This paper studies the problem of risk-averse receding horizon motion planning for agents with uncertain dynamics, in the presence of stochastic, dynamic obstacles. We propose a model predictive control (MPC) scheme that formulates the obstacle avoidance constraint using coherent risk measures. To handle disturbances, or process noise, in the state dynamics, the state constraints are tightened in a risk-aware manner to provide a disturbance feedback policy. We also propose a waypoint following algorithm that uses the proposed MPC scheme for discrete distributions and prove its risk-sensitive recursive feasibility while guaranteeing finite-time task completion. We further investigate some commonly used coherent risk metrics, namely, conditional value-at-risk (CVaR), entropic value-at-risk (EVaR), and g-entropic risk measures, and propose a tractable incorporation within MPC. We illustrate our framework via simulation studies.

本文研究了在存在随机动态障碍的情况下，具有不确定动力学的代理的风险规避后退地平线运动规划问题。我们提出了一种模型预测控制（MPC）方案，该方案使用一致的风险度量来制定避障约束。为了处理状态动态中的干扰或过程噪声，以风险意识的方式收紧状态约束以提供干扰反馈策略。我们还提出了一种路点跟踪算法，该算法使用所提出的离散分布 MPC 方案，并证明其风险敏感的递归可行性，同时保证有限时间任务完成。我们进一步研究了一些常用的连贯风险度量，即条件风险价值（CVaR）、熵风险价值（EVaR）和g熵风险度量，并提出了在MPC中易于处理的合并。我们通过模拟研究来说明我们的框架。