Research on robot path planning based on fuzzy neural network and particle swarm optimization

In a certain evaluation standard, robot path planning is to find a collision-free path from the initial state to the target state in an environment with obstacles, which is one of the key research directions of intelligent mobile robots. The mathematical model of the surrounding environment is established by using the grid method. The obstacle avoidance strategy of the fuzzy neural network is proposed. The function of the obstacle avoidance is realized by searching the next feasible node by the fuzzy neural network. Aiming at the parameter optimization problem of fuzzy neural network, the improved particle swarm optimization algorithm is used to optimize the parameters of fuzzy neural network, which avoids the instability of the system caused by improper parameter selection. Simulation results verify the effectiveness of the method. The simulation results show that the path planning of mobile robot based on fuzzy neural network and particle swarm optimization achieves performance index of the minimum sum of the obstacle cost and the route cost.