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• W2901405768 abstract "Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents, such as humans. This paper introduces a novel framework for robot navigation that accounts for high-order system dynamics and maintains safety in the presence of external disturbances, other robots, and non-deterministic intentional agents. Our approach precomputes a tracking error margin for each robot, generates confidence-aware human motion predictions, and coordinates multiple robots with a sequential priority ordering, effectively enabling scalable safe trajectory planning and execution. We demonstrate our approach in hardware with two robots and two humans. We also showcase our work's scalability in a larger simulation." @default.
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• W2901405768 date "2018-11-14" @default.
• W2901405768 modified "2023-09-27" @default.
• W2901405768 title "A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance" @default.
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