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Distributionally Robust Acceleration Control Barrier Filter for Efficient UAV Obstacle Avoidance
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DOI：10.1109/LRA.2026.3677752.png)
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Abstract
En 中文
Dynamic obstacle avoidance (DOA) for uncrewed 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.
Keywords:
Collision avoidance
aerial systems: perception and autonomy
robot safety
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