复杂环境下规划无人机三维航迹时,随机型的粒子群优化由于问题维度高导致收敛性差难以获得最优甚至可行航迹;而确定型的稀疏A*算法易陷入局部搜索导致搜索时间长且计算量大。基于分层思想,将高维航迹规划问题转换为多个低维问题。首先通过粒子群优化规划出少量导引航迹点集,然后采用稀疏A*算法计算导引点间的航迹段。仿真结果表明该方法能在获得满意解的前提下提高复杂环境下无人机航迹规划效率。

For three-dimensional UAV route planning in complex environment, the probabilistic particle swarm optimization (PSO) has difficulty in acquiring optimal path or even feasible path due to high dimensionality, while the deterministic sparse A algorithm is likely to be trapped into local search which leads to long search time and expensive computation. Based on hierarchical path planning, the high dimensional path planning could be decomposed to some low dimensional problems. Firstly, some navigation waypoints was planned using PSO, and then the path linking the navigation points was planned using sparse A algorithm. The simulation results indicate the proposed method could improve feasible path planning efficiency in complex environment.