Research on displacement compensation method of inspection robot based on CS-BP neural network

The displacement error of intelligent patrol robot will continuously increase when it conducts patrol inspection along a fixed trajectory, thus deviating from the established inspection route. This paper presents a displacement compensation method based on CS-BP neural network. The Cuckoo search algorithm is used to optimize the weight and threshold of BP neural network in order to obtain the most excellent neural network structure. The experimental results show that the compensated displacement curve is closer to the actual displacement curve, and the displacement error is much smaller than the uncompensated theoretical displacement curve. At 58.8 s in the experiment, the deviation between the output displacement and the actual displacement after compensation by CS-BP neural network can be maintained at about 3 cm. The displacement compensation method is feasible and can effectively alleviate the problem that the motion path of the intelligent inspection robot deviates from the inspection route.