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Three-dimensional truss path planning of cellular robots based on improved sparrow algorithm

Published online by Cambridge University Press:  10 November 2023

Ye Dai Open the ORCID record for Ye Dai [Opens in a new window] ,
Shikun Li ,
Xinda Chen ,
Xinlei Nie ,
Xukun Rui  and
Qihao Zhang
Ye Dai*
Affiliation:
Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China
Shikun Li
Affiliation:
Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China
Xinda Chen
Affiliation:
Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China
Xinlei Nie
Affiliation:
Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China
Xukun Rui
Affiliation:
Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China
Qihao Zhang
Affiliation:
Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China
*
Corresponding author: Ye Dai; Email: daiye312@163.com
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Abstract

As space missions are needed in the future, the assembly volume of the space truss will become larger and larger, and the advancing path of the on-orbit cellular robot to the mission target will become more and more complicated. If the shortest moving path cannot be found in the truss environment, the climbing time of the robot on the truss will be greatly increased. To improve the speed of the cellular robot moving to the target point on the large space truss, this paper designs a cellular robot structure and configuration suitable for climbing on the truss and uses the improved sparrow algorithm to solve the problem of robot motion path planning. By establishing a mathematical model of the space truss, the improved sparrow algorithm is used to find the shortest path between the starting point and the end point in the truss environment. Finally, the data of this algorithm are compared with the data of other algorithms. The data results show that the improved sparrow algorithm is very effective in solving the optimal path of the space truss. The improved sparrow algorithm keeps the same optimal path compared with the standard sparrow algorithm, and the overall reaction time is increased by 51.60%, and the number of effective iterations is increased by about 13.87%.

Keywords

modular robotsmobile robotspath planningspace roboticsdesign

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 2 , February 2024 , pp. 347 - 366
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

Liu, Y. H., Motion Error And Reliability Analysis of Space Cell Robots Master’s thesis (Harbin Institute of Technology, Harbin, China, 2020).Google Scholar
Liu, C. Y., Liu, J. G. and Liu, Y., “An open modular self-reconfigurable robot,” Modular Machine Tool & Automatic Manufacturing Technique. 2(1001-2265), 26–29+33 (2018).Google Scholar
Fukuda, T., Buss, M. and Kawauchi, Y., “Communication System of Cellular Robot: CEBOT,” 15th Annual Conference of IEEE Industrial Electronics Society, Philadelphia, PA, USA (1989), vol. 3, pp. 634–639.Google Scholar
Tanaka, H., “Reconfigurable cellular satellites maintained by space robots,” Robot. Mechatron. 18(3), 356–364 (2006).CrossRefGoogle Scholar
White, P. J. and Yim, M., “Reliable external actuation for full reachability in robotic modular self-reconfiguration,” Int. J. Robot. Res. 29(5), 598–612 (2010).CrossRefGoogle Scholar
Piranda, B. and Bourgeois, J., “Designing a quasi-spherical module for a huge modular robot to create programmable matter,” Auton. Robot. 42(8), 1619–1633 (2018).CrossRefGoogle Scholar
Liu, K. Y., Design and Reconfiguration Strategy of Series Reconfiguration Modular Robot Master’s thesis (Shanghai Jiao Tong University, Shanghai, China, 2019).Google Scholar
Wen, X. L., Study on Planning of Cellular Space Robot for On-orbit Truss-Climbing Master’s thesis (Harbin Institute of Technology, Harbin, China, 2020).Google Scholar
Wu, X., Xu, L., Zhen, R. and Wu, X., “Bi-directional adaptive A* algorithm toward optimal path planning for large-scale UAV under multi-constraints,” IEEE Access 8(2169-3536), 85431–85440 (2020).CrossRefGoogle Scholar
Fu, D. K. and Fan, P. Q., “Improved A* algorithm for 3D UAV path planning,” Intell. Comput. Appl. 10(12), 155–159 (2020).Google Scholar
Maurovic, I., Seder, M., Lenac, K. and Petrovic, I., “Path planning for active SLAM based on the D* algorithm with negative edge weights,” IEEE Trans. Syst. Man Cybern. Syst. 48(8), 1321–1331 (2017).CrossRefGoogle Scholar
Wang, S. J., Hu, L. K. and Wang, Y. F., “Path planning of indoor mobile robot based on improved D* algorithm,” Comput. Eng. Des. 41(04), 1118–1124 (2020).Google Scholar
Zhang, F., Bai, W., Qiao, Y. H., Xing, B. Y. and Zhou, P. C., “UAV indoor path planning based on improved D* algorithm,” CAAI Trans. Intell. Syst. 14(04), 662–669 (2019).Google Scholar
Yuan, M. S., Chen, M. and Wu, Q. X., “Cooperative path planning for multiple UAVs based on NSGA-III algorithm,” J. Jilin Univ. (Inf. Sci. Ed.) 39(03), 295–302 (2021).Google Scholar
Pehlivanoglu, Y. V., “A new vibrational genetic algorithm enhanced with a voronoi diagram for path planning of autonomous UAV,” Aerosp. Sci. Technol. 16(1), 47–55 (2012).CrossRefGoogle Scholar
Shao, S., Peng, Y., He, C. and Du, Y., “Efficient path planning for UAV formation via comprehensively improved particle swarm optimization,” ISA Trans. 97(0019-0578), 415–430 (2020).CrossRefGoogle ScholarPubMed
Han, Q. T., “Research on cooperate task allocation of multiple UAVs based on PSO algorithm,” J. Ordnance Equip. Eng. 40(11), 74–78 (2019).Google Scholar
Reynoso, M. P., “On the time-optimal trajectory planning along predetermined geometric paths and optimal control synthesis for trajectory tracking of robot manipulators,” Dissertations Theses – Gradworks 1997(4), 907–908 (2013).Google Scholar
Amir, H. K. and Maryam, H., “An adaptive genetic algorithm for robot motion planning in 2D complex environments,” Comput. Electr. Eng. 43(0045-7906), 317–329 (2015).Google Scholar
Anh, V. L., Veerajagadheswar, P., Vinu, S. and Rajesh, E. M., “Modified A-star algorithm for efficient coverage path planning in tetris inspired self-reconfigurable robot with integrated laser sensor,” Sensors 18(8), 2585 (2018).Google Scholar
Nouri Rahmat Abadi, B. and Carretero, J. A., “Modeling and real-time motion planning of a class of kinematically redundant parallel mechanisms with reconfigurable platform,” ASME J. Mech. Robot. 15(2), 021004 (2023).CrossRefGoogle Scholar
Dharmawan, A. G., Foong, S. and Soh, G. S., “Task-constrained optimal motion planning of redundant robots via sequential expanded lagrangian homotopy,” ASME J. Mech. Robot. 10(3), 031010 (2018).CrossRefGoogle Scholar
Zhu, J. T., “Research on robot obstacle avoidance based on neural network and sparrow algorithm,” Comput. Meas. Control 31(04), 258–263,288 (2023).Google Scholar
Wang, H., Tong, N. and Fu, Q., “Sparrow search algorithm with multiple strategies fusion,” Comput. Syst. Appl. 32(06), 159–165 (2023).Google Scholar
Bai, W. J., Jia, X. C. and Lu, T., “Application of improved sparrow search algorithms in three-dimensional path planning,” Control Eng. China 29(10), 1800–1809 (2022).Google Scholar
Xia, M. F., Li, L. M. and Ren, R. B., “Landslide prediction model based on KPCA-SSA-GRNN,” Foreign Electron. Meas. Technol. 41(09), 109–115 (2022).Google Scholar
Liu, Y., Hou, Z., Tan, Y., Liu, H. and Song, C., “Research on multi-AGVspath planning and coordination mechanism,” IEEE Access 8(2169-3536), 213345–213356 (2020).CrossRefGoogle Scholar
Pozna, C., Precup, R.-E., Horvath, E. and Petriu, E. M., “Hybrid particle filter-particle swarm optimization algorithm and application to fuzzy controlled servo systems,” IEEE Trans. Fuzzy Syst. 30(10), 4286–4297 (2022).CrossRefGoogle Scholar
Gao, B., Shen, W., Guan, H., Zheng, L. and Zhang, W., “Research on multistrategy improved evolutionary sparrow search algorithm and its application,” IEEE Access 10(2169-3536), 62520–62534 (2022).CrossRefGoogle Scholar