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Energy-efficient trajectory optimization for planetary surface manipulators via heuristic multi-objective particle swarm optimization algorithm

Published online by Cambridge University Press:  31 March 2025

Guangzhou Xiao Open the ORCID record for Guangzhou Xiao [Opens in a new window] ,
Yuting Ma  and
Yunpeng Li
Guangzhou Xiao
Affiliation:
Jiangxi Hongdu Aviation Industry Group, Nanchang, China School of Astronautics, Harbin Institute of Technology, Harbin, China
Yuting Ma
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Yunpeng Li*
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
*
Corresponding author: Yunpeng Li; Email: yunpengli@hit.edu.cn
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Abstract

This paper addresses the issue of energy-efficient trajectory optimization for planetary surface manipulators under kinematic and dynamic constraints. To mitigate the inefficiency of existing algorithms, an adaptive boundary adjustment strategy for the multi-dimensional decision space is proposed, which modifies the time intervals between neighboring configuration nodes, enabling precise adaptation of the decision space boundaries. Additionally, a complementary dual-archive guided boundary exploration strategy is introduced to connect the feasible and infeasible regions, allowing for the effective utilization of information from infeasible solutions near the constraint boundaries. This heuristic approach guides the particle swarm in efficiently exploring areas close to the constraints, significantly enhancing the evolutionary optimization capability of the swarm. Furthermore, a swarm optimal position updating strategy based on sparsity sorting is developed. This guides the particle swarm to concentrate on exploring positions where non-dominated solutions on the Pareto front are more sparsely distributed, ensuring uniformity and completeness in the final Pareto front. Finally, the aforementioned strategies are integrated into a heuristic multi-objective particle swarm optimization (HMOPSO) algorithm for the trajectory optimization of manipulators. Comparative experiments are conducted with HMOPSO and existing advanced algorithms in the field of multi-objective optimization. Experimental results demonstrate that HMOPSO exhibits superior evolutionary optimization capabilities and faster convergence rates. Moreover, performance metrics such as inverse generation distance and dominant area during the iterative process of HMOPSO significantly outperform those of existing optimization algorithms.

Keywords

planetary surface manipulatorstrajectory optimizationmulti-objective particle swarm optimization

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 4 , April 2025 , pp. 1411 - 1432
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Kuramoto, K., Kawakatsu, Y., Fujimoto, M., Araya, A., Barucci, M. A., Genda, H., Hirata, N., Ikeda, H., Imamura, T., Helbert, J., Kameda, S., Kobayashi, M., Kusano, H., Lawrence, D. J., Matsumoto, K., Michel, P., Miyamoto, H., Morota, T., Nakagawa, H., Nakamura, T., Ogawa, K., Otake, H., Ozaki, M., Russell, S., Sasaki, S., Sawada, H., Senshu, H., Tachibana, S., Terada, N., Ulamec, S., Usui, T., Wada, K., Watanabe, S. and Yokota, S., “Martian moons exploration MMX: Sample return mission to Phobos elucidating formation processes of habitable planets,” Earth Planets Space 74(1), 12 (2022).CrossRefGoogle Scholar
Hassanalian, M., Rice, D. and Abdelkefi, A., “Evolution of space drones for planetary exploration: A review,” Prog. Aeosp. Sci. 97, 61–105 (2018).CrossRefGoogle Scholar
Li, C., Zhang, R., Yu, D., Zhang, R., Yu, D., Dong, G., Liu, J., Geng, Y., Sun, Z., Yan, W., Ren, X., Su, Y., Zuo, W., Zhang, T., Cao, J., Fang, G., Yang, J., Shu, R., Lin, Y., Zou, Y., Liu, D., Liu, B., Kong, D., Zhu, X. and Ouyang, Z., “China’s Mars exploration mission and science investigation,” Space Sci. Rev. 217(4), 57 (2021).CrossRefGoogle Scholar
Verma, V., Hartman, F., Rankin, A., Del Sesto, T., Toupet, O., Graser, E., Myint, S., Davis, K., Klein, D., Koch, J., Brooks, S., Bailey, P., Justice, H., Dolci, M. and Ono, H., “First 210 Solar Days of Mars 2020 Perseverance Robotic Operations-Mobility, Robotic Arm, Sampling, and Helicopter”. In: Proceedings of the IEEE Aerospace Conference (2022).CrossRefGoogle Scholar
Nair, M. H., Rai, M. C. and Poozhiyil, M., “Design engineering a walking robotic manipulator for in-space assembly missions,” Front. Robot. AI 9, 995813 (2022).CrossRefGoogle ScholarPubMed
Paz-Delgado, G. J., Sánchez-Ibáñez, J. R., Domínguez, R., Pérez-Del-Pulgar, C. J., Kirchner, F. and García-Cerezo, A., “Combined path and motion planning for workspace restricted mobile manipulators in planetary exploration,” IEEE Access 11, 78152–78169 (2023).CrossRefGoogle Scholar
Ata, A. A., “Optimal trajectory planning of manipulators: A review,” J. Eng. Sci. Technol. 2(1), 32–54 (2007).Google Scholar
Gasparetto, A. and Zanotto, V., “A new method for smooth trajectory planning of robot manipulators,” Mech. Mach. Theory 42(4), 455–471 (2007).CrossRefGoogle Scholar
Otsu, K., Agha-Mohammadi, A. A. and Paton, M., “Where to look? Predictive perception with applications to planetary exploration,” IEEE Robot. Autom. Lett. 3(2), 635–642 (2017).CrossRefGoogle Scholar
Schmidt, G. R., Landis, G. A. and Oleson, S. R., “Human exploration using real-time robotic operations (HERRO): A space exploration strategy for the 21st century,” Acta Astronaut. 80, 105–113 (2012).CrossRefGoogle Scholar
Fallah, S., Yue, B., Vahid-Araghi, O. and Khajepour, A., “Energy management of planetary rovers using a fast feature-based path planning and hardware-in-the-loop experiments,” IEEE Trans. Veh. Technol. 62(6), 2389–2401 (2013).CrossRefGoogle Scholar
Ye, J., Hao, L. and Cheng, H., “Multi-objective optimal trajectory planning for robot manipulator attention to end-effector path limitation,” Robotica 42(6), 1761–1780 (2024).CrossRefGoogle Scholar
Schuster, M. J., Brunner, S. G., Bussmann, K., Büttner, S., Dömel, A., Hellerer, M., Lehner, H., Lehner, P., Porges, O., Reill, J., Riedel, S., Vayugundla, M., Vodermayer, B., Bodenmüller, T., Brand, C., Friedl, W., Grixa, I., Hirschmüller, H., Kaßecker, M., Márton, Z., Nissler, C., Ruess, F., Suppaand, M. Wedler, A., “Towards autonomous planetary exploration: The lightweight rover unit, its success in the SpaceBotCamp challenge, and beyond,” J. Intell. Robot. Syst. 93(3-4), 461–494 (2019).CrossRefGoogle Scholar
Liu, Y., Du, Z. and Wu, Z., Liu, F. and Li, X., “Multiobjective preimpact trajectory planning of space manipulator for self-assembling a heavy payload,” Int. J. Adv. Robot. Syst. 18(1), 1729881421990285 (2021).CrossRefGoogle Scholar
Gregory, J., Olivares, A. and Staffetti, E., “Energy-optimal trajectory planning for robot manipulators with holonomic constraints,” Syst. Control Lett. 61(2), 279–291 (2012).CrossRefGoogle Scholar
Zhang, X. and Shi, G., “Multi-objective optimal trajectory planning for manipulators in the presence of obstacles,” Robotica 40(4), 888–906 (2022).CrossRefGoogle Scholar
Sanprasit, K. and Artrit, P., “Multi-objective whale optimization algorithm for balance recovery of a humanoid robot,” Int. J. Mech. Eng. Robot. Res. 9(6), 882–893 (2020).Google Scholar
Zhao, H., Zhang, B., Yang, L., Sun, J. and Gao, Z., “Obstacle avoidance and near time-optimal trajectory planning of a robotic manipulator based on an improved whale optimisation algorithm,” Arab. J. Sci. Eng. 47(12), 16421–16438 (2022).CrossRefGoogle Scholar
Shanmugasundar, G., Fegade, V., Mahdal, M. and Kalita, K., “Optimization of variable stiffness joint in robot manipulator using a novel NSWOA-Marcos approach,” Processes 10(6), 1074 (2022).CrossRefGoogle Scholar
Tripathi, P. K., Bandyopadhyay, S. and Pal, S. K., “Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients,” Inf. Sci. 177(22), 5033–5049 (2007).CrossRefGoogle Scholar
Cao, X., Yan, H., Huang, Z., Ai, S., Xu, Y., Fu, R. and Zou, X., “A multi-objective particle swarm optimization for trajectory planning of fruit picking manipulator,” Agronomy 11(11), 2286 (2021).CrossRefGoogle Scholar
Lan, J., Xie, Y., Liu, G. and Cao, M., “A multi-objective trajectory planning method for collaborative robot,” Electronics 9(5), 859 (2020).CrossRefGoogle Scholar
Liu, J., Yang, Z. and Li, D., “A multiple search strategies based grey wolf optimizer for solving multi-objective optimization problems,” Expert Syst. Appl. 145, 113134 (2020).CrossRefGoogle Scholar
Petrović, M., Jokić, A., Miljković, Z. and Kulesza, Z., “Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm,” Appl. Soft. Comput. 131, 109784 (2022).CrossRefGoogle Scholar
Mirjalili, S., Saremi, S., Mirjalili and, S. M. Coelho, L. S., “Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization,” Expert Syst. Appl. 47, 106–119 (2016).CrossRefGoogle Scholar
Hu, Y., Zhang, Y. and Gong, D., “Multiobjective particle swarm optimization for feature selection with fuzzy cost,” IEEE Trans. Cybern. 51(2), 874–888 (2021).CrossRefGoogle ScholarPubMed
Zhang, Y., Ji, X., Gao, X., Gong, D. and Sun, X., “Objective-constraint mutual-guided surrogate-based particle swarm optimization for expensive constrained multimodal problems,” IEEE Trans. Evol. Comput. 27(4), 908–922 (2023).CrossRefGoogle Scholar
Cheraghi, R. and Jahangir, M. H., “Multi-objective optimization of a hybrid renewable energy system supplying a residential building using NSGA-II and MOPSO algorithms,” Energy Conv. Manag. 294, 117515 (2023).CrossRefGoogle Scholar
Chettibi, T., “Multi-objective trajectory planning for industrial robots using a hybrid optimization approach,” Robotica 42(6), 2026–2045 (2024).CrossRefGoogle Scholar
Dao, T. K., Ngo, T. G., Pan and, J. S. Nguyen, T., “Enhancing path planning capabilities of automated guided vehicles in dynamic environments: Multi-objective pso and dynamic-window approach,” Biomimetics 9(1), 35 (2024).CrossRefGoogle ScholarPubMed
Xiao, G., Zhang, L. and Wu, T., Han, Y., Ding, Y. and Han, C., “FBi-RRT: A path planning algorithm for manipulators with heuristic node expansion,” Robotica 42(3), 644–659 (2024).CrossRefGoogle Scholar
Xiao, G., Wu, T., Weng, R., Zhang, R., Han, Y., Dong, Y. and Liang, Y., “NA-OR: A path optimization method for manipulators via node attraction and obstacle repulsion,” Sci. China-Technol. Sci. 66(5), 1205–1213 (2023).CrossRefGoogle ScholarPubMed