Applying the stereo-vision detection technique to the development of underwater inspection task with PSO-based dynamic routing algorithm for autonomous underwater vehicles
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 re...
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| Vydané v: | Ocean engineering Ročník 139; s. 127 - 139 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.07.2017
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| ISSN: | 0029-8018, 1873-5258 |
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| 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.
•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 |
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| AbstractList | 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.
•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 |
| Author | Wang, Shun-Ming Huang, Lin-Chin Lin, Yu-Hsien Fang, Ming-Chung |
| Author_xml | – sequence: 1 givenname: Yu-Hsien surname: Lin fullname: Lin, Yu-Hsien email: vyhlin@mail.ncku.edu.tw organization: Dept. of Systems & Naval Mechatronic Eng., National Cheng-Kung University, Tainan City 70101, Taiwan – sequence: 2 givenname: Shun-Ming surname: Wang fullname: Wang, Shun-Ming organization: Dept. of Systems & Naval Mechatronic Eng., National Cheng-Kung University, Tainan City 70101, Taiwan – sequence: 3 givenname: Lin-Chin surname: Huang fullname: Huang, Lin-Chin organization: Dept. of Systems & Naval Mechatronic Eng., National Cheng-Kung University, Tainan City 70101, Taiwan – sequence: 4 givenname: Ming-Chung surname: Fang fullname: Fang, Ming-Chung organization: Dept. of Systems & Naval Mechatronic Eng., National Cheng-Kung University, Tainan City 70101, Taiwan |
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| Cites_doi | 10.1109/OCEANSSYD.2010.5603905 10.15607/RSS.2005.I.008 10.1007/s10596-009-9142-1 10.1016/j.robot.2014.10.007 10.1109/3477.956031 10.1007/BFb0040810 10.1016/j.neucom.2012.09.019 10.1016/0025-3227(84)90006-9 10.1007/s11370-013-0138-2 10.1109/JOE.2013.2278891 10.1007/s001380100065 10.1109/IROS.2010.5649246 10.1016/j.oceaneng.2014.11.001 10.1109/CYBER.2014.6917525 10.1109/OCEANSE.2005.1513260 |
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| References | Onwunalu, Durlofsky (bib13) 2010; 14 Paull, Saeedi, Seto, Li (bib15) 2014; 39 Zhang, Gong, Zhang (bib19) 2013; 103 Eustice, R., Singh, H., Leonard, J.J., Walter, M., Ballard, R., 2005. Visually navigating the RMS titanic with SLAM information filters. In: Proceedings of Robotics: Science and Systems (RSS). Saravanakumar, Asokan (bib16) 2013; 6 Armstrong, B., Wolbrecht, E., Edwards, D., 2010. AUV navigation in the presence of a magnetic disturbance with an extended Kalman filter, OCEANS 2010 IEEE-Sydney. IEEE, pp. 1–6. Belderson, Jones, Gorini, Kenyon (bib3) 1984; 56 Kermorgant, H.A., Scourzic, D., 2005. Interrelated functional topics concerning autonomy related issues in the context of autonomous inspection of underwater structures. Oceans 2005-Europe. IEEE, pp. 1370–1375. Belderson, Jones, Gorini, Kenyon (bib4) 1984; 56 Mallios, A., Ridao, P., Ribas, D., Maurelli, F., Petillot, Y., 2010. EKF-SLAM for AUV navigation under probabilistic sonar scan-matching. Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pp. 4404–4411. Fang, Wang, Wu, Lin (bib7) 2015; 93 Ortiz, Simó, Oliver (bib14) 2002; 13 Chen, H., Stavinoha, S., Walker, M., Zhang, B., Fuhlbrigge, T., 2014. Exploring robotic applications in offshore oil & gas industry. Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2014 IEEE 4th Annual International Conference on. IEEE, pp. 563–568. Ataei, Yousefi-Koma (bib2) 2015; 67 Foresti (bib8) 2001; 31 Kennedy (bib10) 2011 Griffiths (bib9) 1997 Zerr, Mailfert, Bertholom, Ayreault (bib18) 2005; 121 Shi, Y., Eberhart, R.C., 1998. Parameter selection in particle swarm optimization. International Conference on Evolutionary Programming. Springer, pp. 591–600. 10.1016/j.oceaneng.2017.04.051_bib17 Fang (10.1016/j.oceaneng.2017.04.051_bib7) 2015; 93 Ataei (10.1016/j.oceaneng.2017.04.051_bib2) 2015; 67 Saravanakumar (10.1016/j.oceaneng.2017.04.051_bib16) 2013; 6 Onwunalu (10.1016/j.oceaneng.2017.04.051_bib13) 2010; 14 Belderson (10.1016/j.oceaneng.2017.04.051_bib3) 1984; 56 Griffiths (10.1016/j.oceaneng.2017.04.051_bib9) 1997 Zhang (10.1016/j.oceaneng.2017.04.051_bib19) 2013; 103 Paull (10.1016/j.oceaneng.2017.04.051_bib15) 2014; 39 Zerr (10.1016/j.oceaneng.2017.04.051_bib18) 2005; 121 Kennedy (10.1016/j.oceaneng.2017.04.051_bib10) 2011 10.1016/j.oceaneng.2017.04.051_bib6 Foresti (10.1016/j.oceaneng.2017.04.051_bib8) 2001; 31 10.1016/j.oceaneng.2017.04.051_bib5 Ortiz (10.1016/j.oceaneng.2017.04.051_bib14) 2002; 13 10.1016/j.oceaneng.2017.04.051_bib1 10.1016/j.oceaneng.2017.04.051_bib11 Belderson (10.1016/j.oceaneng.2017.04.051_bib4) 1984; 56 10.1016/j.oceaneng.2017.04.051_bib12 |
| References_xml | – volume: 39 start-page: 131 year: 2014 end-page: 149 ident: bib15 article-title: AUV navigation and localization: a review publication-title: Ocean. Eng. IEEE J. Of – volume: 56 start-page: 65 year: 1984 end-page: 78 ident: bib4 article-title: A long-range side-scan sonar (Gloria) survey of the Romanche active transform in the Equatorial Atlantic publication-title: Mar. Geol. – volume: 56 start-page: 65 year: 1984 end-page: 78 ident: bib3 article-title: A long-range side-scan sonar (Gloria) survey of the Romanche active transform in the equatorial Atlantic publication-title: Mar. Geol. – reference: Eustice, R., Singh, H., Leonard, J.J., Walter, M., Ballard, R., 2005. Visually navigating the RMS titanic with SLAM information filters. In: Proceedings of Robotics: Science and Systems (RSS). – reference: Kermorgant, H.A., Scourzic, D., 2005. Interrelated functional topics concerning autonomy related issues in the context of autonomous inspection of underwater structures. Oceans 2005-Europe. IEEE, pp. 1370–1375. – reference: Armstrong, B., Wolbrecht, E., Edwards, D., 2010. AUV navigation in the presence of a magnetic disturbance with an extended Kalman filter, OCEANS 2010 IEEE-Sydney. IEEE, pp. 1–6. – volume: 14 start-page: 183 year: 2010 end-page: 198 ident: bib13 article-title: Application of a particle swarm optimization algorithm for determining optimum well location and type publication-title: Comput. Geosci. – volume: 31 start-page: 691 year: 2001 end-page: 705 ident: bib8 article-title: Visual inspection of sea bottom structures by an autonomous underwater vehicle publication-title: Syst. Man Cybern. Part B: Cybern. IEEE Trans. on – volume: 121 start-page: 124 year: 2005 end-page: 130 ident: bib18 article-title: Sidescan sonar image processing for AUV navigation publication-title: Eur. Oceans – reference: Mallios, A., Ridao, P., Ribas, D., Maurelli, F., Petillot, Y., 2010. EKF-SLAM for AUV navigation under probabilistic sonar scan-matching. Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pp. 4404–4411. – volume: 103 start-page: 172 year: 2013 end-page: 185 ident: bib19 article-title: Robot path planning in uncertain environment using multi-objective particle swarm optimization publication-title: Neurocomputing – volume: 67 start-page: 23 year: 2015 end-page: 32 ident: bib2 article-title: Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle publication-title: Robot. Auton. Syst. – volume: 93 start-page: 11 year: 2015 end-page: 24 ident: bib7 article-title: Applying the self-tuning fuzzy control with the image detection technique on the obstacle-avoidance for autonomous underwater vehicles publication-title: Ocean Eng. – volume: 6 start-page: 211 year: 2013 end-page: 224 ident: bib16 article-title: Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space publication-title: Intell. Serv. Robot. – volume: 13 start-page: 129 year: 2002 end-page: 140 ident: bib14 article-title: A vision system for an underwater cable tracker publication-title: Mach. Vision. Appl. – start-page: 24 year: 1997 end-page: 25 ident: bib9 article-title: Ocean science applications for autonomous underwater vehicles – the work plan For Autosub-1 for 1997–2000 and beyond publication-title: Unmanned Underw. Veh. Showc. – start-page: 760 year: 2011 end-page: 766 ident: bib10 article-title: Particle Swarm Optimization, Encyclopedia of Machine Learning – reference: Chen, H., Stavinoha, S., Walker, M., Zhang, B., Fuhlbrigge, T., 2014. Exploring robotic applications in offshore oil & gas industry. Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2014 IEEE 4th Annual International Conference on. IEEE, pp. 563–568. – reference: Shi, Y., Eberhart, R.C., 1998. Parameter selection in particle swarm optimization. International Conference on Evolutionary Programming. Springer, pp. 591–600. – ident: 10.1016/j.oceaneng.2017.04.051_bib1 doi: 10.1109/OCEANSSYD.2010.5603905 – ident: 10.1016/j.oceaneng.2017.04.051_bib6 doi: 10.15607/RSS.2005.I.008 – volume: 14 start-page: 183 issue: 1 year: 2010 ident: 10.1016/j.oceaneng.2017.04.051_bib13 article-title: Application of a particle swarm optimization algorithm for determining optimum well location and type publication-title: Comput. Geosci. doi: 10.1007/s10596-009-9142-1 – volume: 67 start-page: 23 year: 2015 ident: 10.1016/j.oceaneng.2017.04.051_bib2 article-title: Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle publication-title: Robot. Auton. Syst. doi: 10.1016/j.robot.2014.10.007 – volume: 31 start-page: 691 issue: 5 year: 2001 ident: 10.1016/j.oceaneng.2017.04.051_bib8 article-title: Visual inspection of sea bottom structures by an autonomous underwater vehicle publication-title: Syst. Man Cybern. Part B: Cybern. IEEE Trans. on doi: 10.1109/3477.956031 – start-page: 760 year: 2011 ident: 10.1016/j.oceaneng.2017.04.051_bib10 – ident: 10.1016/j.oceaneng.2017.04.051_bib17 doi: 10.1007/BFb0040810 – volume: 103 start-page: 172 year: 2013 ident: 10.1016/j.oceaneng.2017.04.051_bib19 article-title: Robot path planning in uncertain environment using multi-objective particle swarm optimization publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.09.019 – volume: 56 start-page: 65 issue: 1 year: 1984 ident: 10.1016/j.oceaneng.2017.04.051_bib4 article-title: A long-range side-scan sonar (Gloria) survey of the Romanche active transform in the Equatorial Atlantic publication-title: Mar. Geol. doi: 10.1016/0025-3227(84)90006-9 – volume: 6 start-page: 211 issue: 4 year: 2013 ident: 10.1016/j.oceaneng.2017.04.051_bib16 article-title: Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space publication-title: Intell. Serv. Robot. doi: 10.1007/s11370-013-0138-2 – start-page: 24 year: 1997 ident: 10.1016/j.oceaneng.2017.04.051_bib9 article-title: Ocean science applications for autonomous underwater vehicles – the work plan For Autosub-1 for 1997–2000 and beyond publication-title: Unmanned Underw. Veh. Showc. – volume: 39 start-page: 131 issue: 1 year: 2014 ident: 10.1016/j.oceaneng.2017.04.051_bib15 article-title: AUV navigation and localization: a review publication-title: Ocean. Eng. IEEE J. Of doi: 10.1109/JOE.2013.2278891 – volume: 121 start-page: 124 year: 2005 ident: 10.1016/j.oceaneng.2017.04.051_bib18 article-title: Sidescan sonar image processing for AUV navigation publication-title: Eur. Oceans – volume: 13 start-page: 129 issue: 3 year: 2002 ident: 10.1016/j.oceaneng.2017.04.051_bib14 article-title: A vision system for an underwater cable tracker publication-title: Mach. Vision. Appl. doi: 10.1007/s001380100065 – ident: 10.1016/j.oceaneng.2017.04.051_bib12 doi: 10.1109/IROS.2010.5649246 – volume: 93 start-page: 11 year: 2015 ident: 10.1016/j.oceaneng.2017.04.051_bib7 article-title: Applying the self-tuning fuzzy control with the image detection technique on the obstacle-avoidance for autonomous underwater vehicles publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2014.11.001 – volume: 56 start-page: 65 issue: 1–4 year: 1984 ident: 10.1016/j.oceaneng.2017.04.051_bib3 article-title: A long-range side-scan sonar (Gloria) survey of the Romanche active transform in the equatorial Atlantic publication-title: Mar. Geol. doi: 10.1016/0025-3227(84)90006-9 – ident: 10.1016/j.oceaneng.2017.04.051_bib5 doi: 10.1109/CYBER.2014.6917525 – ident: 10.1016/j.oceaneng.2017.04.051_bib11 doi: 10.1109/OCEANSE.2005.1513260 |
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| Title | Applying the stereo-vision detection technique to the development of underwater inspection task with PSO-based dynamic routing algorithm for autonomous underwater vehicles |
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