Distributionally Robust Acceleration Control Barrier Filter for Efficient UAV Obstacle Avoidance

Abstract

Dynamic obstacle avoidance (DOA) for unmanned aerial vehicles (UAVs) requires fast reaction under limited onboard resources. We introduce the distributionally robust acceleration control barrier function (DR-ACBF) as an efficient collision avoidance method maintaining safety regions. The method constructs a second-order control barrier function as linear half-space constraints on commanded acceleration. Latency, actuator limits, and obstacle accelerations are handled through an effective clearance that considers dynamics and delay. Uncertainty is mitigated using Cantelli tightening with per-obstacle risk. A DR-conditional value at risk (DR-CVaR) early trigger expands margins near violations to improve DOA. To meet real-time avoidance-control at 100 Hz, we use fixed-time Gauss-Southwell projections instead of quadratic programs (QP). Simulation results show similar avoidance performance with 31% lower computational load than QP and outperform the state-of-the-art baseline approaches. Experiments with Crazyflie UAVs demonstrate the feasibility of our approach.