Double BP Q-Learning Algorithm for Local Path Planning of Mobile Robot

Abstract

Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning process of mobile robot, this paper proposed a Double BP Q-learning algorithm based on the fusion of Double Q-learning algorithm and BP neural network. In order to solve the dimensional disaster problem, two BP neural network fitting value functions with the same network structure were used to replace the two Q value tables in Double Q-Learning algorithm to solve the problem that the Q value table cannot store excessive state information. By adding the mechanism of priority experience replay and using the parameter transfer to initialize the model parameters in different environments, it could accelerate the convergence rate of the algorithm, improve the learning efficiency and the generalization ability of the model. By designing specific action selection strategy in special environment, the deadlock state could be avoided and the mobile robot could reach the target point. Finally, the designed Double BP Q-learning algorithm was simulated and verified, and the probability of mobile robot reaching the target point in the parameter update process was compared with the Double Q-learning algorithm under the same condition of the planned path length. The results showed that the model trained by the improved Double BP Q-learning algorithm had a higher success rate in finding the optimal or sub-optimal path in the dense discrete environment, besides, it had stronger model generalization ability, fewer redundant sections, and could reach the target point without entering the deadlock zone in the special obstacles environment.

Path Planning of Industrial Wheeled Robots Based on Wireless Communication and Machine Learning Algorithms

“…e combined force of the repulsive force propels the robot to its destination. e neural network algorithm uses a specific neural network structure to represent the environment, and on this basis, the energy function is defined or the network output information is used for robot path planning [11][12][13][14][15].…”

Proximal policy optimization based dynamic path planning algorithm for mobile robots

“…Ref. [13] proposed a DBPQ algorithm based on the fusion of Double Q‐learning algorithm and back propagation neural network. Ref.…”