The paper “Risk-Aware Motion Planning and Control Using CVaR-Constrained Optimization“, authored by Astghik Hakobyan, Gyeong Chan Kim, and Insoon Yang, has been accepted to the IEEE Robotics and Automation Letters (RA-L) and the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). This paper provides a principled decision-making method that systematically adjusts the tradeoff between safety and conservativeness in an environment with randomly moving obstacles.

Abstract: We propose a risk-aware motion planning and decision-making method that systematically adjusts the safety and conservativeness in an environment with randomly moving obstacles. The key component of this method is the conditional value-at-risk (CVaR) used to measure the safety risk that a robot faces. Unlike chance constraints, CVaR constraints are coherent, convex, and distinguish between tail events. We propose a two-stage method for safe motion planning and control: a reference trajectory is generated by using RRT* in the first stage, and then a receding horizon controller is employed to limit the safety risk by using CVaR constraints in the second stage. However, the second stage problem is nontrivial to solve as it is a triple-level stochastic program. We develop a computationally tractable approach through (i) a reformulation of the CVaR constraints, (ii) a sample average approximation, and (iii) a linearly constrained mixed integer convex program formulation. The performance and utility of this risk-aware method are demonstrated through simulation using a 12-dimensional model of quadrotors.