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Control of Multiple Autonomous Robots

New Ideas Composition of behaviors via “live object” hierarchy Live objects encapsulate data, algorithms and control of execution in a multi-threaded architecture Automated mode-switching for formation control Reinforcement learning to implement mode switching. Impact

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Control of Multiple Autonomous Robots

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  1. New Ideas Composition of behaviors via “live object” hierarchy Live objects encapsulate data, algorithms and control of execution in a multi-threaded architecture Automated mode-switching for formation control Reinforcement learning to implement mode switching Impact Live object design allows straightforward re-use and combination of sensing and control algorithms Mode-switching formation control leads to provable and predictable performance Reinforcement learning for mode switching reduces time to design software-enabled control algorithms 9 8 7 6 5 4 3 2 1 0 -1 -2 0 2 4 6 8 10 Control of Multiple Autonomous Robots Trajectories of R1, R2 and R3 R1 R2 R3 Y (m) X (m) Robots automatically configure themselves and fall into desired formation Sony Aibo robots in formation Apr 00 Dec 00 Oct 01 Jun 02 Automatic generation of simulation code from high level description Rapid tasking and deployment of a team of 3-4 robots Learning of multirobot team behaviors Demo with multiple wheeled and legged robots

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