コヒーレントリスク指標を用いたリスク回避型移動計画法
Risk-Averse Receding Horizon Motion Planning for Obstacle Avoidance using Coherent Risk Measures
不確実なダイナミクスと動的障害物がある環境で、コヒーレントリスク指標を用いて障害物回避制約を定式化したMPCベースのリスク回避型移動計画法を提案し、ウェイポイント追従アルゴリズムの実現可能性と有限時間でのタスク完了を証明した。
著者: Anushri Dixit, Mohamadreza Ahmadi, Joel W. Burdick
分類: eess.SY, cs.RO, cs.SY, math.OC
原文アブストラクト
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.
