Applying the stereo-vision detection technique to the development of underwater inspection task with PSO-based dynamic routing algorithm for autonomous underwater vehicles. (15th July 2017)

• Record Type: Journal Article
• Authors: Lin, Yu-Hsien; Wang, Shun-Ming; Huang, Lin-Chin; Fang, Ming-Chung
• Is Part Of: Ocean engineering. Volume 139(2017)
• Page Start: 127
• Page End: 139
• Publication Date: 2017-07-15
• Subjects: Stereo vision -- Underwater inspection -- Dynamic routing algorithm -- Fuzzy control -- PSO -- AUV; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2017.04.051
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: This research is an extension of image detection technique for obstacle-avoidance of autonomous underwater vehicles (AUVs) by applying the BK triangle sub-product of fuzzy relations. According to the concept of stereo-vision detection technique, the obstacles as well as offshore structures can be reconstructed by depth images. By obtaining the depth information in the space, the optimal route can be evaluated combining PSO (Particle Swarm Optimization)-based dynamic routing algorithm. In this study, the graphical language, LabVIEW (Laboratory Virtual Instrument Engineering Workbench), is used to simulate the AUV's inspection task in the offshore wind farm. The interface shows the pose, trajectories, perspectives and real-time series of 6-Degrees of Freedom (DOF) motion for the AUV. In the existence of obstacles, the AUV is found to conduct inspection tasks of the offshore wind farm with feasible routes by considering minimum time and energy consumption successfully. In summary, the stereo-vision detection technique with PSO-based dynamic routing algorithm is not only beneficial to optimize feasible routes but also identify features of objects for the purpose tracking and obstacle-avoidance more efficiently. Highlights: The image detection technique with PSO-based dynamic routing algorithm has been developed The relationship of the two Pareto-optimal sets is non-dominant under different velocity conditions Each solution of the minimum time route takes less time than that of the minimum energy consumption routes Each energy optimization solution expends a lower amount of energy than the time optimization solutions The difference of these two sets is high under low-velocity conditions