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：我们考虑在存在高斯运动和传感器噪声情况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融合了运动学冲突搜索(K-CBS)的可扩展性与Belief-A框架中高斯信念树的效率优势，并继承了Belief-A底层基于采样的规划器的完备性保证。我们进一步开发了三种不同的机器人间概率碰撞检测方法，在计算效率与精度之间进行权衡。该算法在规划从初始状态到目标状态的运动轨迹时，能够同步考虑各机器人不确定性演化过程，同时提供机会约束的安全保障。基准测试在表征规划器整体性能的同时，对比了碰撞检测器的计算时间与保守性关系。实验结果表明，CC-K-CBS可扩展至30个机器人规模。