We consider the problem of risk-sensitive motion planning in the presence of randomly moving obstacles. To this end, we adopt a model predictive control (MPC) scheme and pose the obstacle avoidance constraint in the MPC problem as a distributionally robust constraint with a KL divergence ambiguity set. This constraint is the dual representation of the Entropic Value-at-Risk (EVaR). Building upon this viewpoint, we propose an algorithm to follow waypoints and discuss its feasibility and completion in finite time. We compare the policies obtained using EVaR with those obtained using another common coherent risk measure, Conditional Value-at-Risk (CVaR), via numerical experiments for a 2D system. We also implement the waypoint following algorithm on a 3D quadcopter simulation.

中文翻译：

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使用熵风险值的风险敏感型运动计划

我们考虑存在随机移动障碍物的风险敏感型运动计划问题。为此，我们采用模型预测控制（MPC）方案，并将MPC问题中的避障约束设置为具有KL发散歧义集的分布鲁棒约束。此约束是熵风险值（EVaR）的双重表示。基于此观点，我们提出了一种算法来跟踪航路点并讨论其在有限时间内的可行性和完成性。我们通过2D系统的数值实验，比较了使用EVaR获得的策略与使用另一种常见的一致风险度量（条件风险值（CVaR））获得的策略。我们还在3D四轴飞行器仿真中实现航路点跟随算法。