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Robust motion planning is a well-studied problem in the robotics literature,yet current algorithms struggle to operate scalably and safely in the presenceof other moving agents, such as humans. This paper introduces a novel frameworkfor robot navigation that accounts for high-order system dynamics and maintainssafety in the presence of external disturbances, other robots, andnon-deterministic intentional agents. Our approach precomputes a tracking errormargin for each robot, generates confidence-aware human motion predictions, andcoordinates multiple robots with a sequential priority ordering, effectivelyenabling scalable safe trajectory planning and execution. We demonstrate ourapproach in hardware with two robots and two humans. We also showcase ourwork's scalability in a larger simulation.

A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance