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.

翻译：本文研究在存在随机动态障碍物的环境下，具有不确定动力学特性的智能体进行风险规避滚动时域运动规划问题。我们提出一种模型预测控制（MPC）框架，利用一致风险度量构建避障约束。为处理状态动力学中的扰动或过程噪声，采用风险感知方式收紧状态约束，进而设计扰动反馈策略。同时，针对离散分布场景，提出基于所提MPC方案的航点跟踪算法，证明其风险敏感递归可行性并保证有限时间内任务完成。进一步分析条件风险价值（CVaR）、熵风险价值（EVaR）及g-熵风险度量等常用一致风险指标，并提出其在MPC中的可解耦合方法。最后通过仿真实验验证框架有效性。