A Research On Robot Motion Control Technology Based On Deep Neural Network

The calculation of robot forward kinematics and inverse kinematics models,the derivation of dynamics models,the calibration of robot structural parameters,error compensation,motion planning,perception and decision-making constitute the topics of robot application and research.For these topics,there are traditional and proven theory and method support.With the continuous development of robotics technology,all walks of life have higher and higher requirements for robots.Compared with traditional robots that do simple and repetitive tasks in closed spaces,more intelligent robots such as flexible robots,visual servo robots,collaborative robots,and service robots have appeared one after another,and effectively solve more complex problems.Intelligence is the future development trend of robots.Therefore,this article proposes a set of theoretical approach based on neural network and reinforcement learning for the calculation of the robot's forward and inverse kinematics model,structural parameter calibration,error compensation,perception and decision.Aiming at the inverse kinematics of serial robots and the forward kinematics of parallel robots,this paper uses neural networks to model the forward kinematics data of serial robots and the inverse kinematics data of parallel robots,so as to obtain two neural network to replace forward kinematics for parallel robot and inverse kinematics for serial robot,and the hidden dangers in it are discussed,the reasons are analyzed,and a solution based on reinforcement learning and neural network is given.In order to compensate for the robot's working error,this paper establishes a neural network model for the robot joint angle and joint compensation and evaluates the compensation effect.After compensation,robot accuracy has been improved.At the same time,the actual structure parameters of the robot are calibrated by the method based on reinforcement learning,and propose a calibration methods for serial or parallel robots respectively,and evaluate the accuracy of the calibrated robots.Finally,this article introduces target detection based on convolutional neural network and robot visual servo control,and successfully applies in crop management work.The theories and methods for each problem in this article have been theoretically analyzed and experimentally verified.The experimental results prove the effectiveness and practicability of these methods,and also prove that based on the traditional algorithms of robots and supplemented by intelligent algorithms can bring new breakthroughs to robotics.