Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore.
LionsBot International Pte. Ltd., #03-02, 11 Changi South Street 3, Singapore 486122, Singapore.
Sensors (Basel). 2021 Nov 1;21(21):7287. doi: 10.3390/s21217287.
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.
人工检查排水管道既费力又耗时，还容易发生事故。本工作提出了一种基于人工智能的机器人辅助远程排水检查和测绘框架，使用我们内部开发的可重构机器人 Raptor。四层 IoRT 充当用户和机器人之间的桥梁，通过它可以实现无缝的信息共享。使用迁移学习方案对 Faster RCNN ResNet50、Faster RCNN ResNet101 和 Faster RCNN Inception-ResNet-v2 深度学习框架进行了训练，并将其部署在 IoRT 框架远程缺陷检测任务中。通过使用 SLAM 技术进行各种实时排水检查现场试验，评估了经过训练的 CNN 算法和排水检查机器人 Raptor 的效率。实验结果表明，机器人的机动性稳定，在不同类型的排水管道中进行映射和定位也很准确。最后，为了有效地进行排水管道维护，通过融合激光雷达-SLAM 地图中的缺陷检测结果，生成基于 SLAM 的缺陷地图。
