Motion Planning Method for In-Pipe Walking Robots Using Height Maps and CNN-Based Pipe Branches Detector

Abstract

In this chapter, the problem of motion planning for an in-pipe walking robot is studied. One of the key parts of motion planning for a walking robot is a step sequence generation. In the case of in-pipe walking robots it requires choosing a series of feasible contact locations for each of the robot's legs, avoiding regions on the inner surface of the pipe where the robot cannot step to, such as pipe branches. The chapter provides an approach to localization of pipe branches, based on deep convolutional neural networks. This allows including the information about the branches into the so-called height map of the pipeline and plan the step sequences accordingly. The chapter shows that it is possible to achieve prediction accuracy better than 0.5 mm for a network trained on a simulation-based dataset.