Trajectory Planning of Industrial Robot Based on Improved Radial Basis Function Neural Network

With the more and more extensive use of robot technology in industrial production, the trajectory planning requirements of industrial robots are constantly increasing. In order to solve the problem that the trajectory is difficult to fit due to the nonlinearity in the continuous trajectory planning of robot, this paper presents a trajectory planning method of industrial robots which combines RBF (radial basis function) neural network and PSO (particle swarm optimization) algorithm. When RBF neural network has fewer hidden layer nodes, PSO algorithm is used to optimize the connection weight and threshold of RBF neural network. Through simulation analysis, the RBF neural network before and after improvement is compared. The results show that compared with the trajectory planning algorithm before improvement, the PSO-RBF neural network has smaller error and stronger adaptability, which meets the expected requirements of industrial robot trajectory planning.