The High-speed Rotorcraft UAV Trajectory Planning Based on the Beetle Antennae Search Optimization Algorithm

In this study, a novel exponential artificial potential field is applied to the three-dimensional trajectory planning of high-speed rotor drones. It perfectly avoids obstacles and accurately reaches the target position, and is difficult to fall into the local minimum. However, the selection of gravitational gain coefficient and repulsive gain coefficient in the artificial potential field method is a very difficult problem. In this paper, we choose to use the beetle antennae search optimization algorithm to filter out the most suitable gain coefficient. The BAS optimization algorithm is used for the first time to optimize the gain coefficient of the artificial potential field. Due to its individual search mechanism and update strategy, the optimization algorithm accelerates the speed of iterative convergence and reduces the possibility of falling into the local optimal solution of the algorithm. In the simulation experiment, it can converge to the optimal value after the number of iterations less than ten times. The results well prove that the algorithm can realize the global path planning of the high-speed rotor drones. Moreover, the optimized trajectory planning method has a fast convergence speed and a stable convergence effect, and is suitable for the three-dimensional real-time trajectory planning of high-speed rotor drones.