Research Of Robot Arm System With Imitation Learning Mechanism

Robot arm is the most extensively applied automated machinery in the field of technology. Therefore, the research on the acquisition of robot arm behaviors is an important part of robot movement skill studies. As imitation is a critical learning style for human beings and animals to achieve movement skills, the robot arm adopts imitation learning mechanism to acquire behaviors so as to simplify movement coding and improve learning efficiency. This paper focuses on the behavior capability acquisition by a robot arm through a method of imitation learning to carry out relevant studies. Main contents of it are as follows.Firstly, the design of a robot arm system with imitation learning mechanism and the acquisition of teaching behavior information. Based on imitation learning system process framework of robots and existing problems of present robot arms, a hands-on teaching robot arm system is designed with a mechanism of imitation learning;furthermore, according to the hands-on teaching characteristics, an electric power supply plan is also made for offline teaching of this system to complete type selection and debugging of hardware module. In an environment of ADAMS, a 3-D scale simulation model is established for the robot arm. Then, by dragging the simulation function to imitate the hands-on teaching process, collection of teaching behavior information is fulfilled. In addition, such information is processed as well specific to a kinematic model of the robot arm with an aim to acquire its joint angular motion information.Secondly, the imitation learning control strategy design and implementation for robot arm based on the modified BP neural network. Specific to problems such as complex control method programming and low learning efficiency of the traditional robot arm, a modified BP neural network is adopted to express the imitation learning strategy. Moreover, not only is the construction and parameter design of network fulfilled based on the teaching behavior information, but a network weight optimization approach based on genetic algorithm is also presented direct at initial network weight value selection of the BP neural network, with an aim to complete network training and hence acquire the behavioral imitation strategy for robot arm.On the basis of ADAMS and MATLAB, a 3-D joint simulation system of the robot arm is established to complete the 3-D movement simulation experimental research for it and implement imitations of its behavioral movements.Thirdly, both the study on complex behavior imitation of robot arm based on RBF neural network and the acquisition of optimal teaching trajectory based on Gaussian mixture model are carried out. Specific to the acquisition of robot arm’s complex behavior movements and the diversity of hands-on teaching trajectory, theRBF neural network is employed to express the corresponding imitation learning strategy; while GMM and GMR are also used to optimize the multiple teaching trajectory to achieve the optimal teaching trajectory of behavioral movements.According to the optimal teaching trajectory and the complex robot arm behavior imitation learning strategy acquisition of RBF neural network, complex behavioral movement imitations of the robot arm are realized through a simulation experiment study.Research accomplishments of this paper have a positive significance in improving both the learning efficiency and the learning precision of a robot arm system; and it also have certain application values in terms of the behavioral movement acquisition of this system.