Path Planning Algorithm For Automated Guided Vehicles

With the rapid development of artificial intelligence,information image processing technology,computer integrated manufacturing technology and sensor fusion technology,the application scope of Automated Guided Vehicle(AGV)in flexible manufacturing systems and intelligent storage systems has gradually expanded.AGV is a kind of wheeled mobile robot,it is a kind of automatic intelligent handling equipment with high safety and high flexibility.This article summarizes and analyzes the research status of AGV and its path planning at home and abroad,and studies the path planning algorithm of AGV in three different working space environments.The specific content is described as follows.Firstly,in terms of path planning algorithms and environment modeling,the concept and classification methods of path planning are studied,and several common environment modeling methods and the working principles of path planning algorithms are explained,and their respective advantages and disadvantages are compared.Secondly,in terms of AGV global path planning,a two-way search A* algorithm based on dynamic parameter adjustment is studied.Compared with the simulation experiment of the classic A* algorithm,the effectiveness and feasibility of the two-way search A* algorithm based on dynamic parameter adjustment is verified.Thirdly,in the aspect of AGV local path planning,an ant colony algorithm based on potential field guidance is studied.The artificial potential field algorithm’s potential situation force information of the virtual potential field is combined with the heuristic information of the ant colony algorithm to solve the premature and slow convergence speed of the ant colony algorithm in the search path process,and the artificial potential field is used The algorithm dynamically avoids sudden obstacles encountered during driving.Through the comparison of simulation experiments,the effectiveness and feasibility of the ant colony algorithm guided by the potential field are verified.Finally,in the aspect of AGV path planning with unknown obstacle information in the workspace,a path search method based on reinforcement learning algorithm is studied.By introducing the BP neural network algorithm into the Q-learning algorithm,the AGV can find a collision-free optimal path by itself without prior knowledge.The simulation experiment verifies the feasibility and effectiveness of the reinforcement learning algorithm based on BP neural network in the AGV path planning problem.