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：我们考虑在高斯运动和传感器噪声存在的情况下的机会约束多机器人运动规划问题。我们提出的算法CC-K-CBS，利用了基于运动冲突的搜索(K-CBS)的可扩展性，结合了Belief-A框架中使用的高斯置信树的效率，并继承Belief-A低级基于采样的计划器的完整性保证。我们还开发了三种不同的机器人-机器人概率碰撞检查方法，以平衡计算量和准确性。我们的算法生成运动计划，将每个机器人从其初始状态驱动到其目标状态，并考虑其不确定性的演变与机会约束的安全保证。基准测试将计算时间与碰撞检查器的保守性进行比较，同时对规划器的整体性能进行了表征。结果显示，CC-K-CBS可以扩展到30个机器人。