An integration of distributionally robust risk allocation into sampling-based motion planning algorithms for robots operating in uncertain environments is proposed. We perform non-uniform risk allocation by decomposing the distributionally robust joint risk constraints defined over the entire planning horizon into individual risk constraints given the total risk budget. Specifically, the deterministic tightening defined using the individual risk constraints is leveraged to define our proposed exact risk allocation procedure. Our idea of embedding the risk allocation technique into sampling based motion planning algorithms realises guaranteed conservative, yet increasingly more risk feasible trajectories for efficient state space exploration.

翻译：针对在不确定环境中运行的机器人运动规划问题，提出将分布鲁棒风险分配机制集成到基于采样的运动规划算法中。通过将整个规划时域上定义的分布鲁棒联合风险约束分解为给定总风险预算下的个体风险约束，实现非均匀风险分配。具体而言，利用基于个体风险约束定义的确定性紧缩策略，构建了所提出的精确风险分配流程。将风险分配技术嵌入基于采样的运动规划算法这一创新思路，能够在保证保守性的同时，逐步生成具有更高风险可行性的轨迹，从而实现高效的状态空间探索。