Control Method for Joint Climbing Trajectory of Wheeled Robots Based on Machine Learning
The potential energy and kinetic energy of wheeled robots are greatly affected by the slope environment during climbing,and the joint motion angle and joint force are difficult to control,which leads to deviation in trajectory control.In order to improve the joint trajectory control effect of wheeled robots,a joint trajectory control method of wheeled robots based on machine learning is proposed.It analyzes the potential energy and kinetic energy of the robot at different stages of slope movement,and establishes the dynamic equation of the robot,combines the robot with the intelligent automatic control system.The machine learning algo-rithm is designed to optimize the joint motion control strategy and achieve high-precision control of the joint trajectory during the climbing process of the wheeled robot.The experimental results show that the joint motion angle deviation is less than 2ºand the target force deviation is less than 2 N during the climbing process of the robot controlled by the proposed method,which im-proves the joint trajectory control effect.
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