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与传感器噪声时的机会约束多机器人运动规划问题。所提出的算法CC-K-CBS结合了动力学冲突搜索（K-CBS）的可扩展性与Belief-A框架中高斯置信树的高效性，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，在计算量与准确性之间进行权衡。该算法生成的运动规划能够驱动每个机器人从初始状态到达目标状态，同时考虑其不确定性的演化，并提供机会约束安全保障。基准测试对比了碰撞检测器的保守性与计算时间，并评估了规划器的整体性能。结果表明，CC-K-CBS可扩展至30个机器人。