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http://dx.doi.org/10.1016/j.net.2021.10.005

Motion planning of a steam generator mobile tube-inspection robot  

Xu, Biying (State Key Laboratory of Robotics and System, Harbin Institute of Technology)
Li, Ge (State Key Laboratory of Robotics and System, Harbin Institute of Technology)
Zhang, Kuan (State Key Laboratory of Robotics and System, Harbin Institute of Technology)
Cai, Hegao (State Key Laboratory of Robotics and System, Harbin Institute of Technology)
Zhao, Jie (State Key Laboratory of Robotics and System, Harbin Institute of Technology)
Fan, Jizhuang (State Key Laboratory of Robotics and System, Harbin Institute of Technology)
Publication Information
Nuclear Engineering and Technology / v.54, no.4, 2022 , pp. 1374-1381 More about this Journal
Abstract
Under the influence of nuclear radiation, the reliability of steam generators (SGs) is an important factor in the efficiency and safety of nuclear power plant (NPP) reactors. Motion planning that remotely manipulates an SG mobile tube-inspection robot to inspect SG heat transfer tubes is the mainstream trend of NPP robot development. To achieve motion planning, conditional traversal is usually used for base position optimization, and then the A* algorithm is used for path planning. However, the proposed approach requires considerable processing time and has a single expansion during path planning and plan paths with many turns, which decreases the working speed of the robot. Therefore, to reduce the calculation time and improve the efficiency of motion planning, modifications such as the matrix method, improved parent node, turning cost, and improved expanded node were proposed in this study. We also present a comprehensive evaluation index to evaluate the performance of the improved algorithm. We validated the efficiency of the proposed method by planning on a tube sheet with square-type tube arrays and experimenting with Model SG.
Keywords
Steam generator mobile tube-inspection robot; Motion planning; Base position; Optimization; Path planning; Comprehensive evaluation index;
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1 A.K. Guruji, H. Agarwal, D.K. Parsediya, Time-efficient A* algorithm for robot path planning, Procedia Technology 23 (2016) 144-149, https://doi.org/10.1016/j.protcy.2016.03.010.   DOI
2 W. Xin, L. Wanlin, F. Chao, H. Likai, Path planning research based on an improved A* algorithmfor mobile robot, IOP Conf. Ser. Mater. Sci. Eng. 569 (5) (2019), https://doi.org/10.1088/1757-899X/569/5/052044.   DOI
3 B. Sun, Y. Yang, Numerically investigating the influence of tube support plates on thermal-hydraulic characteristics in a steam generator, Appl. Therm. Eng. 51 (1-2) (2013) 611-622, https://doi.org/10.1016/j.applthermaleng.2012.10.009.   DOI
4 V. Uchanin, V. Najda, E. Hristoforou, The development of eddy current technique for WWER steam generators inspection, in: Steam Generator Systems: Operational Reliability and Efficiency, 2011. Vienna.
5 M. Zikky, Review of A* (A star) navigation mesh pathfinding as the alternative of artificial intelligent for ghosts agent on the pacman game, EMITTER International Journal of Engineering Technology 4 (1) (2016) 141-149, https://doi.org/10.24003/emitter.v4i1.117.   DOI
6 S. Karaman, M.R. Walter, A. Perez, E. Frazzoli, S. Teller, Anytime motion planning using the RRT, IEEE Int. Conf. Robot. Autom. (2011) 1478-1483, https://doi.org/10.1109/ICRA.2011.5980479.   DOI
7 S. Yamamoto, January). Development of inspection robot for nuclear power plant, in: Proceedings 1992 IEEE International Conference on Robotics and Automation, IEEE Computer Society, 1992, pp. 1559-1560.
8 S.M. Persson, I. Sharf, Sampling-based A* algorithm for robot path-planning, Int. J. Robot Res. 33 (13) (2014) 1683-1708, https://doi.org/10.1177/02783649 14547786.   DOI
9 R.S. Parpinelli, H.S. Lopes, A.A. Freitas, Data mining with an ant colony optimization algorithm, IEEE Trans. Evol. Comput. 6 (4) (2002) 321-332, https://doi.org/10.1109/TEVC.2002.802452.   DOI
10 N.I. Kolev, N.I. Kolev, Multiphase Flow Dynamics, vol. 1, Springer, New York, Heidelberg, Berlin, 2005.
11 J. Peng, Y. Huang, G. Luo, Robot path planning based on improved A* algorithm, Cybern. Inf. Technol. 15 (2) (2015) 171-180, https://doi.org/10.1515/cait-2015-0036.   DOI
12 J. Yao, C. Lin, X. Xie, A.J.A. Wang, C.C. Hung, Path planning for virtual human motion using improved A* algorithm. ITNG2010, in: 7th International Conference on Information Technology: New Generations, 2010, pp. 1154-1158, https://doi.org/10.1109/ITNG.2010.53.   DOI
13 F. Duchon, A. Babinec, M. Kajan, P. Beno, M. Florek, T. Fico, L. Jurisica, Path planning with modified A star algorithm for a mobile robot, Procedia Engineering 96 (2014) 59-69, https://doi.org/10.1016/j.proeng.2014.12.098.   DOI
14 J. Carsten, D. Ferguson, A. Stentz, 3D field D*: improved path planning and replanning in three dimensions, IEEE International Conference on Intelligent Robots and Systems (2006) 3381-3386, https://doi.org/10.1109/IROS.2006.282516.   DOI
15 C. Gosselin, Global planning of dynamically feasible trajectories for three-DOF spatial cable-suspended parallel robots, in: Cable-driven Parallel Robots, Springer, Berlin, Heidelberg, 2013, pp. 3-22.
16 Jovica Riznic (Ed.), Steam Generators for Nuclear Power Plants, Woodhead Publishing, 2017.
17 S.J. Green, G. Hetsroni, PWR steam generators, Int. J. Multiphas. Flow 21 (1995) 1-97.
18 D. Harabor, A. Grastien, Improving jump point search, Proceedings International Conference on Automated Planning and Scheduling, ICAPS, January (2014) 128-135.
19 J. Li, X. Wu, T. Xu, H. Guo, J. Sun, Q. Gao, A novel inspection robot for nuclear station steam generator secondary side with self-localization, Robotics and Biomimetics 4 (1) (2017), https://doi.org/10.1186/s40638-017-0078-y, 0-8.   DOI
20 L. Obrutsky, Nuclear steam generator tube inspection tools, in: Steam Generators for Nuclear Power Plants, Elsevier Ltd, 2017, https://doi.org/10.1016/B978-0-08-100894-2.00018-2.   DOI
21 N. Buniyamin, W.W. Ngah, W. a J. Wan Ngah, N. Sariff, Z. Mohamad, A simple local path planning algorithm for autonomous mobile robots, Int. J. Sys. Appl. Eng. Dev. 5 (2) (2011) 151-159.
22 M.G. Park, J.H. Jeon, M.C. Lee, Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing, in: IEEE International Symposium on Industrial Electronics Proceedings, June, 2001.
23 L. Scrucca, GA: a package for genetic algorithms in R, J. Stat. Software 53 (4) (2013) 1-37, https://doi.org/10.18637/jss.v053.i04.   DOI
24 M.M. Ali, P. Kaelo, Improved particle swarm algorithms for global optimization, Appl. Math. Comput. 196 (2) (2008) 578-593, https://doi.org/10.1016/j.amc.2007.06.0 20.   DOI