Discrete-Time High-Order Control Barrier Function Approach for Quadrotors Obstacle Avoidance

The ability to navigate through dense obstacles and narrow gaps is crucial for practical deployment of quadrotors in complex environments. However, existing approaches face challenges in stable planning and exhibit limitations in modeling precision. In this paper, we propose a novel framework for efficient motion planning and safe trajectory tracking in cluttered environments. We first propose a global planner that incorporates A* pathfinding and polynomial trajectories. Then, we develop a novel quadrotor control architecture by integrating nonlinear model predictive control (NMPC) and discrete-time high-order control barrier functions (DHOCBFs). This architecture achieves optimal dynamic performance while ensuring safety. Extensive simulations experiments are conducted in environments characterized by dense obstacles and narrow gaps, validating its effectiveness in attitude control and obstacle avoidance.