Science Technology and Engineering
1671-1815
2023
23
30
13187
13194
10.12404/j.issn.1671-1815.2023.23.30.13187
article
基于改进粒子群算法的城市物流无人机路径规划
Route Planning of Urban Logistics UAV Based on Improved Particle Swarm Optimization Algorithm
城市物流无人机路径规划是无人机任务规划系统的一项核心内容。为安全、高效实现物流无人机路径规划问题，首先，采用栅格法进行环境建模，考虑无人机性能限制，以路径长度最短、无人机高度变化以及栅格危险度最小为目标，建立多约束物流无人机路径规划模型。其次，针对传统粒子群算法存在的问题，引入Singer映射改进粒子初始分布、线性调整加速因子和最大速度，粒子位置新更新策略，及动态调整惯性权值，应用改进的粒子群优化算法求解模型。最后，进行了算例仿真分析。当栅格粒度取5米，路径节点取5个，代价函数权值分别取0.1、0.4和0.5时，与其他4种算法相比，本文算法总代价值最佳，分别减少44.5%、3.5%、42.8%和30%。结果表明，本文的模型与算法用于无人机路径规划是可行的和有效的。
Urban logistics UAV path planning is a core content of UAV mission planning system. In order to realize the path planning problem of logistics UAVs safely and efficiently, firstly, the environment modeling was carried out by using grid method. Considering the performance limitations of UAVs, the path planning model of multi-constraint logistics UAVs was established by taking the shortest path length, the height variation of UAVs and the minimum grid risk as targets. Secondly, in view of the problems existing in the traditional particle swarm optimization algorithm, Singer mapping was introduced to improve the initial particle distribution, linear adjustment of acceleration coefficients and maximum velocity, new updating strategy of particle position, and dynamic adjustment of inertia weight, and the improved particle swarm optimization algorithm was applied to solve the model. Finally, an example is given for simulation analysis. When the grid size is 5 meters, the path nodes are 5 and the cost function weights are 0.1, 0.4 and 0.5 respectively, compared with the other four algorithms, the total generation value of the proposed algorithm is the best, which is reduced by 44.5%, 3.5%, 42.8% and 30%, respectively. The results show that the model and algorithm in this paper are feasible and effective for UAV path planning.
三维路径规划；物流无人机；栅格危险度；改进粒子群优化算法
3D path planning;Logistics UAV; Grid risk; Improved particle swarm optimization algorithm
王飞,杨清平
Wang Fei, Yang Qingping
jsygc/article/abstract/2301016