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移动机器人路径规划是机器人学的一个重要研究领域,蚁群算法是一种模拟蚂蚁群体觅食行为的仿生优化算法。结合机器人路径规划的特点,将确定性选择和蚁群算法的随机性选择相结合进行节点转移,每次循环后只对较优蚂蚁路径进行信息素更新,提高了算法收敛的速度;在寻找路径过程中蚂蚁无后继转移节点时,采用蚂蚁回退策略,增强了算法在复杂障碍物环境中寻找路径的健壮性。仿真试验表明,该算法能在障碍物较复杂的情况下迅速规划出较优的全局路径。
Mobile robot path planning is an important research area of robotics. Ant colony algorithm is a kind of biomimetic optimization algorithm that simulates foraging behavior of ant population. Combined with the characteristics of robot path planning, the deterministic selection is combined with the stochastic choice of ant colony algorithm to transfer the nodes. Only the pheromone update is performed on the optimal ant path after each iteration, which improves the convergence speed of the algorithm. In the process of ants without subsequent node transfer, the ants rollback strategy is adopted to enhance the robustness of the algorithm in searching for paths in complex obstacle environments. Simulation results show that the proposed algorithm can rapidly plan a better global path under more complicated obstacles.