Abstract
Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.
Highlights
IntroductionImproving the trajectory planning of robot manipulators is one of the core focuses of robot research, and has great research prospects [1]
With the advancements in automation and robot technology, robots have begun to be widely used in the industrial, agricultural, and medical fields, among many others.Improving the trajectory planning of robot manipulators is one of the core focuses of robot research, and has great research prospects [1]
This indicates that the adaptive robust controller based on the radial basis function (RBF) neural network designed in this study achieved good results in realizing the trajectory tracking of the 6-DOF manipulator, has extremely high stability, and improves the trajectory tracking performance of the manipulator
Summary
Improving the trajectory planning of robot manipulators is one of the core focuses of robot research, and has great research prospects [1]. Precise robot manipulator trajectories can improve the efficiency of a robot’s various tasks, such as workshop operations, crop picking, medical surgery and so on. Manipulator trajectory planning should consider obstacle avoidance, trajectory accuracy, smooth operation, energy consumption, among other factors, and needs to consider the problems of external interference, communication delay, and the nonlinearity and uncertainty of robot manipulators [2,3,4,5]. In order to solve these problems, many researchers have studied the kinematics formula, dynamic model, and control technology of robot manipulators. Research into the kinematics formula and dynamic model of robot manipulators has been gradually growing.
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