Distributionally Robust Acceleration Control Barrier Filter for Efficient UAV Obstacle Avoidance

TL;DR

This paper presents a distributionally robust control barrier function approach for UAV obstacle avoidance that ensures safety with lower computational effort, suitable for real-time applications.

Contribution

It introduces the DR-ACBF method combining distributional robustness, risk management, and efficient computation for UAV collision avoidance.

Findings

Achieves similar avoidance performance with less computation.

Demonstrated feasibility on Crazyflie drones.

Handles latency, actuator limits, and obstacle dynamics effectively.

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)based early trigger expands margins near violations to improve DOA. Real-time execution is ensured via constant-time Gauss-Southwell projections. Simulation studies achieve similar avoidance performance at substantially lower computational effort than state-of-the-art baseline approaches. Experiments with Crazyflie drones demonstrate the feasibility of our approach.