无人机三维航迹规划的量子粒子群优化算法

Quantum Particle Swarm Optimization Algorithm of Three-Dimensional Path Planning of Unmanned Aerial Vehicle

针对传统的粒子群优化算法容易陷入局部最优解的问题,采用量子粒子群优化算法开展了无人机三维航迹规划。详细分析了固定翼无人机的飞行性能约束条件。为了减小算法计算复杂度,提高规划效率,对三维航迹规划问题的高度规划采用了直接设定策略,即,设置各个航路点的高度介于最大、最小飞行高度之间,从而将三维航路规划问题简化为二维航路规划问题。设计了收缩-扩张因子的线性增大调节策略、代价函数和航迹规划流程。分别采用量子粒子群优化算法和传统粒子群优化算法开展了无人机三维航迹规划仿真实验。仿真结果对比表明,所设计的量子粒子群优化算法比传统粒子群优化算法具有更高的全局搜索能力和搜索精度。

Aiming at the problem of easily falling into local optimal solution by conventional particle swarm optimization algorithm,three-dimensional path planning of unmanned aerial vehicle(UAV)is developed by applying the quantum particle swarm optimization algorithm(QPSO).The flight performance constraint conditions of UAV are analyzed in detail.In order to reduce the computing complexity of algorithm and increase the plan efficiency,the height plan of three-dimensional path planning problem is adopted by the direct setting scheme,namely,the height of every waypoint is set between the maximum and the minimum.Thus,three-dimensional path planning problem is simplified as two-dimensional path planning problem.The linear increase adjustment scheme of compression-expansion factor,cost function and path planning flowchart are designed.The simulation experiments of three dimensional path planning of UAV are implemented by adopting respectively the quantum particle swarm optimization algorithm and the conventional particle swarm optimization algorithm.The contrast of simulation result shows that the designed QPSO algorithm has higher global search ability and search precision than the conventional particle swarm optimization algorithm.