基于改进局部搜索算法的三维空间路径规划研究

3D Space Path Planning Research Based on Improved Local Search Algorithm

在机器人路径规划中,搜索区域维数增大会导致路径搜索算法收敛时间过长甚至不收敛的现象发生。针对此类问题,文中以改进的局部搜索算法为基础,融合蚁群算法中信息素因子和人工势场算法中势场因子,建立了启发函数模型以提高寻优的目的性,并对搜索到的路径用迭代法进行优化。文中具体讨论了三维空间中路径点的选取方式和启发函数模型的建立方法,同时给出了算法的详细流程。最后通过MATLAB仿真实验证明基于改进后的算法进行路径规划时,迭代次数降低,搜索速度变快,路径点轨迹趋势更加平稳。

In robot path planning, the increase of the search area dimension will cause the path search algorithm to not converge or converge for too long time. Aiming at this problem, heuristic function model was established to improve the purpose of optimization based on the improved local search algorithm, integrating the pheromone in ant colony algorithm and potential field factor in the artificial potential field algorithm. The iteration method was used for search path optimization. The selection of path points in three-dimensional space and the establishment of heuristic function model were discussed in detail, and the detailed flow of the algorithm was presented. Finally, through the MATLAB simulation experiment, the results showed when the path planning was carried out based on the improved algorithm, the number of iterations was decreased, the search speed was faster, and the path point trajectory trend was more stable.