Collaborative inspection for large-scale urban sewer pipe networks by coupling multiple robotic pipe capsules and spatial optimization

Abstract

Urban sewer pipelines are prone to diverse faults, such as cracks, erosion, and root intrusion. Effective and efficient inspection methods are essential for large-scale urban sewer pipe networks. This paper presented a collaborative inspection approach to inspect urban sewer pipes, which integrates robotic pipe capsules (RPCs) with lightweight deep learning and spatial optimization. A bi-level network is built to represent diverse movements of workers and the RPCs and their collaboration. A specialized lightweight deep neural network is designed to identify faults with images captured by PRC in real time. The worker and RPC routes are spatially optimized with hybrid meta-heuristics. An experiment in Shenzhen, China, demonstrated that it achieves a balanced accuracy of 83.43% with 7.64 frames per second, which outperforms baseline methods. The presented method provides an alternative approach for large-scale urban sewer pipe networks.