论文标题
使用连贯的风险措施避免障碍物的避免障碍物的水平运动计划避免风险的后退运动计划
Risk-Averse Receding Horizon Motion Planning for Obstacle Avoidance using Coherent Risk Measures
论文作者
论文摘要
本文研究了在存在随机，动态障碍的情况下，针对具有不确定动力学的药物的防范水平运动计划的问题的问题。我们提出了一种模型预测控制（MPC）方案，该方案使用连贯的风险度量来制定障碍避免限制。为了处理状态动力学中的干扰或过程噪声，以风险感知方式收紧了状态约束，以提供扰动反馈政策。我们还建议使用算法的航向点，该算法使用建议的MPC方案进行离散分布，并证明其对风险敏感的递归可行性，同时保证有限的任务完成。我们进一步研究了一些常用的相干风险指标，即有条件的危险价值（CVAR），熵价危险价值（EVAR）和G-进入gentropic风险度量，并提出了MPC中的可拖动掺入。我们通过模拟研究说明了我们的框架。
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.
