Hostname: page-component-5b777bbd6c-gtgcz Total loading time: 0 Render date: 2025-06-18T19:14:04.317Z Has data issue: false hasContentIssue false
  • English
  • Français

Configuration optimization of variable topological space robot for impulse minimization based on bilevel approach

Published online by Cambridge University Press:  30 May 2025

Hongxu Wang Open the ORCID record for Hongxu Wang [Opens in a new window] ,
Jinsheng Guo ,
Carlo Canali ,
Chengfei Yue ,
Xibin Cao  and
Darwin G. Caldwell
Hongxu Wang
Affiliation:
Research Center of Satellite Technology, Harbin Institute of Technology, Harbin, China State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster, Harbin Institute of Technology, Harbin, China Advanced Robotics, Italian Institute of Technology, Genova, Italy
Jinsheng Guo
Affiliation:
Research Center of Satellite Technology, Harbin Institute of Technology, Harbin, China State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster, Harbin Institute of Technology, Harbin, China
Carlo Canali
Affiliation:
Advanced Robotics, Italian Institute of Technology, Genova, Italy
Chengfei Yue*
Affiliation:
State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster, Harbin Institute of Technology, Harbin, China School of Aerospace Science, Harbin Institute of Technology Shenzhen, Shenzhen, China
Xibin Cao
Affiliation:
Research Center of Satellite Technology, Harbin Institute of Technology, Harbin, China State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster, Harbin Institute of Technology, Harbin, China
Darwin G. Caldwell
Affiliation:
Advanced Robotics, Italian Institute of Technology, Genova, Italy
*
Corresponding author: Chengfei Yue; Email: yuechengfei@hit.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Variable topological space robots are essential for providing adaptability and flexibility, enabling the robot to adjust its morphology to perform a range of tasks in the unstructured environment of space. However, impact is a common consequence of topology transformation in space robotics, which may lead to irreversible damage, such as the shedding of solid lubrication on joints. Nevertheless, determining the precise force-time relationships of such impacts poses significant challenges, especially when accounting for various connection mechanisms. In this work, a docking strategy that optimizes the manipulator’s joint angle configuration to minimize the impulse when the topology changes is proposed. First, an estimation technique is developed to quantify the impulse generated by topology transformation, employing spatial operator algebra and generalized momentum balance equations. Based on this model, the impulse minimization is modelled as a bilevel optimization problem, which decomposes a complex multipolar problem into two simpler subproblems. Although this optimization model may compromise computational efficiency, it increases the probability of achieving an optimal solution. To address this, a bilevel solution strategy based on a heuristic algorithm is proposed. In this framework, the lower level uses particle swarm optimization to determine the global optimum, while the upper level adopts simulated annealing to enhance computational speed. Finally, simulations are conducted to validate the proposed approach. Results demonstrate that the proposed method substantially reduces impulse.

Keywords

impulse minimizationbilevel optimizationspace robots with variable topologytopology transformationheuristic algorithm
Type
Research Article
Information
Robotica , First View , pp. 1 - 19
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Belvin, W. K., Doggett, W. R., Watson, J. J., Dorsey, J. T., Warren, J. E., Jones, T. C., Komendera, E. E., Mann, T. and Bowman, L. M., “In-Space Structural Assembly: Applications and Technology,” 3rd AIAA Spacecraft Structures Conference (2016) p. 2163.Google Scholar
Zhou, Y., Luo, J. and Wang, M., “Dynamic manipulability analysis of multi-arm space robot,” Robotica 39(1), 23–41 (2021).10.1017/S0263574720000077CrossRefGoogle Scholar
Fu, X., Ai, H. and Chen, L., “Integrated sliding mode control with input restriction, output feedback and repetitive learning for space robot with flexible-base, flexible-link and flexible-joint,” Robotica 41(1), 370–391 (2023).10.1017/S0263574722001369CrossRefGoogle Scholar
Wu, Y.-H., Yu, Z.-C., Li, C.-Y., He, M.-J., Hua, B. and Chen, Z.-M., “Reinforcement learning in dual-arm trajectory planning for a free-floating space robot,” Aerospace Sci. Technol. 98, 105657 (2020).10.1016/j.ast.2019.105657CrossRefGoogle Scholar
Kalaycioglu, S. and de Ruiter, A., “Dual arm coordination of redundant space manipulators mounted on a spacecraft,” Robotica 41(8), 2489–2518 (2023).10.1017/S0263574723000504CrossRefGoogle Scholar
Yue, C., Lin, T., Zhang, X., Chen, X. and Cao, X., “Hierarchical path planning for multi-arm spacecraft with general translational and rotational locomotion mode,” Sci. China Technol. Sci. 66(4), 1180–1191 (2023).10.1007/s11431-022-2275-2CrossRefGoogle Scholar
Shang, D., Li, X., Yin, M., Liu, J. and Zhou, S., “Dynamic modeling and tracking control strategy for flexible telescopic space manipulator based on neural network compensation,” Adv. Space Res. 72(9), 3645–3665 (2023).10.1016/j.asr.2023.07.038CrossRefGoogle Scholar
Cong, P.-C. and Zhang, X., “Preimpact configuration analysis of a dual-arm space manipulator with a prismatic joint for capturing an object,” Robotica 31(6), 853–860 (2013).10.1017/S0263574713000118CrossRefGoogle Scholar
Yan, L., Xu, W., Hu, Z. and Liang, B., “Multi-objective configuration optimization for coordinated capture of dual-arm space robot,” Acta Astronaut. 167, 189–200 (2020).10.1016/j.actaastro.2019.11.002CrossRefGoogle Scholar
Lu, X. and Jia, Y., “Trajectory planning of free-floating space manipulators with spacecraft attitude stabilization and manipulability optimization,” IEEE Trans. Syst. Man Cybern. Syst. 51(12), 7346–7362 (2020).10.1109/TSMC.2020.2966859CrossRefGoogle Scholar
Takao, Y., Mori, O. and Kawaguchi, J., “Analysis and design of a spacecraft docking system using a deployable boom,” Acta Astronaut. 179, 172–185 (2021).10.1016/j.actaastro.2020.10.031CrossRefGoogle Scholar
Daneshjou, K. and Alibakhshi, R., “Multibody dynamical modeling for spacecraft docking process with spring-damper buffering device: A new validation approach,” Adv. Space Res. 61(1), 497–512 (2018).10.1016/j.asr.2017.10.036CrossRefGoogle Scholar
Zhao, Z., Liu, C. and Chen, T., “Docking dynamics between two spacecrafts with apdses,” Multibody Syst. Dyn. 37(3), 245–270 (2016).10.1007/s11044-015-9477-4CrossRefGoogle Scholar
Haug, E. J., Wu, S. C. and Yang, S. M., “Dynamics of mechanical systems with coulomb friction, stiction, impact and constraint addition-deletion–i theory,” Mech. Mach. Theory 21(5), 401–406 (1986).10.1016/0094-114X(86)90088-1CrossRefGoogle Scholar
Khulief, Y., “Numerical modelling of impulsive events in mechanical systems,” Int. J. Model. Simul. 30(1), 80–86 (2010).10.1080/02286203.2010.11442561CrossRefGoogle Scholar
Chang, C.-C. and Peng, S.-T., “Impulsive motion of multibody systems,” Multibody Syst. Dyn. 17(1), 47–70 (2007).10.1007/s11044-007-9035-9CrossRefGoogle Scholar
Raina, D., Gora, S., Maheshwari, D. and Shah, S. V., “Impact modeling and reactionless control for post-capturing and maneuvering of orbiting objects using a multi-arm space robot,” Acta Astronaut. 182, 21–36 (2021).10.1016/j.actaastro.2021.01.034CrossRefGoogle Scholar
Jouybari, B. R., Osgouie, K. G. and Meghdari, A., “Optimization of kinematic redundancy and workspace analysis of a dual-arm cam-lock robot,” Robotica 34(1), 23–42 (2016).10.1017/S0263574714000423CrossRefGoogle Scholar
Zhou, Y., Luo, J. and Wang, M., “Grasping force optimization for dual-arm space robot after capturing target based on task compatibility,” Adv. Space Res. 70(5), 1496–1511 (2022).10.1016/j.asr.2022.06.006CrossRefGoogle Scholar
Zhang, B., Liang, B., Wang, X., Li, G., Chen, Z. and Zhu, X., “Manipulability measure of dual-arm space robot and its application to design an optimal configuration,” Acta Astronaut. 128, 322–329 (2016).10.1016/j.actaastro.2016.07.040CrossRefGoogle Scholar
Xu, R., Luo, J. and Wang, M., “Optimal grasping pose for dual-arm space robot cooperative manipulation based on global manipulability,” Acta Astronaut. 183, 300–309 (2021).10.1016/j.actaastro.2021.03.021CrossRefGoogle Scholar
Toglia, C., Sabatini, M., Gasbarri, P. and Palmerini, G. B., “Optimal target grasping of a flexible space manipulator for a class of objectives,” Acta Astronaut. 68(7-8), 1031–1041 (2011).10.1016/j.actaastro.2010.09.013CrossRefGoogle Scholar
Lee, H., Sohn, J. and Lee, J., “Optimization of Dual-Arm Configurations for Multiple Object Handling,” 2012 IEEE International Symposium on Industrial Electronics (IEEE, 2012) pp. 1291–1296.Google Scholar
Osgouie, K. G., Meghdari, A., Sohrabpour, S. and Jelodar, M. S., “Genetic Algorithm Based Optimization for Dual-Arm Cam-Lock Robot Configuration,” 2007 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (IEEE, 2007) pp. 1–6.Google Scholar
Xue, Z., Liu, J., Li, Y. and Liu, J., “Configuration optimization of a dual-arm reconfigurable space robot based on closed-chain inertia matching,” IEEE Trans. Autom. Sci. Eng. 22, 6421–6438 (2024).10.1109/TASE.2024.3445453CrossRefGoogle Scholar
Zhao, J., Yang, X., Zhao, Z., Yang, G. and Zhao, L., “Inverse kinematics and multi-objective configuration optimization of the SSRMS manipulator,” Adv. Space Res. 72(9), 3580–3594 (2023).10.1016/j.asr.2023.06.058CrossRefGoogle Scholar
Colson, B., Marcotte, P. and Savard, G., “Bilevel programming: A survey,” 4OR 3, 87–107 (2005).10.1007/s10288-005-0071-0CrossRefGoogle Scholar
Zhang, S., Li, A., Ren, J. and Ren, R., “Kinematics inverse solution of assembly robot based on improved particle swarm optimization,” Robotica 42(3), 833–845 (2024).10.1017/S0263574723001789CrossRefGoogle Scholar
Fusic, J. and Sitharthan, R., “Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment,” Robotica 42(4), 977–1000 (2024).Google Scholar
Delahaye, D., Chaimatanan, S. and Mongeau, M., Simulated Annealing: From Basics to Applications (Springer International Publishing, Cham, 2019) pp. 1–35.Google Scholar
Malzahn, J., Kashiri, N., Romiti, E., Baccelliere, L., Cordasco, S., Laurenzi, A., Margan, A., Hoffman, E. M., Muratore, L., and Tsagarakis, N., “Modular configurable robot, corresponding method and computer program product. US Patent App. 17/787,819.Google Scholar
Hu, J. and Wang, T., “An efficient high-precision recursive dynamic algorithm for closed-loop multibody systems,” Int. J. Numer. Methods Eng. 118(4), 181–208 (2019).10.1002/nme.6007CrossRefGoogle Scholar
Jain, A. and Rodriguez, G., “Recursive flexible multibody system dynamics using spatial operators,” J. Guid. Control Dyn. 15(6), 1453–1466 (1992).10.2514/3.11409CrossRefGoogle Scholar
Camacho-Vallejo, J.-F., Corpus, C. and Villegas, J. G., “Metaheuristics for bilevel optimization: A comprehensive review,” Comput. Oper. Res. 161, 106410 (2024).10.1016/j.cor.2023.106410CrossRefGoogle Scholar
Liang, J., Guo, H., Chen, K., Yu, K., Yue, C. and Li, X., “An improved kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics,” Robotica 41(4), 1087–1097 (2023).10.1017/S026357472200145XCrossRefGoogle Scholar
Lin, T., Yue, C., Liu, Z. and Cao, X., “Modular multi-level replanning tamp framework for dynamic environment,” IEEE Robot. Autom. Lett. 9(5), 4234–4241 (2024).10.1109/LRA.2024.3377556CrossRefGoogle Scholar