Path Planning Algorithm and Simulation for Mobile Robot

摘要

Research the global path optimization of mobile robot.In order to overcome the shortcoming and improve the speed and accuracy of the traditional evolution algorithm,the paper combined cloud theory with rough set in path planning of mobile robot.In simulation experiment,the environment was described by grid method and random produced initial path group.First,rough set was used for training initial path group,a series of feasible paths were calculated by the minimum decision rule training,and the path population was optimized by cloud model.Finally,the best path to walk was acquired.The simulation results verify that when the initial group is large,the convergence speed and search quality can be improved by the algorithm combined with the evolutionary algorithm.