Abstract
In order to improve the robot path planning speed and accuracy, this paper combined cloud theory and rough sets in path planning. First initial path group can be gained by using rough set training, then a series of feasible path group can drawn by the minimum decision rule training, finally the population based on cloud model optimization can also gained and further the best route can be acquired. The simulation results verified the effectiveness of the proposed algorithm.
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