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We consider a chance-constrained multi-robot motion planning problem in thepresence of Gaussian motion and sensor noise. Our proposed algorithm, CC-K-CBS,leverages the scalability of kinodynamic conflict-based search (K-CBS) inconjunction with the efficiency of the Gaussian belief trees used in theBelief-A framework, and inherits the completeness guarantees of Belief-A'slow-level sampling-based planner. We also develop three different methods forrobot-robot probabilistic collision checking, which trade off computation withaccuracy. Our algorithm generates motion plans driving each robot from itsinitial state to its goal while accounting for the evolution of its uncertaintywith chance-constrained safety guarantees. Benchmarks compare computation timeto conservatism of the collision checkers, in addition to characterizing theperformance of the planner as a whole. Results show that CC-K-CBS can scale upto 30 robots.

Chance-Constrained Multi-Robot Motion Planning under Gaussian Uncertainties