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.

翻译：在随机移动的障碍面前,我们考虑对风险敏感的运动规划问题。为此,我们采用模型预测控制(MPC)办法,并在MPC问题中设置障碍避免限制,作为分配上强有力的限制,并设定了KL差异的模糊性。这种限制是EVa(EVaR)的双重代表。我们基于这一观点,建议采用一种算法,遵循路标,讨论其可行性和在有限时间内完成。我们比较了使用EVaR(EVaR)获得的政策与使用另一种共同的一致风险措施(CVaR)获得的政策,即2D系统的数值实验。我们还在3D四分法模拟中执行路径算法。