Research On AGV Intelligent Obstacle Avoidance And Path Planning

With the progress of science and technology and the improvement of computer level,artificial intelligence technology develops rapidly.Automatic Guided Vehicle(AGV)has become an important transportation in the fields of intelligent factory,intelligent logistics,intelligent medical treatment and so on,and has been widely used in all walks of life.How to design the path planning and dynamic obstacle avoidance algorithm is a key scientific problem in the research of AGV system.Therefore,the research on the path planning and dynamic obstacle avoidance algorithm of AGV has an important theoretical basis and practical significance.By analyzing the application environment of AGV,this paper deeply studies the global path planning and local obstacle avoidance algorithm of AGV,and proposes an intelligent obstacle avoidance path planning algorithm based on improved ant colony algorithm and optimized dynamic window method,so as to improve the path search ability and obstacle avoidance ability of AGV in dynamic environment.The effectiveness of the algorithm is verified by the experiment of autonomous obstacle avoidance of robot movement controlled by ROS system.The main research contents are as follows1.Aiming at the problems of traditional ant colony algorithm in AGV path planning,such as easy to fall into local optimal solution and weak search ability,a path planning algorithm based on improved ant colony algorithm is proposed.Combined with the heuristic function of a * algorithm,the non-uniform initial pheromone matrix is established,the inflection point evaluation function is set,and the deadlock ant processing strategy is used to improve the ant colony algorithm,which improves the accuracy and speed of the search path of the ant colony algorithm.The effectiveness of the algorithm is verified by MATLAB simulation.2.Aiming at the uncertain dynamic obstacles in AGV application environment,an obstacle avoidance algorithm based on optimized dynamic window method is proposed.The evaluation function g of dynamic window method is improved by introducing the evaluation factors of target distance and the number of obstacles.Considering the possible conflict of multiple AGV paths,the priority strategy based on the traffic cost function is proposed,and the path conflict is solved by comparing the path traffic cost.3.Build the navigation obstacle avoidance experimental platform based on AGV and ROS,and verify the AGV dynamic obstacle avoidance path planning algorithm which combines the improved ant colony algorithm and the improved dynamic window method.The results show that the improved ant colony algorithm and the optimized dynamic window algorithm have better global path planning ability and real-time obstacle avoidance effect.