Design of Robot Path Planning Control Algorithm Based on Wavelet Neural Network

Abstract

At present, there are many optimization algorithms in the research of path planning for mobile robots, but all of them have certain limitations. How to make mobile robots make full use of existing constraints and choose the appropriate algorithm for path planning is an important research topic. In this paper, a robot path planning control algorithm based on WNN (Waveform Neural Network) is proposed. The algorithm describes the dynamic changing environment of the robot by describing the topographic map of the neural network, and can find out the optimal traveling route of the robot in real time only by describing the topographic map of the neural network, without any previous information of the dynamic environment. Its more unique feature is that although it is based on neural network algorithm, it does not need self-learning process. The simulation results show that the convergence speed of the improved algorithm is obviously faster than that of the original algorithm, and the shortest path is broken and collision-free. The simulation experiment shows that the overall path planning scheme is feasible and effective, and has certain practical value.