Distributionally Robust Acceleration Control Barrier Filter for Efficient UAV Obstacle Avoidance
This addresses efficient and safe obstacle avoidance for UAVs, but it is incremental as it builds on existing control barrier function methods.
The paper tackled dynamic obstacle avoidance for UAVs by introducing a distributionally robust acceleration control barrier function (DR-ACBF) method, achieving similar avoidance performance with substantially lower computational effort than state-of-the-art baselines.
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.