UAV Path Planning Method based on Quantum Squirrel Search Algorithm

To solve the path planning problem of UAV in complex terrain environments to avoid various threats and constraints. The Quantum Squirrel Search Algorithm (QSSA) is proposed to solve the premature convergence and search stagnation phenomena of Squirrel Search Algorithms (SSA) in three-dimensional path planning. Firstly, a three-dimensional trajectory planning model for unmanned aerial vehicles is established based on their objective functions and constraints. Secondly, aiming at the premature phenomenon of squirrel foraging algorithms in dealing with high-dimensional band constraints, this paper designs quantum rotation gates and quantum rotation angles to improve the mining ability of the algorithm. Finally, the quantum squirrel foraging algorithm is applied to the path planning problem. The experimental results show that QSSA outperforms Whale optimization algorithm (WOA), Grey Wolf optimization (GWO) and Squirrel Search Algorithm (SSA) in terms of its optimization performance. It can quickly obtain a safe and feasible path compared to other algorithms.