Risk-Aware Path Planning Using CVaR for Quadrotors

Abstract

In the presence of obstacles with static position uncertainty, a risk-aware path planning method using the conditional value-at-risk (CVaR) is proposed. Given the current position of the quadrotor, CVaR can effectively quantify the risk of collision with the static uncertain obstacle whose center of mass (CoM) follows a joint normal distribution. As a specific application of CVaR, the CVaR constrained A*(CVaR-A* for simplicity) algorithm is designed to search for the optimal path while ensuring the safety of the quadrotor. The simulation results are presented to indicate the feasibility and effectiveness of the proposed CVaR-A* algorithm.