针对传统蚁群算法在移动机器人路径规划中存在搜索盲目性、收敛速度慢及路径转折点多等问题,提出了一种基于改进蚁群算法的移动机器人路径规划算法。首先,利用跳点搜索(Jump Point Search,JPS)算法不均匀分配初始信息素,降低蚁群前期盲目搜索的概率;然后,引入切比雪夫距离加权因子和转弯代价改进启发函数,提高算法的收敛速度、全局路径寻优能力和搜索路径的平滑程度;最后,提出一种新的信息素更新策略,引入自适应奖惩因子,自适应调整迭代前、后期的信息素奖惩因子,保证了算法全局最优收敛。实验仿真结果表明,在不同地图环境下,与现有文献结果对比,该算法可以有效地缩短路径搜索的迭代次数和最优路径长度,并提高路径的平滑程度。
Path planning for mobile robots based on improved ant colony algorithm
In order to address the drawbacks of traditional ant colony algorithm in mobile robot path planning,such as blind search,slow convergence speed,multiple path turning points,it proposes a mobile robot path planning algorithm based on improved ant colony algorithm.Firstly,the Jump Point Search(JPS)algorithm is utilized to unevenly distribute initial phero-mones,reducing the likelihood of blind search during the early stages of the ant colony.Then,a Chebyshev distance weighting fac-tor and turning cost are introduced to improve the heuristic function,enhancing the algorithm's convergence speed,global path op-timization capability,and smoothness of the search path.Finally,a novel pheromone update strategy is proposed that introduces an adaptive reward-punishment factor to adaptively adjust the pheromone reward-punishment factor during pre-and post-iteration pha-ses,ensuring the algorithm's global optimal convergence.Experimental simulation results demonstrate that,in various map environ-ments and compared to existing literature results,the proposed algorithm effectively reduces the number of iterations and optimal path length required for path search while increasing path smoothness.
ant colony algorithmpath planningJump Point Search(JPS)algorithmmobile robotpheromone heuristic