1. Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework
- Author
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Karthikeyan Elangovan, Lee Ming Jun Melivin, Povendhan Palanisamy, Balakrishnan Ramalingam, Dylan Ng Terntzer, Selvasundari Balakrishnan, Rajesh Elara Mohan, Archana Semwal, and Braulio Félix Gómez
- Subjects
Scheme (programming language) ,Computer science ,reconfigurable robot ,drain inspection ,TP1-1185 ,Biochemistry ,Bridge (nautical) ,Field (computer science) ,Article ,computer vision ,Analytical Chemistry ,Animals ,Humans ,Computer vision ,Electrical and Electronic Engineering ,mapping ,Instrumentation ,computer.programming_language ,Raptors ,business.industry ,Deep learning ,Chemical technology ,deep learning ,Robotics ,Atomic and Molecular Physics, and Optics ,Visual inspection ,Task (computing) ,defect inspection ,Robot ,Artificial intelligence ,business ,Transfer of learning ,computer ,Algorithms - 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