, 2011, 2, pp. 65-77 (see attached). In both papers the method of orientation in an ambient electromagnetic field was described. My paper is focusing on a magnetic partial of the field. In a central point of their investigation is an electrical partial. Enclosed please find the table of comparison for an evident understanding with the highlighted pieces in both analyzed papers. From: Anthony VenGraitis <a.vengraitis@ieee.org> Date: July 10, 2015 10:03:16 PM GMT+03:00 Cc: "hall@cse.usf.edu" <hall@cse.usf.edu>, Jon George Rokne <rokne@ucalgary.ca>, c.siller@ieee.org, Gianluca Setti <gianluca.setti@unife.it> Subject: Re: Fwd: plagiarized paper DOI 10.1109/TRO.2013.2255451 A decision was reached on the appeal that you sent to the PSPB Chair on 4 December 2013 regarding a decision on a plagiarism complaint you raised with material published in the IEEE Transactions on Robotics. Your appeal stated that the committee appointed by publication officers of the IEEE Robotics and Automation Society failed to consider the case properly. The PSPB Chair consulted with the IEEE Publishing Conduct Committee (PCC) to review the appeal and the associated documents from the plagiarism complaint. After a thorough review of all the evidence, the PCC determined that there is basically no connection between the two articles, other than the fact that both deal with methods for locating and positioning objects based on sensing different aspects of the electromagnetic field around the object. According to the PCC's report, the Boyer et al. article does not plagiarize the Sklyar article in any sense of the term. No verbatim use of words from one article to the other was found, and no use of ideas from one article in the other was found, with or without attribution. As such, the PCC confirmed that the case was handled appropriately, and the decision reached by the publication officers of the IEEE Robotics and Automation Society in September 2013 was correct, and no further action is needed from IEEE. The PCC's decision was final and not open for further appeal.

Anthony VenGraitis, IPR Specialist,IEEE Intellectual Property Rights Office 445 Hoes Lane, Piscataway, NJ 08854, Fax: +17325621746,.ieee.org/copyright Date: July 26, 2015 9:28:14 PM GMT+03:00 To: Anthony VenGraitis <a.vengraitis@ieee.org> Cc: hall@cse.usf.edu, Jon George Rokne <rokne@ucalgary.ca>, c.siller@ieee.org, "Gianluca Setti" <gianluca.setti@unife.it> Subject: scientific misconduct in doi:10.1109/ETFA.2009.5347181 & doi:10.1109/TIM.2011.2147690 Dear Tony, The method of magnetic orientation which was published as “Magnetic map building for mobile robot localization purpose,” by D. Navarro and G. Benet in Proc. 14th IEEE Int. Conf. Emerging Technol. Factory Autom., Palma de Mallorca, Spain, 2009, pp. 1742–1745, doi:10.1109/ETFA.2009.5347181 (http://ieeexplore.ieee.org/stamp/stamp.jsp? tp=&arnumber=5347181) had been presented ten years ago at CLAWAR Conference (see attached) and finally published in the Positioning journal at www.scirp.org/journal/PaperInformation.aspx?paperID=5051#abstract. Consequently, the paper "Magnetic Maps for Indoor Navigation" by Brandon Gozick, Kalyan Pathapati Subbu, Ram Dantu, and Tomyo Maeshiro in IEEE Trans. Instrum. Meas. 2011, 60, 3883–3891, doi:10.1109/TIM.2011.2147690 has cited this paper as Ref. [2] illegally. Date: July 27, 2015 6:02:26 PM GMT+03:00 I'm acknowledging receipt of your message and attachment. We will forward to the appropriate IEEE publication officers for their review. Date: July 26, 2015 7:16:01 PM GMT+03:00 To: vittorio.passaro@poliba.it, reindl@imtek.de, melessea@fiu.edu, Rudi.Seitz@unh.edu, astar@pitt.edu Cc: editor.ijes@esgpublisher.com, dekan@tf.bor.ac.rs, mantonijevic@tf.bor.ac.rs, schoening@fh-aachen.de, peter.seitz@unine.ch, younis@cs.umbc.edu, tim.wark@csiro.au Subject: scientific misconduct in doi:10.3390/s150717168 Dear Editor-in-Chief Dr. Passaro, The method of indoor orientation according to the "magnetic field anomalies inside buildings" by "magnetic field measurements" which was published in Sensors 2015, 15(7), 17168-17194; doi:10.3390/s150717168, http://www.mdpi.com/1424-8220/15/7/17168 had been presented ten years ago at CLAWAR Conference (see attached) and finally published in the Positioning journal at www.scirp.org/journal/PaperInformation.aspx?paperID=5051#abstract. From: Martyn Rittman <rittman@mdpi.com> Date: October 20, 2015 11:05:03 AM Cc: vittorio.passaro@poliba.it, reindl@imtek.de, melessea@fiu.edu, Rudi.Seitz@unh.edu, astar@pitt.edu, Sensors Editorial Office <sensors@mdpi.com>, "'Franck Vazquez'" <vazquez@mdpi.com>, "lin@mdpi.com" <lin@mdpi.com> Your message about this issue has been forwarded to me. Thank you for raising your concerns. We have carried out a thorough investigation involving several experts in the field. Our opinion is that while there are similarities in the method, the application is different and the method used is not identical to that from the paper 10 years, thus it does not constitute plagiarism as you have suggested. We have decided that no further action is necessary in this case. Martyn Rittman, Ph.D., Chief Production Editor, MDPI AG Postfach, CH-4005 Basel, Switzerland, Fax +41 61 302 89 18 Office: Klybeckstrasse 64, CH-4057 Basel, Switzerland Date: July 26, 2015 9:21:26 PM GMT+03:00

'Underwater Reflex Navigation in Confined Environment Based on CRIBBING Sense'