The Closed-Loop Control System Design of Hexapod Robot Autonomous Navigation Based on Fuzzy Neural Network

For the autonomous navigation issue of hexapod robot in unknown environment, an autonomous navigation closed-loop control algorithm based on fuzzy neural network is proposed, and a navigation control system of hexapod robot is designed according to the algorithm. The algorithm combines the logical reasoning ability of fuzzy control with the learning and training ability of neural network, and introduces the closed-loop control method into optimization. The control system is composed of four modules:information input, fuzzy neural network, instruction execution and information feedback. The perception of environment and location information is completed by GPS (global positioning system) sensor, electronic compass sensor and ultrasonic sensor. The fuzzy neural network control algorithm is reconstructed by C language and is applied to the system. The simulation results show that the closed-loop control algorithm based on fuzzy neural network is better than the open-loop control algorithm theoretically. The closed-loop control algorithm can reduce the walking distance of the hexapod in the environment with obstacles, and the walking speed is increased by 6.14%, the walking time is shortened by 8.74%. On this basis, a physical test is carried out. The experimental results show that the control system can achieve the autonomous obstacle-avoidance control of the hexapod robot. Compared with the open-loop control system, the walking path can be shortened effectively, the walking speed is increased by 5.66% and the walking time is shortened by 7.25%, which verifies the feasibility and practicability of the closed-loop control system.