This author is incredibly excited to live in this time of renaissance for robot motion. Within the next two decades, expect advances in motion planning that parallel those achieved in artificial intelligence during the past couple of decades. Days that humanoid robots can dance alongside humans may be far ahead. But, just like computer artificial intelligence Deep Blue beat chess champion Garry Kasparov in 1997 and Watson bested its human Jeopardy opponents in 2011, expect full-size robotic race cars to drive faster and better than Formula One world champion Michael Schumacher and robotic airplanes to outmaneuver even the best human acrobatic pilots.
Talos, MIT’s entry vehicle in the Defense Advanced Research Projects Agency’s Urban Challenge, was guided by a robust extension of a widely used samplingbased motion planning algorithm called the Rapidly-exploring Random Tree. (Jason Dorfman)
POTENTIAL WIDE IMPACT Planning algorithms also hold the potential for long-lasting societal and economic impact. Algorithms that predict drivers’ intentions and react when necessary may save many lives that would otherwise be lost to traffic accidents. Other planning algorithms can keep tight air spaces safe, take more significant roles in governing unmanned vehicles, and help us discover our planet and beyond. We are in a good position to take a lead role and make MIT and the AeroAstro Department home to the development and deployment of this technology. SERTAC KARAMAN is the Charles Stark Draper Assistant Professor of Aeronautics and Astronautics. His research interests lie in the broad areas of robotics and control theory. He studies the applications of probability theory, stochastic processes, stochastic geometry, formal methods, and optimization for the design and analysis of high-performance cyber-physical systems. Sertac Karaman may be reached at sertac@mit.edu.
Efficient motion: easy for us, a challenge for robots
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