Research on optimization method of climbing ability of inspection robots based on torque allocation

ObjectiveAiming at the problems of easy slippage and insufficient environmental adaptability of high-voltage transmission line inspection robots during climbing, a real-time adaptive torque allocation method for front and rear walking wheel motors was proposed to improve the robot’s climbing ability and operational stability.MethodsFirstly, a static mechanical model of the robot’s uniform climbing was established to derive the mapping relation between the driving torque of front and rear wheels and the climbing angle. Secondly, a dual-motor driven torque allocation system matching the gravitational load was designed. Finally, a control system based on the fuzzy neural network proportional-integral-derivative (PID) algorithm was constructed to dynamically correct the driving torque of front and rear wheels, keeping the slip rate within 0.2.ResultsSimulation and test results show that the method increases the robot’s maximum climbing angle from 15° to 30°; compared with fuzzy PID and traditional PID control, the response speed of fuzzy neural network PID control is increased by 88.33% and 89.14% respectively, and the overshoot is reduced by 77.35% and 82.50% respectively; when the slope is 0°-30°, the slip rate of walking wheels is less than 0.2, which meets the requirements of inspection operations.