基于蚁群算法和蝙蝠算法的多点路径规划

Multi-point path planning based on ant colony algorithm and bat algorithm

本文针对移动机器人多点路径规划问题,提出一种综合蚁群算法和蝙蝠算法的路径规划算法。利用蚁群算法建立节点之间的最短路径网络,在传统蚁群算法中引入了指向角和转向角作为启发信息,采用奖惩机制优化信息素更新方式,降低了路径的转折次数和转折角度,提高了算法的收敛速度。结合最短路径网络建立多点路径规划的目标函数,在求解最优节点访问顺序时,改进了蝙蝠算法结构,引入分层搜索方式和新的局部寻优机制,提高了蝙蝠算法的求解精度、速度和稳定性。通过仿真实验表明,本文所提出的算法能够有效解决多点路径规划问题,相比于现有算法,计算复杂度更低,搜索效率更高,整体路径更平滑,长度更短。

Aiming at the multi-point path planning problem of mobile robots,a path planning algorithm combining ant colony algorithm and bat algorithm is proposed in this paper.The ant colony algorithm is used to establish the shortest path network between nodes.The pointing angle and turning angle are introduced as heuristic information in the traditional ant colony algorithm to reduce the paths′turning times and turning angles.The reward and punishment mechanism is used to optimize the pheromone updating mode and improve the convergence speed of the algorithm.The objective function of multi-point path planning is based on the shortest path network.When solving the optimal node access order,the structure of the bat algorithm is improved,the hierarchical search method and a new local optimization mechanism are introduced,and the bat algorithm′s solving accuracy,speed,and stability are improved.The simulation results demonstrate that the proposed algorithm effectively addresses the issue of multi-point path planning.In comparison to existing algorithms,it exhibits lower computational complexity,higher search efficiency,smoother overall paths,and shorter lengths.