Assembler Assemble by Nguyen Xuan Man

SSEMBLER S S E MB L E

Research Cluster 4, 2017-2018 M.Arch Architectural Design //M4G

RESEARCH CLUSTER 4, GILLES RETSIN, MANUEL JIMENEZ GARCIA, VICENTE SOLER M4G: Mengyu Huang, Dafni Katrakalidi, Martha Masli, Man Nguyen, Wenji Zhang

UCL, The Bartlett School of Architecture

CONTENTS 01 INTRODUCTION Thesis Statement Precedents Project Overview

02 ASSEMBLER ASSEMBLE Experimentation Robot Basic Movement Robot - Tile Development Prototypes Mechanism Building Strategy

03 COMPUTATIONAL AGGREGATION Assembling Logic Computational Logic Machine Learning Structure Agregation Strategy Structural Optimisation Robot Computational System BIM Application

04 FABRICATION Materials Production Prototypes Scalability

05 ARCHITECTURAL SCALE Architectural Speculation Design Proposal Prototypes

B-PRO SHOW 2018 Prototypes Assembling Sequence

REFLECTIONS APPENDIX Workshop 01 - Game Workshop 02 - Robot Workshop 03 - Algorithm

INTRODUCTION

THESIS STATEMENT INTRODUCTION

The objective of the design thesis is to question an autonomous process in architecture, specifically within the domestic context of building houses in the digital postwork era. The project itself is not only focusing on the design part, but more towards exploration into a system of production, which incorporate automation in construction process by modularise both of the construction product and the robot. By designing an identical geometric syntax between robotic process (active pieces) and building components (passive pieces), Assembler Assemble is able to transform an end-effector into an active component of the design process. Based on the research on Digital Material and Robotic Assembly, the project is also aimed to develop a computational system which ultilize the recent development in Machine Learning, to increase the robots ability to adapt to irregullar environments and to construct the house in a fully autonomous manner.

ASSEMBLE

ROBOT

TILE

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ASSEMBLER

PRECEDENTS INTRODUCTION

MODULARITY //

AUTOMATION //

ASSEMBLY //

To begin the research project, a series of precedent studies was conducted. Started with the idea of Industrial Mass Standardization that was introduced by Dom-ino House and Universal House by Phillip Morel that can be implemented to the project by designing a modular components between both passive and active elements. Followed by the in depth observation on the Programmable Matter by Kenny Cheung, and to implementat a programmable and computational material that later will introduce a potential application for a self- guided assembly. And lastly, for the production and distribution process, a research was conducted based on Wiki House project to expand the idea of open source and DIY in todays architectural and construction field.

The automation studies was begin by observing a Discrete Cellular Lattice Assembly project by MIT. The project was inspired by the possibility of an assembly system where the design of a modular robotic acted as an assembler with a specified lattice topology, such that the lattice can itself be removed from the incremental assembly process. Followed by the research that was conducted by Wyss Institute, Termes project, a series of inputs can be implemented in the project. As demonstrated in Termes, a simple robot can perform a collective task such as transporting large objects and autonomous building human-scale structure. However, to achieve the project aim, a simpler robotic behavioural system is more prefferable, hence the in depth research on MIT [m]MTM by Nadya Peek was conducted in our project. [m]MTM inspired the project by using an easily modifable modular components that can be applied into our design project. And lastly, M-Blocks by MIT was inspired the project by their self reconfigurable robotics process which later on can be developed into the project.

To work safely in an irregular environments, robots are usually designed in a extremely complex way which limits their effectiveness to perfrom a task. Even in a fully automated factory, neccessary equipments are limited by scale, cost and flexibility, which drives the whole system off from being fully automation. Therefore, a modular self-guided assembly robots can be involved due to its capability to easily adapt to environment, in both ways as a robot, structures and element itself, which break the limitation to create a large-scale model. Furthermore, the application of digital materials can positively be contributed to the the project due to reduction of error. According to Neil Gershenfeld (2013), a snap-fit alignment features mean that a ‘child’s assembly of Lego will be more accurate than the child’s motor skills would allow’(Gershenfeld, 2013). And as for the production and distribution system, the use of flat pack method from Tallinn Pavilion by Gilles Retsin is also relevant to the project, as each of the building block components are universal and can be reversed, recombined and reused for future construction.

Discrete Cellular Lattice Assembly Center fro Bits and Atoms - MIT

Finite Working Zone Industrial Arm Multi-Task Robot

Universal House Digital Voxel Aggregation Phillip Morrel - 2001

Programmable Matter Autonomous Assembly System Kenny Cheung - 2009

Wiki House Web Opensource DIY Alastair Parvin - 2011

Termes Wyss Institute

[m]MTM Center fro Bits and Atoms - MIT

M-Blocks Cube Robots MIT,CSAIL

Infinite Working Zone Automatic Modular Assembly System (AMAS) - MIT

Digital Material Lego Block Godfried Kirk Christiansen (1958)

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Dom-ino House Industrial Mass Standardization Le Corbusier - 1914

Architectural Discrete Material Gilles Retsin - Tallinn Pavilion

PROJECT OVERVIEW INTRODUCTION

A relative robot system is preffered to be used in this project due to circumvent external infrastructure constraints that allow infinity working zone with unlimited form and size. Most of robotic construction for the off-site prefabrication are completed with stationary robotic arms, which do not easily lend themselves to construction sites. Therefore, this project is preffered of having a simple and identical geometry between the robot and the tiles may present some benefits. According to Neil Gershenfeld (2015), ‘Relative’ construction robots require a completely different design approach, given that ‘these robots, plus the materials they assemble, are best viewed as a combined system’. (Gershenfeld, et al., 2015).

Finite Working Zone

Construction Product

Robotic Technology

SEPARATED SYSTEM

RELATIVE ROBOT Infinite Working Zone

Construction Product

Robotic Technology

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GLOBAL ROBOT

INTEGRATED SYSTEM

PROJECT OVERVIEW INTRODUCTION

SUSTAINABILITY - SCALABILITY

CENTRALIZED PRODUCTION

CREATIVE COMMON

DIGITAL AGGREGATION

AMATEUR, DESIGNER, BUILDER

With the use of modular and finite number of products, the whole production system can be easily divided and distributed. The number of materials, and other related sources that are needed in the construction become more predictable, makes it easier to form a more efficient workflow system. To allow a more effective colaborative development, the common communication standard between each contributors and other disciplines such as amateur designer and builder teams should be established. Through the use of the system, time, cost, skill, energy and other resources can be optimised. This allows each contributors and disciplines without needing to know everything to contribute and interact with the same data packages which can be easily modified as per needed, and where possible, work to existing standards or even seek to establish intuitive new ones.

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CHAIN OF PRODUCTION

PROJECT OVERVIEW INTRODUCTION

Computational Design Logic and Aggregation plays a major role in the design process of the project. Started with the combinatoric logic of the tile, a digital input was created with a large amount of data which are generated for further aggregation. Followed by various algorithms logic that were applied also for the system of control for the locomotion and assembly sequence for the robot. Furthermore, the aggregated structure was optimized through the use of machine learning process, generating an optimum structure. And finally, BIM system is also developed to allow user to visualize and customize Assembler Assemble new way of construction system, to create a fully automated habitat.

MACHINE LEARNING

ROBOT LOCOMOTION SYSTEM

AUTONOMOUS SYSTEM

FULLY AUTOMATED HABITAT

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STRUCTURAL OPTIMISATION

Assembler Assemble

EXPERIMENTATION

ASSEMBLER ASSEMBLE

ROBOT - TILE

ASSEMBLY

ROBOT - TILE

ASSEMBLY

ROBOT - TILE

ASSEMBLY

AGGREGATION

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AGGREGATION

EXPERIMENTATION

ASSEMBLER ASSEMBLE

GEOMETRY

VOLUME

ROBOT

TILE

ASSEMBLER

ASSEMBLE

Robot

Tile

Based on the exploration of various different geometrical and relatonship between the robot and tiles, simple identical geometric syntax between the robot pieces (active) and building components (passive) resulted the most effective and efficient way to operate.

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Thus, this project will focus on extracting the benefits of using relative robot, following by the observation on how the development and implementation of computational and physical systems in robotic assembly process can be used to demonstrate the ability of an individual robot to assemble tiles as an architectural components? What are the impacts of the local and global scales on the mobility of systems and how these scales can be used to create configurable structures?

ROBOT BASIC MOVEMENT ASSEMBLER ASSEMBLE

BASIC MOVEMENT

ROBOT BASIC MOVEMENT

Robot Tile

Switch Joint

Robot Tile

Switch Joint

BASIC MOVEMENT

Switch Joint

②

①

② ④ ②

① ③ ①

② ④

① ③

COLLABORATIVE ROBOTS COLLABORATIVE ROBOTS

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ROBOT ROBOTSTEPS STEPS

ROBOT BASIC MOVEMENTS ASSEMBLER ASSEMBLE

ROBOT - TILE MOVEMENTS

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ROBOT BUILDING SEQUENCE

ROBOT BASIC MOVEMENTS ASSEMBLER ASSEMBLE

ROBOT - TILE LOOP BUILDING SEQUENCE 28

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COLLABORATIVE ROBOT 29

ROBOT - TILE DEVELOPMENT ASSEMBLER ASSEMBLE

ROBOT

TILE

ROBOT

TILE

ROBOT

TILE

ROBOT

TILE

Option 01 Grid 1x1

Option 02 Grid 3x3

Option 03 Grid 2x2

Option 04 Grid 2x2

GRID

PLAN

ARM

TILE

ROBOT

TILE

ROBOT

TILE

ROBOT

TILE

Option 05 Grid 5x5

Option 06 Grid 5x5

Option 07 Grid 5x5

Option 08 Grid 5x5

2 3

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ROBOT

ROBOT - TILE PROTOTYPES ASSEMBLER ASSEMBLE

TILE

Grid 1x1

Grid 3x3

ROBOT

Grid 1x1

Grid 3x3

TILE

Grid 5x5

ROBOT

Grid 5x5

Grid 5x5 M4G . Assembler Asemble

TILE

MECHANISM - ROBOT RESEARCH ASSEMBLER ASSEMBLE

ROBOT COMPONENT

GRID SYSTEM

ROBOT DETAILS

2 3

Mechanism

4 5

Main Gear to open robot arm (Up and Bottom) Slider Bar to attach Robot to Tile (Vertical and Horizontal)

5x5 Grid System

Robot Arm to pick up the tiles

Interlocking

Robot Top View

Robot - Tile Connector to slide, then grip the tile

Robot Arm to slide the connector

Robot Arm Activation Zone

MECHANISM

INTERLOCKING

ROBOT

MAIN FUNCTION: MOTOR 360 ROTATION SWITCH JOINT

TILE

OPTION 01. Clipper ROBOT TILE

ROBOT

OPTION 01

1x Central Motor 1x Attached Gear

OPTION 02. Slider

TILE

ROBOT TILE

OPTION 03. Push OPTION 02

1 x Central Motor 4 x Attached Gears ROBOT TILE

OPTION 04. Gripper 1 x Central Motor 4 x Attached Gears

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OPTION 03

MECHANISM - TILE RESEARCH ASSEMBLER ASSEMBLE

TILE COMPONENT

GRID SYSTEM

TILE DETAILS

2 3

Mechanism

4 5

5x5 Grid System

Interlocking

Tile Top View

Tile Activation Zone

Slider Bar to lock tiles Horizontal and Vertical

JOINT ALTERNATIVES

MECHANISM

ROBOT

INTERLOCKING

TILE

ROBOT TILE

OPTION 01. Clipper

ROBOT ROBOT

TILE

TILE ROBOT

TILE

OPTION 02. Slider

ROBOT ROBOT

TILE

OPTION 03. Push

ROBOT TILE

TILE M4G . Assembler Asemble

ROBOT

OPTION 04. Gripper

MECHANISM - ROBOT PROPOSAL ASSEMBLER ASSEMBLE

ROBOT COMPONENT

GRID SYSTEM 1

MECHANISM 5

2 3 4 5

Mechanism

Down

5x5 Grid System 1

Motors for Main Movement 4

2 3 4 5

Robot Top View

Microcontroller Interlocking

Robot End -Effector

Support Frame

Main Motors

Arm Motors

END - EFFECTOR

ROBOT - TILE INTERLOCKING

Motor

Activate Central Gear

Lock Tile

Activate Central Gear

Motor

Trigger Linear Gear Activate Activate Linear Gear Central Gear Linear Gear

Pick Up Tile

Motor

Screw in/ Out Linear Gear Central Gear

Linear Gear

Switch Joint

Motor

Screw Lock Tile

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Robot - Tile Interlocking Place Tile

MECHANISM - ROBOT PROPOSAL ASSEMBLER ASSEMBLE

ROBOT COMPONENT

GRID SYSTEM 1

TILE DETAILS 5

Tracking Slot

3 4

Robot - Tile Interlocking System

5x5 Grid System 1

2 3 4 5

Tile Top View

Tile Slot

Chamfered Edge Robot Arm and Tracking Slot Interlocking System

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ROBOT PROTOTYPE ASSEMBLER ASSEMBLE

Microcontroller

Support Frame

Robot Handles

42 42

Robot Arms

Motors

Robot Prototype

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1mm Alumunium Box 3D Printed Handles 3mm Alumunium Frame 4x Servo Motors 9V Battery Wires/ Cables Microcontroller

43 43

44 44

ROBOT PROTOTYPE ASSEMBLER ASSEMBLE

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ROBOT - TILE PROTOTYPES ASSEMBLER ASSEMBLE

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ROBOT PROTOTYPE ASSEMBLER ASSEMBLE

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LOOP STRUCTURE PROTOTYPE

MECHANISM - ROBOT PROPOSAL ASSEMBLER ASSEMBLE

Acrylic Robot

Cardboard Robot

Alumunium Robot

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Orange Cardboard Robot

MECHANISM - ROBOT PROPOSAL ASSEMBLER ASSEMBLE

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BUILDING STRATEGY ASSEMBLER ASSEMBLE

Pre - Calculation Steps Lorem ipsum dolor sit amet, consectetuer adipiscing

Pre Calculation Steps elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi

Initially,enim a pre-calculation system will be exerci embeded ad minim veniam, quis nostrud tation into theullamcorper robotic system, to lobortis count number of steps, suscipit nisl ut aliquip ex ea commodo consequat. Duis autem vel eum iriure dolor and most efficient direction of movement to reach the target location.

Robot Origin Position

Robot Target Position

Robot Basic Movement

Single Robot

Start Target

1 2

2 3

1 2 3

2 3

1 2

3 4

4 5

5 6

6 7

Robot 01 Origin Position

Robots Target Position

Robot 02 Origin Position

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Collaborative Robots

BUILDING STRATEGY ASSEMBLER ASSEMBLE

Stepping Strategy

Climbing Strategy

Collaborative Strategy

4 3

Looping Assembly

Looping Strategy

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Mulltiplication Looping Stepping Strategy

BUILDING SEQUENCE ASSEMBLER ASSEMBLE

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COMPUTATIONAL AGGREGATION

ASSEMBLING LOGIC - RESEARCH COMPUTATIONAL LOGIC

ASSEMBLING LOGIC

Based on 5x5 Grid System, the tile are lined with 25 grids on top and bottom, followed by 5 grids on each sides. All grids were numbered in order from No.1 to No.70 (as shown on the diagram). For each grids, holes are provided and numbered at the centre of each squares. For the interlocking system, there are two basic rules that will be introduced. One is overlapping between two horizontal tiles, and the other one is a vertical tile stamping on top of a horizontal tile, vice versa. Based on numbers that were previously given and digitally calculated, all possibilities of connection will be analyzed. The diagram on the right shows the examples of the connection of hole No.25 and No.18, and hole No.55 and No.14.

COMBINATORIAL

JOINTS

LOOP AGGREGATION LOGIC By analysing the basic tile aggregation, the combination will be based on instance of three tiles that can produce a huge amount of possibilities that will be used for future design proposal. In some cases. when the robot need to build the tile from one position to another position, climbing up procedure will be needed in order to put the next tile on a higher position.

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Once the basic aggregation has been clarified, the next step would be creating the loop to make it modular. by adding the third one and the fourth one. The diagram above shows the examples of a combination between three horizontal tiles, which later will create a looping system. In order to give possibilities for different types of final design form, one three-combination or one loop will be considered as one object.

ASSEMBLING LOGIC - RESEARCH COMPUTATIONAL LOGIC

Build the shortest path

By analysing the basic tile aggregation, the combination will be based on instance of three tiles that can produce a huge amount of possibilities that will be used for future design proposal. In some cases. when the robot need to build the tile from one position to another position, climbing up procedure will be needed in order to put the next tile on a higher position.

Remove redundant tiles

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ASSEMBLING LOGIC - RESEARCH COMPUTATIONAL LOGIC

Loop Structure

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ASSEMBLING LOGIC - RESEARCH COMPUTATIONAL LOGIC

The diagram shows the classified colors that are generated due to given conditions on testing how stronger the connections between two tiles are. When two tiles are joining together, the joints will be inserted through the double layer of the tiles. The length of the joints will be varied according to the combination.

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COMPUTATIONAL LOGIC -RESEARCH COMPUTATIONAL AGGREGATION

The aggregation sequence of a small shelter was computed using a simple pathfinding algorithms such as A*, which allows the robot to find the shortest route from a picking tile position to the placing position. As an example, 6 robots were used to built up a small box structure form by using 150 tiles, with the use of most simple flipping movements. Additionally, for the upper part of the wall and the roof, two robots were required to work in collaboration in order to climb up and placing the tile. First attempts of reinforcing the structure are depicted on these pages from our first experiments that were made before the advance of the robot possible movements.

The box shelter, as may seen as a small structure, still require a structure reinforcement and error migration during the assembly process. Overlapping tile were added on as the second layer of the wall and roof. 1000 iterations of the shelter were generated, with mutated structures displayed notable gradients of structural capability, opening and habitability. The aggregated structures were categorized by utilizing the un-supervised learning techniques for clustering the different iterations. Through the use of t-SNE with 5 different ways of describing the elements including: Total Area, Edge Angle, Bottom Surface Area, Bounding Box Volume, Distance Between Central, Centre of Gravity and Mass, the structure was elaborated and optimized. Those descriptors together could create 7 dimensions data which provide complexity in terms of dimensionality reduction technique to provide feature extractions of the provided data.

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ROBOT AGGREGATION SEQUENCE TO BUILD SMALL SCALE STRUCTURE, MADE BY UNITY

COMPUTATIONAL LOGIC -RESEARCH COMPUTATIONAL AGGREGATION

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ASSEMBLING LOGIC - PROPOSAL COMPUTATIONAL LOGIC

The Combinational Logic derived from the Movement and Assembly sequence of the Robot. Each tile has six positions that are attachable to adjacent ones. For computer processing of the connecting points, we number them from 0 to 5. Hence, there are two main types of connection : Straight and Corner,which are the result of the Straight Movement and Turning movement of robot, and all of the possible connection between two tiles has been enumerated.

Basic Assembling Logic

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Assembling Combinations

ASSEMBLING LOGIC - PROPOSAL COMPUTATIONAL AGGREGATION

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Patterns and Loop Assembling

ASSEMBLING LOGIC - EXPERIMENTATION COMPUTATIONAL AGGREGATION

Aggregation Experimentation

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COMPUTATIONAL LOGIC -RESEARCH COMPUTATIONAL AGGREGATION

Points of Activities

ISO Surface

Aggregation Phase 01

Aggregation Phase 02

Aggregation Phase 03

Main Strucrure

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STRUCTURAL OPTIMISATION COMPUTATIONAL AGGREGATION

Topological Optimisation Iterations

Topological Optimisation Analysis

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COMPUTATIONAL LOGIC -RESEARCH COMPUTATIONAL AGGREGATION

By using the supervised learning technique, a multi layers neural network was developed to train the robot as an agent to pick a tile and place it into the predefined location, then go back to the original picking location to restart the loop. The simple input-output for the task are the picking and placing position. However, due to the complexity movement of the robot, it is difficult to figure out the needed steps for the robot to build a structure. Hence, by build-up the dataset through simple movement and manual-control action, and through the use of both classification and regression as a learning task can gradually train the robot to figure the correct movement that leads them towards the desired location. The agent learns to behave in an environment depending on the rewards and penalises on every generation of training. Therefore, the algorithms are leaning toward a reinforcement learning model.

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Part to Whole Relationship - Neural Network

Robot Locomotion - Neural Network

MACHINE LEARNING EXPERIMENTATION COMPUTATIONAL AGGREGATION

The aim of the machine learning application for the robot in this design research is to use reinforcement learning to train the robot as the agent to pick a tile and place it in a predefined location on the structure, then go back to the picking point and restart the loop. The simple input-output for the task are the picking and placing position, but due to the complexity movement of the robot, it is difficult to figure out all the step of the robot in the process to build the structure. Using Unity ML-Agents beta 2.0 asset, we trained the robot through Tensorflow framework to complete several tasks in an interactive environment. Unity ML-Agents uses a reinforcement learning technique called Proximal Policy Optimization (PPO). PPO is a “neural network to approximate the ideal function that maps an agent’s observations to the best action an agent can take in a given state” (Unity, 2018). The agent learns to behave in environment depending on the rewards and penalties on every steps of training. The information that need to feed to the algorithm including states, actions and rewards for each single generation of training.

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MACHINE LEARNING APPLICATION COMPUTATIONAL AGGREGATION

The next experiment was to train the robot on uneven environment. Using the same training data, 6 agents were already able to climb over a sloppy landscape. By adding the local state of the landscape and after 30000 more steps of training, all the robot are able to adjust torque locally to flipped over bumpy landscape to reach the target. This training give the robot ability to not just only working on a controlled factory, but also on unexpected built environment. On the practical application, machine learning algorithm is suitable for the robot to build the foundation on irregular landscape, create an even base slab, then typical grid-based algorithm are used to build the main structure. The physical hardware prototype of robot would need an digital gyroscope , or an Internal Measurement Units to report position and rotation state of the robot, feeding the real-time machine learning algorithms could be apply to re-correct the robot when it goes off grid.

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MACHINE LEARNING EXPERIMENTATION COMPUTATIONAL AGGREGATION

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MACHINE LEARNING ON-SITE APPLICATION COMPUTATIONAL AGGREGATION

To address the problem of uneven terrain, machine learning is used to train the robot to be fully adaptable in unfamiliar and irregularities build environment, so that they will be able to build in uneven landscape. This diagram illustrates the process of how the robot can build the foundation on uneven landscape, automatically by adjusting the base height by using machine learning algorithm, followed by building the structure in typical grid-based manner.

Uneven Landscape Scenario

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STRUCTURE AGGREGATION STRATEGY COMPUTATIONAL AGGREGATION

Using counterbalancing strategy, the robot could be able to build up the structure without the need for scaffolding, with attention to local force distribution through every sequence of aggregation.

Counter Balance Method

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ROBOT COMPUTATIONAL SYSTEM - FORCE AWARENESS COMPUTATIONAL AGGREGATION

When the robot detect a broken point in the structure, it can send signal to call other robot to help fixing the structure. Physical hardware prototype will integrate an Force Resistance Sensor (FRS) to the end-effector to report about the local force and torque. Difference iteration of this process will served as dataset for machine learning algorithm in future development. Through training with real-time sensory data, the robotâ&#x20AC;&#x2122;s capability to tackle with unexpected environment will increase overtime, giving it more freedom and autonomy in local decision making while building the structure.

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STRUCTURE AGGREGATION STRATEGY COMPUTATIONAL AGGREGATION

The grow process of the house start from the User defined location of the basic function inside the house : live, eat, cook, sleep, work, study, bath and gym. Then, mass and void of the house were generated based on basic spatial dimension for each of the space and its relation with others. Then the whole structure was voxelized follow a topological optimization, generated a stable structure which take the counterbalancing grow and the guidance. Then, the path planning algorithm are executed, to find the path for each robot to build the house, with the start picking point unpacked from a container to the end placing point on the structure.

AUTONOMOUS SYSTEM

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STRUCTURAL OPTIMISATION COMPUTATIONAL AGGREGATION

The building sequence is incorporate with structural analysis, giving the user interactive decision on the grow direction of the structure. The aggregation and assembly process are also based on both structure simulation and force-sensing system on the relative robot. Using Unity and NDVIA PhysX 3.3 engine, a real-time structure analysis was constantly running in the process of building a simple column â&#x20AC;&#x201C; beam structure. The grow direction was govern by the simulation of structural behaviour.

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The BIM application that we develop is part of a new type of construction platform, which not just create a common syntax between generative design and robotic assembly, but also able to utilized artificial computational intuition to suggest optimal solution for designer. The new typology of architecture created by this process are the representation for a new form of intelligent in design process, bridging the gap between â&#x20AC;&#x153;alienate architectureâ&#x20AC;? create by computer and a domestic habitable environment for human . The complexity of the design require the creation process of future building as a collaboration between human and machine, together create a sustainable production chain on the turn of The Fourth Industrial Revolution.

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BIM VERSION 01 - USER APPLICATION COMPUTATIONAL AGGREGATION

Dwelling User - Application

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ROBOT COMPUTATIONAL SYSTEM - PATH FINDING COMPUTATIONAL AGGREGATION

CLIMABLE SURFACE

Grow Voxel follow shortest path from origin voxel check all the faces of voxel If voxels have a share face = > boundary faces != share face Climable face = boundary surfaces + ground surface which haven not been built

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TRAFIC LIGHT SYSTEM

Generate shortest path from start point to end point On the path, checking the next point if it is ocuppied by robot or tile If point is occupied, wait for the other robot to pass. Continue walking.

From end point, search for closest start point available on the ground Find shortest path from first end point to one start point Raise the cost of all the edge on the occupied path Find shortest path from sencond end point to second start point Minimize all cross junction

TILE COLISION CHECK

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ALTERNATIVE ROUTES

On the path, checking the side face of the next target point If sideface collide with the tile, change tile direction to the other side

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ROBOT COMPUTATIONAL SYSTEM - PATH FINDING COMPUTATIONAL AGGREGATION

The path planning algorithm was based on several predefined library of algorithm like Cellular Automata, Shortest path. Each time a voxel was generated, the climbable surface for the robot was recalculated. A Finite Element Analysis was run occasionally to allocate the grow direction according to the structural behaviour. Then the path-finding algorithm generate the struct path for multiple robots collaboratively build the structure, and locally decided if the local point is adequately stable to deposit the tile or not. A higher cost was tagged for the path that was used by one robot, so the other robot will priority to find an alternative route to increase the system efficiency. Where two route crossing each other, a â&#x20AC;&#x153;traffic lightâ&#x20AC;? logic is added to avoid collision. The pattern of the joint are actually the trace of the path that robot use to build the structure.

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ROBOT COMPUTATIONAL SYSTEM - PATH FINDING COMPUTATIONAL AGGREGATION

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ROBOT COMPUTATIONAL SYSTEM - PATH FINDING COMPUTATIONAL AGGREGATION

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ROBOT LOCOMATION SYSTEM - PATH FINDING COMPUTATIONAL AGGREGATION

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BIM - USER APPLICATION

COMPUTATIONAL AGGREGATION

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BIM VERSION 02 - USER APPLICATION COMPUTATIONAL AGGREGATION

Dwelling User - Application

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BIM VERSION 02 - USER APPLICATION COMPUTATIONAL AGGREGATION

Dwelling User - Application

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BIM VERSION 02 - USER APPLICATION COMPUTATIONAL AGGREGATION

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FABRICATION

MATERIALS - RESEARCH FABRICATION

There are three main factors that will be considered for selecting the materials, including the weight, quality and cost. Lighter materials become the main preferences for the robotic logistic reason. The materials need to be light for the robot to lift and delivered them to the desired location. As the aim of the design proposal is to build a low cost houses and shelters, the price of the materials will be one of the most considerable factors. The main materials that are chosen to the project are OSB Board (as main tiles), Glass Box and Aluminium Box.

Timber

Cardboard

Alumunium

OSB

Laser Cut

Water Jet

CNC

SIP (Structure Insulated Panel) will be applied to tiles design. Two panels will be placed on the two sides, followed by foam in the middle, which help to provide an insulation system. Currently, there are three materials that will be considered for the tile materials. The first one is the ight weight honeycomb paper board, which will provide the thickness that can protect the foam inside. The second one is OSB, that is well-acknowledged for constrcution materials. And the third one will be thick wooden panel, which commonly used as a construction material.

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SIP Panel

SIP Panel with OSB

TILE

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CNC MANUFACTURE

ROBOT

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PRODUCTION FABRICATION

Finally we end up with OSB and foam, which are the main construction material and combined as the model of SIP panel. We CNC the OSB board first and assemble them with press-fit technique.

For this one, we use waterjet machine to cut aluminium sheet and fold it manually just like to fold a pizza box. Cardboard is going to be the supporting structure inside.

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05 04

MATERIALS - RESEARCH MATERIALITY FABRICATION

Phase 01. RESEARCH Material: 9mm PLYWOOD Production: LaserCut

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Phase 02. RESEARCH Material: 12mm CARDBOARD Production: LaserCut

Phase 03. RESEARCH Material: 11mm OSB Board Production: CNC

Phase 04. FINAL PRODUCT Material: 1mm ALUMINIUM Production: WaterJet

Phase 04. FINAL PRODUCT Material: 11mm OSB Board Production: CNC

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Phase 04. FINAL PRODUCT Material: 11mm OSB Board Production: CNC and LaserCut

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MATERIALS - RESEARCH FABRICATION

CardBoard

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Plywood

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Aluminium OSB

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MATERIALS - PROPOSAL FABRICATION

11mm OSB

1mm Aluminium

500x500mm

OSB , Steel and LED Strips

500x500mm

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OSB and Acrylic

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PRODUCTION FABRICATION

INTERLOCKING

Slider Panels

Interlocking Pattern

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VERTICAL SLIDER

HORIZONTAL SLIDER

MIX SLIDER

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PRODUCTION FABRICATION

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1:1 Scale Prototype 500x500mm OSB Board with Steel Joints 137

SCALABILITY FABRICATION

Initually, Grasshopper simulation was created to visualize the construction sequence by ABB robotic arm. For the experimentation, a path of how robotic arm is going to rotate and developed the method and direction how is it going to rotate was generated. Firstly, a simplified movement of drag and rotate was tested.

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SCALABILITY FABRICATION

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SCALABILITY FABRICATION

â&#x20AC;&#x153;Based on series of experimentation by using industrial robotic arm, we realized that industrial robot has a finite working zone. Therefore, only limited structure can be built. Hence a relative robot (where the robot moves relative to the pieces it is assembling), such as Assembler is preffered to build a fully automated habitat.â&#x20AC;? M4G

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SCALABILITY FABRICATION

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SCALABILITY FABRICATION

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ARCHITECTURAL SCALABILITY

ARCHITECTURAL SPECULATION ARCHITECTURAL SCALE

In the first scenario, when dealing with such a powerful forces, standard means of crisis-management often resulted being inefficient. In emergency situations, there will be a need of prompt responses for help. Usually, this situation will become harder, as there are damages to the transportation infrastructure. Therefore, the design of the shelters is created to address these issues by proposing a structural system with of limited materials as per needed, easy to transfer without the need of on-site machinery, followed by minimum amount of time and manpower requirements, also offering a more safety working environment to reduce the risk of danger and injury.

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60 Tiles 14 Glass Tiles

52 Tiles 2 Glass Tiles

24 Tiles 24 Glass Tiles

50 Tiles 10 Glass Tiles

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60 Tiles 6 Glass Tiles

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ARCHITECTURAL SPECULATION ARCHITECTURAL SCALE

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In the past few decades, off-site construction methods have become a major alternative technique in the construction fields. However, a logistic method is also needed to be considered upon the completion of the production process. While the building is digitally designed, the parts can be easily calculated for the distribution. For example, the first figure will require 60 tiles for the whole structure and 6 acrylic tiles for the openings. All of the modular components can be easily fitted into a standard size truck and get delivered to the construction site, either as a panel form or in the flat pack sheet form that can be cut by cnc on site.

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06 05

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SCENARIO 02. DWELLING ARCHITECTURAL SPECULATION - SPECULATION ARCHITECTURAL SCALE SPECULATION

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ARCHITECTURAL SPECULATION ARCHITECTURAL SCALE

For the second scenario, poverty and housing crisis can be managed by introducing the more cost efficient structures. The design will be based on autonomous assembly, with a cheaper and easily accessed materials that will form the structural elements. As the shelters scenario, the modules of the house can be shipped in most cases only by the use of one standard sized truck and easily assembled on-site with the minimum need of infrastructure.

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ARCHITECTURAL SPECULATION ARCHITECTURAL SCALE

KINETIC WALL

KINETIC WINDOW

KINETIC FACADE

KINETIC OPENINGS

Opening Window

TRANSITION

Transition Wall to Door

Opening Door Transition Seating to Bed

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The diagram shows a way to design a smart modular house that is responsive to its environment and adaptive to its inhabitants. The most efficient way to achieve the result is to design according to predeterminate techniques that will end up to this smart behaviour. A system of modular structural elements are predefined and designed to meet these specifications in combination with modular robots, then later on will redefine the relationship of the dweller to his dwelling. The robot will accomplish the autonomous assembly and become a part of the end structure (as one of the building elements). Upon the completion, the robot can interact with its environment by receiving and giving data to the system, form and trans-form space, by both direct and indirect control.

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ARCHITECTURAL SPECULATION ARCHITECTURAL SCALE

72 Tiles 24 Glass Tiles

196 Tiles 15 Glass Tiles

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81 Tiles 10 Glass Tiles

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ARCHITECTURAL SPECULATION ARCHITECTURAL SCALE

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Assembling Sequence Simulation 163

ARCHITECTURAL SPECULATION ARCHITECTURAL SCALE

Large scale components will require an extra support to their structure to avoid forces that will question the safety of the building. The panels that are built to form walls will need a reinforcement on their sides to guarantee the stability. This can be accomplished by building walls perpendicular to the main walls, where interior walls can also help to the structureâ&#x20AC;&#x2122;s strength. Structural elementsVERTICAL such as SUPPORT-COLUMN beams and columns can be built with extra overlapping layers, with gaps in between, that are sized equal to the width of the panel/ robot, resulting a faster assembly and locking system. By following this methods, any displacements HOUSE and weak points stressed by compression, tension or bending can be avoided. FLOOR

HORIZONTAL SU HORIZONTAL SUPPORT-BEAM

VERTICAL SUPPORT-COLUMN

HOUSE

FLOOR

WALL

SHEAR WALL

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MAEB-TROPPUS LATNOZIROH FLOOR

HORIZONTAL SUPPORT

FLOOR

N MULOC-TROPPUS LACITREV

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ARCHITECTURAL SPECULATION ARCHITECTURAL SCALE

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06 05

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SCENARIO DESIGN PROPOSAL 01. EMERGENCY SHELTER - PROPOSAL ARCHITECTURAL SCALE SPECULATION

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

1:10 Prototype

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Exterior Dwelling Design Strategy

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Programs and Separation Dwelling Strategy

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Programs and Separation Dwelling Strategy

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Assembling Logic Experimentation

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LIVE

COOK

EAT

BATH

WORK

SLEEP

GYM

Programs and Spaces

Function and Spatial Relationship

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Main Structure

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Design Strategy

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Design Proposal

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Design Proposal

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Design Proposal

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Interior Views

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Interior Views

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

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DESIGN PROPOSAL - PROTOTYPE ARCHITECTURAL SCALE

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DESIGN PROPOSAL - PROTOTYPE ARCHITECTURAL SCALE

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Phase 01 Programs and Spaces

Phase 02 Initial Aggregation

Phase 03 Room Separation

Phase 04 Initial Dwelling Proposal

Phase 05 Dwelling Design Development A

Phase 06 Dwelling Design Development B

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Final Dwelling Design Proposal

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DESIGN PROPOSAL - PRODUCTION ARCHITECTURAL SCALE

A whole system of on-site manufacturing, logistic and automated assembly process is also being developed in Assembler Assemble project. Robots and building components will be fitted into containers and deployed to the site. Every building will require a comparison of 1% of robot to number of needed building components, such as tiles (bricks), glass box, aluminium box, etc.

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DESIGN PROPOSAL - TYPE OF TILES ARCHITECTURAL SCALE

Structure

In order to transform the aggregated structure to become fully autonomous and inhabitable, Assembler Assemble integrate several aspect to the habitable space such as faรงade, water-proofing, electricity, lighting and heating system.

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Types of Tiles

Openings

Waterproof

Lighting

Heating

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Electrical

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DESIGN PROPOSAL - TYPE OF TILES ARCHITECTURAL SCALE

ELECTRICIAL

Faceplate Tile

Electricity Network in Alumunium Profile

Framing System

Underplate Tile

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Contact Copper Tube will be installed on each sides of the tiles to generate electricity.

Electricity will be generated by the contact of the copper, then will be generated through the connected tiles.

HEATING

Faceplate Tile

Heating Pipes

Alumunium Heat Exchanger

Thermal Insulation

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Framing System

Underplate Tiles Built - in Underfloor Heating System

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DESIGN PROPOSAL - TYPE OF TILES ARCHITECTURAL SCALE

LIGHTING

LED Strip Alumunium Profile

Faceplate Tile Contact Tube to generate power for lighting - refer to electircal tile for details

LED Strips and Acrylic Light Difuser

Alumunium Profile

Electricity Network

Framing System

Underplate Tiles

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Contact Tube

PLUMBING

Faceplate Tile

Hot and Cold Water Plumbing System

Framing System

Hot and Cold Water Supply will be distributed through the built-in plumbing system tiles

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Underplate Tiles

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DESIGN PROPOSAL - SHAFT SYSTEM ARCHITECTURAL SCALE

The robot is using the Step Climbing strategy, to build in different directions and levels. We concluded to the solution of the robots building a central staircase that would help them climb up and which would also be the main one for the end user of the house.

This strategy is effective in terms of energy consumption, and suitable to build the central core of the building. The stairs are surrounding the technical shaft, which includes the plumbing system, that will be integrated into the aggregation strategy. We put the wet zone surrounding the shaft to be directly connected to the pipes and avoid any possible leakage due to the water distribution.

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Wet Zone Perspective

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DESIGN PROPOSAL - PATTERNS AND OPENINGS ARCHITECTURAL SCALE

Option 01

Option 02

Option 03

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Patterns fo Screens and Openings

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Interior Perspective

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Design Option 01 - Exterior View 212

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Design Option 01 - Interior View 01 214

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Design Option 01 - Interior View 02 216

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Construction Phase 218

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Design Option 02 - Exterior View 220

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DESIGN PROPOSAL

ARCHITECTURAL SCALE

Dwelling Design Option 02 - Interior View 222

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DESIGN PROPOSAL - VIRTUAL REALITY ARCHITECTURAL SCALE

Dwelling User - Visualisation

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DESIGN PROPOSAL - VIRTUAL REALITY ARCHITECTURAL SCALE

Dwelling User - Visualisation

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DESIGN PROPOSAL - VIRTUAL REALITY ARCHITECTURAL SCALE

Unity VR Exterior Views

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DESIGN PROPOSAL - VIRTUAL REALITY ARCHITECTURAL SCALE

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B-PRO 2018

B-PRO PROTOTYPES BPRO SHOW 2018

BPro Show Selected Prototype Scale 1 : 1 Average Size : 3 x 3 x 3m Tiles : 160-180 pieces

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B-PRO PROTOTYPES BPRO SHOW 2018

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Prototype 01

Prototype 05

OSB Tiles: 220 pieces Alumunium Tiles:16 pieces Glass Tiles:4 pieces

OSB Tiles: 130 pieces Alumunium Tiles: 9 pieces Glass Tiles: - pieces

Prototype 02

Prototype 06

OSB Tiles: 160 pieces Alumunium Tiles: 11 pieces Glass Tiles: - pieces

OSB Tiles: 179 pieces Alumunium Tiles: 14 pieces Glass Tiles: 4 pieces

Prototype 03

Prototype 07

OSB Tiles: 155 pieces Alumunium Tiles: 17 pieces Glass Tiles: - pieces

OSB Tiles: 175 pieces Alumunium Tiles: 14 pieces Glass Tiles: 4 pieces

Prototype 04

Prototype 08

OSB Tiles: 135 pieces Alumunium Tiles: 10 pieces Glass Tiles: - pieces

OSB Tiles: 212 pieces Alumunium Tiles: - pieces Glass Tiles: 8 pieces

Prototype 13

OSB Tiles: 187 pieces Alumunium Tiles: 18 pieces Glass Tiles: 6 pieces

OSB Tiles: 166 pieces Alumunium Tiles: 20 pieces Glass Tiles: 3 pieces

Prototype 10

Prototype 14

OSB Tiles: 196 pieces Alumunium Tiles: 18 pieces Glass Tiles: 7 pieces

OSB Tiles: 203 pieces Alumunium Tiles: 18 pieces Glass Tiles: - pieces

Prototype 11

Prototype 15

OSB Tiles: 230 pieces Alumunium Tiles: 23 pieces Glass Tiles: 6 pieces

OSB Tiles: 219 pieces Alumunium Tiles: - pieces Glass Tiles: - pieces

Prototype 12

Prototype 16

OSB Tiles: 219 pieces Alumunium Tiles: 22 pieces Glass Tiles: 6 pieces

OSB Tiles: 180 pieces Alumunium Tiles: 16 pieces Glass Tiles: 5 pieces

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Prototype 09

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BPRO PROTOTYPE B-PRO PROTOTYPES

ARCHITECTURAL BPRO SHOW 2018SPECULATION

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B-PRO PROTOTYPES BPRO SHOW 2018

OPTION 01 - Approximate 175 Tiles

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OPTION 02 - Approximate 170 Tiles

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OPTION 03 - Approximate 170 Tiles

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B-PRO PROTOTYPES - ASSEMBLING SEQUENCE

BPRO SHOW 2018

ALUMINIUM JOINTS L In 143pcs L Out 48pcs S 5H 98pcs

TILES

STAIRS L In (light) 18pcs OSB 124 pcs

Stair Support 5pcs L Out (light) 16pcs Stair Steps 5pcs S 5H (light) 4pcs

Aluminium 16pcs

TIMBER JOINTS Glass 16pcs

S 4H 8pcs S 3H 29pcs

LED Strip 16pcs S 2H 47pcs S 1H 101pcs

S 4H (light) 3pcs S 3H (light) 14pcs S 2H (light) 14pcs S 1H (light) 12pcs

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B-PRO PROTOTYPES - ASSEMBLING SEQUENCE BPRO SHOW 2018

Tiles 33 pieces Weight 149kg

Tiles 16 pieces Weight 72kg

DAY 01.

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Tiles 6 pieces Weight 27kg

Tiles 2 pieces Weight 9kg

DAY 02.

Tiles 12 pieces Weight 54kg

LED Strip 45m Weight 0.3kg

DAY 03.

Tiles 14 pieces Weight 63kg

Stair 14 pieces Weight 7kg

Tiles 8 pieces Weight 10kg

DAY 04.

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Tiles 40 pieces Weight 180kg

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B-PRO PROTOTYPES - ASSEMBLING SEQUENCE BPRO SHOW 2018

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REFLECTION

REFLECTION M4G

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Considerable gains in quality and productivity in the manufacturing sector has been possible because of automation, digitalisation and robotics. However, the same has not been the case in construction even though digitalisation has been successful in automating design. Therefore, the notion of automation has been brought to the fieldâ&#x20AC;&#x2122;s forefront, and as indeed upon us as an upcoming scenario for construction and architecture. //DISCRETE Robots. Assembler is aimed to develop an inventory of basic modular and movement units, also to yield a number of different scenarios, where each one is configured to an optimal geometry for a specific task. Some improvement could be achived by developing a better interlocking mechanism system between the robot pieces and the building components. //DISCRETE Materials. Combinatorial iterations was created based on a simple-two-ways connection logic resulting various options to create an architectural compositions. Each combinations make its own pieces becoming more flexible and applicable to different scenarios of architectural proposals. To be more developed, an inventory libraries of connection logic could be improved, resulting a better jointing system between each components. //AGGREGATION. Rule-based housing design, which incorporate robot locomotion system, and to follow local decision of each robots on global structure to generate an inhabitable space. Areas of design could be improved, including the adaptivity and reconfigurability of the structure. //COMPUTATIONAL Logic. The maturity of machine learning is the other area of development. Machine learning constitutes an artificial intelligence application that can recognize patterns. Areas to be developed, such as space generation, structure optimizatio, and a further autonomous system for robot path calculation could be achieved.

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//FABRICATION. Fast production and easy to assemble has always been a key to Assembler fabrication and production system. The use of flat sheet CNC materials to be folded has an obvious advantage to the project. However, to be fully autonomous, a better system of automating the production can be applied to the project, resulting a more standardized and higher quality of each pieces.

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APPENDIX

WORKSHOP 1 - GAME APPENDIX

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JENGA BRIDGE The aim of the Jenga Bridge is to create a Construction Game by using the basic aggregration logic and interaction as a Jenga Game.

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The structure is created based on the procedural landscape, which then will be randomly removed. At the final stage, players will have to place the tile back to the structure, then activate the gravity to simulate if the structure is stable or not.

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WORKSHOP 1 - GAME APPENDIX

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COLOR MATCH Color match is a puzzle game that allows the player to rotate the cubes to achieve the matching color between sides.

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Within the game, the outline of the defined shape is created to help the player to place the cubes into the right area and to combine them into the final shape.

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WORKSHOP 2 - ROBOT APPENDIX

Robotic Workshop was aim to explore the basic machine learning idea through the interaction between robot and human. The main task of the robot is to assemlbe the structure, which mirror the built structure by human. A camera system was set up to scan the initial position of the tile, which later created a feedback loop for each of human action. At the first process, human created a structure, then robot will mirror it on to the other side of the table. In the next step, human will give a logic instruction sequence for the robot to follow, and robot will execute the order. At the final stage, robot will learn from the previous structure to become fully autonomous in assembling the future structure.

Mirrored-Actions Experimentations

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FINAL FINAL FORM FORM 01 01

COLLISION COLLISION CHECKING CHECKING PLAYER PLAYER TILES TILES

ROBOT TILES ROBOT TILES

CONNECTION CONNECTION LOGIC LOGIC

MIRROR IN X & Z AXIS MIRROR IN X & Z AXIS

GENERATIVE GAME GENERATIVE GAME

Robotic Simulation 01

FINAL FORM 04 FINAL FORM 04

COLLISION CHECKING COLLISION CHECKING

PLAYER TILES PLAYER TILES

ROBOT TILES ROBOT TILES

CONNECTION LOGIC CONNECTION LOGIC

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Robotic Simulation 02

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WORKSHOP 2 - ROBOT APPENDIX

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WORKSHOP 3 - ALGORITHM APPENDIX

The Algorithm Workshop equipped us with the more advanced computational algorithms such as A* Shortest Path, Diffusion Limited Aggregation (DLA) , Cellular Automata (Conway’s Game Of Life) (CA) and Space Filling Curve . Two structure was aggregated using the combination of those algorithms in an interactive apps called the “Generative Toolbox”.

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RESEARCH CLUSTER 4, GILLES RETSIN, MANUEL JIMENEZ GARCIA, VICENTE SOLER M4G: Mengyu Huang, Dafni Katrakalidi, Martha Masli, Man Nguyen, Wenji Zhang

UCL, The Bartlett School of Architecture

RESEARCH CLUSTER 4, GILLES RETSIN, MANUEL JIMENEZ GARCIA, VICENTE SOLER M4G: Mengyu Huang, Dafni Katrakalidi, Martha Masli, Man Nguyen, Wenji Zhang