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.

I. Introduction

UAV Path planning is a crucial research area. In recent years, there has been an increasing demand for the UAV to fly autonomously in complex, dangerous and unknown areas, including infrastructure inspection [1] [2] [3], underground mine exploration [4] [5] [6] and rescue operations [7] [8] [9]. These requirements have led to increasingly high technical demands for UAVs, particularly in path planning. When planning the path of UAV, it is necessary to consider the following factors: 1) Safety: The path is planned to avoid obstacles and hazardous areas to ensure the safe flight of the UAV. 2) Efficiency: The path planning should take into account variables such as flight duration and fuel consumption, to ensure the lowest possible factor of these variables as well as to guarantee efficient task accomplishment. 3) Feasibility: It is essential to ensure that the path planning can be implemented smoothly in practice. It is necessary to avoid overly complex or challenging path planning scenarios.