Distributionally Robust CVaR-Based Safety Filtering for Motion Planning in Uncertain Environments

TL;DR

This paper introduces a distributionally robust safety filtering method for autonomous robot motion planning that reduces collision risks by using multiple obstacle trajectory samples and safe halfspaces, ensuring formal safety guarantees.

Contribution

It proposes a novel DRO-based safety filtering approach that reformulates collision avoidance with safe halfspaces, improving safety and efficiency in uncertain environments.

Findings

Effective collision risk reduction demonstrated in simulations

Computationally efficient safety filtering method

Formal safety guarantees provided by the DRO approach

Abstract

Safety is a core challenge of autonomous robot motion planning, especially in the presence of dynamic and uncertain obstacles. Many recent results use learning and deep learning-based motion planners and prediction modules to predict multiple possible obstacle trajectories and generate obstacle-aware ego robot plans. However, planners that ignore the inherent uncertainties in such predictions incur collision risks and lack formal safety guarantees. In this paper, we present a computationally efficient safety filtering solution to reduce the collision risk of ego robot motion plans using multiple samples of obstacle trajectory predictions. The proposed approach reformulates the collision avoidance problem by computing safe halfspaces based on obstacle sample trajectories using distributionally robust optimization (DRO) techniques. The safe halfspaces are used in a model predictive control (MPC)-like safety filter to apply corrections to the reference ego trajectory thereby promoting safer planning. The efficacy and computational efficiency of our approach are demonstrated through numerical simulations.