Q-learning Path Optimization Method Based on Beetle Antennae Search Algorithm
In order to improve the path planning results of mobile robots and solve the problems of many corners and large cumulative turning angles in the path planned by the improved Q-learning algorithm,a Q-learning path op-timization method(IBQL)based on beetle antennae search algorithm is proposed.The IBQL algorithm mainly improves the beetle antennae search algorithm in three aspects.First,the step-decreasing strategy is introduced to im-prove the efficiency of beetle antennae search.Second,it adds obstacle collision detection and improves the fitness function to ensure the safety of the path.Third,the principle of motion optimization is used to update points on the path to reduce the blindness of position updates.Simulation results show that the path obtained by the IBQL algorithm is excellent and using the improved beetle antennae search algorithm to optimize the path has little impact on the o-verall real-time performance.The optimized path has advantages such as fewer turning points and good smoothness.
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维普
