1. Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework
- Author
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Povendhan Palanisamy, Rajesh Elara Mohan, Archana Semwal, Lee Ming Jun Melivin, Braulio Félix Gómez, Selvasundari Balakrishnan, Karthikeyan Elangovan, Balakrishnan Ramalingam, and Dylan Ng Terntzer
- Subjects
reconfigurable robot ,defect inspection ,drain inspection ,deep learning ,computer vision ,mapping ,Chemical technology ,TP1-1185 - Abstract
Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The Faster RCNN ResNet50, Faster RCNN ResNet101, and Faster RCNN Inception-ResNet-v2 deep learning frameworks were trained using a transfer learning scheme with six typical concrete defect classes and deployed in an IoRT framework remote defect detection task. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trials using the SLAM technique. The experimental results indicate that robot’s maneuverability was stable, and its mapping and localization were also accurate in different drain types. Finally, for effective drain maintenance, the SLAM-based defect map was generated by fusing defect detection results in the lidar-SLAM map.
- Published
- 2021
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