Multi-rotor UAV path planning based on Model Predictive Control and Control Barrier Function

The safety of unmanned aerial vehicles (UAVs) path planning in autonomous urban flight is challenging due to the presence of buildings. Previous studies have focused more on the distance between buildings and UAVs, but ignored the effect of UAV state on path planning. In this paper, we propose a path planning method based on model predictive control (MPC) and control barrier functions (CBF) for UAVs to effectively address the challenge of autonomous UAVs flight. By incorporating CBF and MPC, the MPC optimization is formulated to predict the UAV's flight path based on the current state of the UAV in the receding horizon before considering obstacle avoidance. The CBF constraint guarantees that there are no collisions between the UAV and obstacles, and that the MPC creates an optimal path. This method helps planners design control strategies that meet safety requirements. It improves the spatial configuration between the UAV and nearby obstacles, ensuring that they remain outside the safe range. The method has been verified through simulation and real-world experiments, demonstrating that the UAV maintains a safe distance from obstacles and can also find the shortest path during its flight.