UAV Trajectory Planning Based on Improved Quantum Particle Swarm Optimization

Trajectory planning is important for unmanned aerial vehicle (UAV) to smoothly execute tasks, and it is also a focus of attention for scholars throughout the world. UAV seeks an optimal or suboptimal path from the starting point to the ending point, with the goal of meeting the predetermined target requirement, while satisfying its own mobility and external interference conditions. Although a lot of researches improved the trajectory planning method, its convergence is still a hot topic, since the algorithm complexity is high, and it is easy to fall into local optima. Hence this paper proposes an improved quantum particle swarm optimization algorithm for UAV trajectory planning to address the problem of premature convergence and local optima in trajectory planning. The simulation results show the feasibility of our improved quantum particle swarm optimization algorithm, which can reduce the number of iterations and improve the planning success rate.