论文标题
分配强大的RRT具有风险分配
Distributionally Robust RRT with Risk Allocation
论文作者
论文摘要
提出了在不确定环境中运行的机器人的分配强大风险分配到基于抽样的运动计划算法中的集成。我们通过将整个计划范围内定义的分配稳健的联合风险约束分解为鉴于总风险预算的个人风险限制，进行了不均匀的风险分配。具体而言，使用单个风险限制定义的确定性收紧，以定义我们提出的确切风险分配程序。我们将风险分配技术嵌入基于抽样的运动计划算法中的想法实现了保守的，但越来越多的风险可行轨迹，以进行有效的状态探索。
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.
