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

翻译：本文考虑存在高斯运动与传感器噪声下的机会约束多机器人运动规划问题。所提出的算法CC-K-CBS，融合了动力学冲突搜索（K-CBS）的可扩展性与Belief-A框架中高斯信念树的高效性，并继承了Belief-A底层基于采样的规划器的完备性保证。同时，我们开发了三种不同的机器人间概率碰撞检测方法，以在计算开销与准确性之间进行权衡。该算法在考虑机器人不确定性演化并满足机会约束安全保障的前提下，生成驱动每个机器人从初始状态到目标的运动规划方案。基准测试不仅评估了碰撞检测器计算时间与保守性之间的权衡，还全面刻画了规划器的性能。实验结果表明，CC-K-CBS可扩展至30个机器人。