Risk-averse receding horizon motion planning for obstacle avoidance using coherent risk measures

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.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This paper investigates the problem of risk-averse receding horizon motion planning for agents with uncertain dynamics in the presence of stochastic, dynamic obstacles. The proposed model predictive control (MPC) scheme formulates the obstacle avoidance constraint using coherent risk measures. A waypoint following algorithm using the MPC scheme is also proposed and proved to be risk-sensitive and recursively feasible while guaranteeing finite-time task completion. The paper further explores commonly used coherent risk metrics and proposes a tractable incorporation within MPC. Simulation studies are conducted to illustrate the framework.