Evaluation of a Smart Mobile Robotic System for Industrial Plant Inspection and Supervision

Georg K.J. Fischer, Max Bergau, D. Adriana Gómez-Rosal, Andreas Wachaja, Johannes Graeter, Matthias Odenweller, Uwe Piechottka,Fabian Höflinger,Nikhil Gosala, Niklas Wetzel,Daniel Büscher,Abhinav Valada,Wolfram Burgard

IEEE Sensors Journal(2024)

引用 0|浏览3
暂无评分
摘要
Automated and autonomous industrial inspection is a longstanding research field, driven by the necessity to enhance safety and efficiency within industrial settings. In addressing this need, we introduce an autonomously navigating robotic system designed for comprehensive plant inspection. This innovative system comprises a robotic platform equipped with a diverse array of sensors integrated to facilitate the detection of various process and infrastructure parameters. These sensors encompass optical (LiDAR, Stereo, UV/IR/RGB cameras), olfactory (electronic nose), and acoustic (microphone array) capabilities, enabling the identification of factors such as methane leaks, flow rates, and infrastructural anomalies. The proposed system underwent individual evaluation at a wastewater treatment site within a chemical plant, providing a practical and challenging environment for testing. The evaluation process encompassed key aspects such as object detection, 3D localization, and path planning, achieving an average precision of around 0.7662 in the 2D object detection. In addition, specific evaluations were conducted for optical methane leak detection and localization, demonstrating the system’s capability to detect leaks as small as 40 mL min -1 . Furthermore, acoustic assessments focusing on pump equipment and gas leak localization yielded an absolute localization error of around 50 cm.
更多
查看译文
关键词
Industry 4.0,distributed AI system,autonomous robots,chemical plant supervision,anomaly detection,object detection,acoustic signal processing,acoustic localization,gas detection,change detection
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要