改进蚁群与动态Q学习融合的机器人路径规划

Robotic Path Planning Integrating Improved Ant Colony Optimization and Dynamic Q-learning

基本Q学习算法应用于路径规划时,动作选择的随机性导致算法前期搜索效率较低,规划耗时长,甚至不能找到完整的可行路径,故提出一种改进蚁群与动态Q学习融合的机器人路径规划算法.利用精英蚂蚁模型和排序蚂蚁模型的信息素增量机制,设计了一种新的信息素增量更新方法,以提高机器人的探索效率;利用改进蚁群算法的信息素矩阵为Q表赋值,以减少机器人初期的无效探索;设计了一种动态选择策略,同时提高收敛速度和算法稳定性.在不同障碍物等级的二维静态栅格地图下进行的仿真结果表明,所提方法能够有效减少寻优过程中的迭代次数与寻优耗时.

When the basic Q-learning algorithm is applied to path planning,the randomness of action selection makes the early search efficiency of the algorithm low and the planning time-consuming,and even a complete and feasible path cannot be found.Therefore,a path planning algorithm of robots based on improved ant colony optimization(ACO)and dynamic Q-learning fusion is proposed.The pheromone increment mechanism of the elite ant model and sorting ant model is used,and a new pheromone increment updating method is designed to improve the exploration efficiency of robots.The pheromone matrix of the improved ant colony optimization algorithm is used to assign values to the Q table,so as to reduce the ineffective exploration of the robot at the initial stage.In addition,a dynamic selection strategy is designed to improve the convergence speed and the stability of the algorithm.Finally,different simulation experiments are carried out on two-dimensional static grid maps with different obstacle levels.The results show that the proposed method can effectively reduce the number of iterations and optimization time consumption in the optimization process.