Research On Motion Planning Of Mobile Manipulator For Logistics Picking Tasks

With the development of e-commerce,logistics has been closely related to people's lives,and the development of logistics industry has become one of the important symbols to measure the degree of national modernization and comprehensive national strength.In the logistics distribution centers,goods picking is still mainly completed by manual operations,which is not only inefficient but also unstable.For example,many workers during the epidemic period have to be quarantined at home,which brings challenges for them to return to work and greatly reduces the logistics efficiency.In order to improve the picking efficiency by replacing manual labor with machines in the logistics scene,this study designs and builts a mobile manipulator picking system to replace human labor to complete the logistics picking task.The specific contents and research results are as follows:Firstly,the kinematics model of the mobile manipulator is constructed this study.Then,the kinematics of the six-axis UR manipulator is analyzed,and the D-H model is established.This model is used for forward kinematics and inverse kinematics analysis,laying a foundation for the subsequent algorithm design.To deal with the mobile manipulator singular state in the process of selection,this study proposes a base location calculation method to improve the picking efficiency based on the target coordinates,the current chassis center coordinates and the manipulator grabbing attitude,to adjust the position and attitude of the moving chassis as little as possible and thus enable the manipulator to avoid singular state when grabbing the object.The feasibility of the algorithm is verified by experiments.Aiming at the low efficiency of the traditional mobile manipulator planning algorithms without optimizing the working station coordinates according to the distribution of grasping targets,a method for optimizing the picking path of mobile manipulators based on improved genetic algorithm is proposed.By analyzing the position of items to be picked,the picking sequence is optimized and the optimal working position is searched based on the improved genetic algorithm.Then,a Traveling Salesman Problem(TSP)model for multiple working station position optimizing is established.An improved genetic algorithm adopting hierarchical evolution selection operator and the optimal nearest neighbor crossover operator is used to plan the shortest path of the mobile manipulator moving between multiple working stations,which improves the convergence speed while guaranteeing a globally optimal solution.The experimental results show that,compared with the traditional genetic algorithm,the convergence speed of the proposed method is increased by 46.15%,the path is shortened by 45.99%,and the system running time is reduced by 25.80%,improving the system efficiency.