High-efficiency inspecting method for mobile robots based on task planning for heat transfer tubes in a steam generator

Abstract

Many heat transfer tubes are distributed on the tube plates of a steam generator that requires periodic inspection by robots. Existing inspection robots are usually involved in issues: Robots with manipulators need complicated installation due to their fixed base; tube mobile robots suffer from low running efficiency because of their structural restricts. Since there are thousands of tubes to be checked, task planning is essential to guarantee the precise, orderly, and efficient inspection process. Most in-service robots check the task tubes using row-by-row and column-by-column planning. This leads to unnecessary inspections, resulting in a long shutdown and affecting the regular operation of a nuclear power plant. Therefore, this paper introduces the structure and control system of a dexterous robot and proposes a task planning method. This method proceeds into three steps: task allocation, base position search, and sequence planning. To allocate the task regions, this method calculates the tool work matrix and proposes a criterion to evaluate a sub-region. And then all tasks contained in the sub-region are considered globally to search the base positions. Lastly, we apply an improved ant colony algorithm for base sequence planning and determine the inspection orders according to the planned path. We validated the optimized algorithm by conducting task planning experiments using our robot on a tube sheet. The results show that the proposed method can accomplish full task coverage with few repetitive or redundant inspections and it increases the efficiency by 33.31% compared to the traditional planning algorithms.

Graphical abstract

Keywords

steam generator transfer tubes / mobile robot / dexterous structure / task planning / efficient inspection

References

[1] Tao M, Ke Z W, Li X L, Qu R T, Zhao Z X, Wu J, Li Y. The research of the model-free adaptive control method of once-through steam generator in nuclear power. In: Proceedings of the 2017 25th International Conference on Nuclear Engineering. Shanghai: ASME, 2017, V001T04A014

[2] Jeon S H , Hong S , Kwon H C , Hur D H . Characteristics of steam generator tube deposits in an operating pressurized water reactor. Journal of Nuclear Materials, 2018, 507: 371–380

[3] Huang Z J , Zou Y K , Ding J , Lu J F . Experimental investigation of heat transfer in coiled tube type molten salt steam generator. Applied Thermal Engineering, 2019, 148: 1131–1138

[4] Drexler A. Steam-water cycle chemistry relevant to nuclear steam generators. In: Riznic J, ed. Steam Generators for Nuclear Power Plants. Elsevier, 2017, 127–153

[5] Anoda Y, Tasaka K, Kumamaru H, Shiba M. Rosa-III System Description for Fuel Assembly, 4. Technical Report, JAERI-M 9363, 1981

[6] Xu B Y, Li G, Xing Z M, Cai H G, Zhao J, Fan J Z. Design and motion performance of new inspection robot for steam generator heat transfer tubes. In: Proceedings of 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR). Xining: IEEE, 2021, 662–667

[7] Wu J R. Research on key technology and prototype development of steam generator overhaul robot. Dissertation for the Doctoral Degree. Harbin: Harbin Engineering University, 2009 (in Chinese)

[8] Green S J , Hetsroni G . PWR steam generators. International Journal of Multiphase Flow, 1995, 21: 1–97

[9] Obrutsky L. Nuclear steam generator tube inspection tools. In: Riznic J, ed. Steam Generators for Nuclear Power Plants, Elsevier, 2017, 495–510

[10] Li J K , Wu X Y , Xu T T , Guo H W , Sun J Q , Gao Q S . A novel inspection robot for nuclear station steam generator secondary side with self-localization. Robotics and Biomimetics, 2017, 4(1): 26

[11] Sun B Z, Yang Y L. Numerically investigating the influence of tube support plates on thermal-hydraulic characteristics in a steam generator. Applied Thermal Engineering, 2013, 51(1–2): 611–622

[12] Ye C F , Huang Y , Udpa L , Udpa S S . Novel rotating current probe with GMR array sensors for steam generate tube inspection. IEEE Sensors Journal, 2016, 16(12): 4995–5002

[13] Gerkey B P , Matarić M J . A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research, 2004, 23(9): 939–954

[14] Elloumi W , El Abed H , Abraham A , Alimi A M . A comparative study of the improvement of performance using a PSO modified by ACO applied to TSP. Applied Soft Computing, 2014, 25: 234–241

[15] Le Ny J, Dahleh M, Feron E. Multi-agent task assignment in the bandit framework. In: Proceedings of the 45th IEEE Conference on Decision and Control. San Diego: IEEE, 2006, 5281–5286

[16] Gerkey B P, Mataric M J. A framework for studying multi-robot task allocation. In: Proceedings of Multi-Robot Systems: From Swarms to Intelligent Automata. Washington DC, 2003, 15–26

[17] Khaluf Y , Vanhee S , Simoens P . Local ant system for allocating robot swarms to time-constrained tasks. Journal of Computational Science, 2019, 31: 33–44

[18] Thakar S, Fang L W, Shah B, Gupta S. Towards time-optimal trajectory planning for pick-and-transport operation with a mobile manipulator. In: Proceedings of 2018 IEEE the 14th International Conference on Automation Science and Engineering (CASE). Munich: IEEE, 2018, 981–987

[19] Xu J R, Harada K, Wan W W, Ueshiba T, Domae Y. Planning an efficient and robust base sequence for a mobile manipulator performing multiple pick-and-place tasks. In: Proceedings of 2020 IEEE International Conference on Robotics and Automation (ICRA). Paris: IEEE, 2020, 11018–11024

[20] Vafadar S, Olabi A, Panahi M S. Optimal motion planning of mobile manipulators with minimum number of platform movements. In: Proceedings of 2018 IEEE International Conference on Industrial Technology (ICIT). Lyon: IEEE, 2018, 262–267

[21] Stern R, Sturtevant N, Felner A, Koenig S, Ma H, Walker T, Li J Y, Atzmon D, Cohen L, Kumar T K, Boyarski E, Bartak R. Multi-agent pathfinding: definitions, variants, and benchmarks. In: Proceedings of the 12th Annual Symposium on Combinatorial Search. 2019

[22] Yu J J , LaValle S M . Optimal multirobot path planning on graphs: complete algorithms and effective heuristics. IEEE Transactions on Robotics, 2016, 32(5): 1163–1177

[23] Xu B Y , Li G , Zhang K , Cai H G , Zhao J , Fan J Z . Motion planning of a steam generator mobile tube-inspection robot. Nuclear Engineering and Technology, 2022, 54(4): 1374–1381

[24] Cheikhrouhou O , Khoufi I . A comprehensive survey on the multiple traveling salesman problem: applications, approaches and taxonomy. Computer Science Review, 2021, 40: 100369

[25] Rokbani N , Kumar R , Abraham A , Alimi A M , Long H V , Priyadarshini I , Son L H . Bi-heuristic ant colony optimization-based approaches for traveling salesman problem. Soft Computing, 2021, 25(5): 3775–3794

[26] Cui Y , Zhong J B , Yang F R , Li S X , Li P H . Multi-subdomain grouping-based particle swarm optimization for the traveling salesman problem. IEEE Access, 2020, 8: 227497–227510

[27] Khan I , Maiti M K . A swap sequence based artificial bee colony algorithm for traveling salesman problem. Swarm and Evolutionary Computation, 2019, 44: 428–438

[28] Ebadinezhad S . DEACO: adopting dynamic evaporation strategy to enhance ACO algorithm for the traveling salesman problem. Engineering Applications of Artificial Intelligence, 2020, 92: 103649

[29] Yang K , You X M , Liu S , Pan H . A novel ant colony optimization based on game for traveling salesman problem. Applied Intelligence, 2020, 50(12): 4529–4542

[30] Kapanoglu M , Alikalfa M , Ozkan M , Yazıcı A , Parlaktuna O . A pattern-based genetic algorithm for multi-robot coverage path planning minimizing completion time. Journal of Intelligent Manufacturing, 2012, 23(4): 1035–1045

[31] Gabriely Y, Rimon E. Spanning-tree based coverage of continuous areas by a mobile robot. Annals of Mathematics and Artificial Intelligence, 2001, 31(1–4): 77–98

[32] Acar E U , Choset H , Rizzi A A , Atkar P N , Hull D . Morse decompositions for coverage tasks. The International Journal of Robotics Research, 2002, 21(4): 331–344

Nomenclature

Abbreviations
ABC	Artificial bee colony optimization
ACO	Ant colony optimization
D‒H	Denavit‒Hertenbeg
DoF	Degree-of-freedom
IK	Inverse kinematics
NPP	Nuclear power plant
PSO	Particle swarm optimization
SG	Steam generator
TSP	Traveling salesman problem
Variables
a	Rectangular region of width/length
BBest	Best base position for the current searching region
BCurrent	Current base position
Base(x, y)	Distance between the foot toes
C	Robot rotation speed
Cr	Turning cost
Cw	Translation cost
d	Distance between two tube holes
d(cur, next, t)	Heuristic cost function
f (wj, lj, k)	Minimum number of tasks to be completed of {\mathcal{V}}_j
F_{\text{D}}\left(\mathcal{B}\right)	Number of different elements in \mathcal{B}
Hole(x, y)	Distribution of the tube holes
k	Distance from the foot toe to the tool
\overline{l}	Length of the unassigned work row
l_j	Length of {\mathcal{V}}_j
l_{\text{max}}	Maximum length of the region according to the work matrix
l_{\text{s}}	Length of the search row
L_{\text{Ant}}\left(\mathcal{B}\right)	Total distance of the points in the set \mathcal{B}
L_{\text{Outer}}	Distribution of the base toes
L_{\text{OutertoTool}}	Distance between the tools
N\left(w\right)	Number of completed tasks
\mathrm{P}\mathrm{l}\mathrm{u}\mathrm{g}(x,y)	Distribution of the plugging holes
[\begin{array}{ccc}{q}_{\text{h}}^1& q_{\text{h}}^2& q_{\text{h}}^3\end{array}]	Robot joint configuration solution
R_m	Maximum size of the optimal region
R_{\text{p}}	Configuration matrix
R_{\text{p}}^{\prime }	Intermediate variable to obtain R_{\text{p}}^{\ast }
R_{\text{p}}^{\ast }	All matrices that minimize the number of base positions
S_{\text{max}}	Robot maximum translation distance
t	Number of turns
T_{\text{Grasp}}	Robot releasing time
T_i	Task tube hole
T_{\text{pb}}	Optimal work matrix
T_{\text{pl}}	Length work matrix
T_{\text{ps}}	Suboptimal work matrix
Task(x, y)	Distribution of the task holes
V_{\text{Rot}}	Robot translation speed
V_{\text{Trans}}	Robot grasping time
V\left(w\right)	Evaluation function of the main working direction
w_j	Width of {\mathcal{V}}_j
\left(x_{\text{h}},y_{\text{h}}\right)	Robot base position solution
\lfloor x\rfloor	Downward rounding function
\overline{\alpha }	Factor along the length direction
{\alpha }_{2k}	Factor along the width direction
{\alpha }_{2k-1},{\alpha }_{2k-2},...,{\alpha }_{k+1}	Factor of the compound direction
\mathcal{B}	Point set containing n base positions
\mathcal{T}	Task set
{\mathcal{V}}_j	Sub-region

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. U2013214) and the Self-Planned Task of the State Key Laboratory of Robotics and System (HIT), China (Grant No. SKLRS202001A03).

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