Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework

Sensors

卷:21

Rajesh Elara Mohan¹  Karthikeyan Elangovan¹  Selvasundari Balakrishnan¹  Archana Semwal¹  Braulio Félix Gómez¹  Povendhan Palanisamy¹  Lee Ming Jun Melivin¹  Balakrishnan Ramalingam¹  Dylan Ng Terntzer² 

[1] Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore;

[2] LionsBot International Pte. Ltd., 03-02, 11 Changi South Street 3, Singapore 486122, Singapore;

关键词: reconfigurable robot;    defect inspection;    drain inspection;    deep learning;    computer vision;    mapping;   

DOI  :  10.3390/s21217287

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown