Multi-objective optimal trajectory planning of space robot using particle swarm optimization

Panfeng Huang; Gang Liu; Jianping Yuan; Yangsheng Xu

doi:10.1007/978-3-540-87734-9_20

Multi-objective optimal trajectory planning of space robot using particle swarm optimization

Panfeng Huang, Gang Liu, Jianping Yuan, Yangsheng Xu

School of Astronautics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

Space robots are playing significant roles in the maintenance and repair of space station and satellites and other future space services. The motion trajectory planning is a key problem for accomplishing above missions. In order to obtain the high efficiency, safety motion trajectory of space robot, the motion trajectory should be optimized in advance. This paper describes the multi-objective optimization for optimizing the motion trajectory of space robot using a multi-objective particle swarm optimization (MOPSO). In this formulation, the multi-objective function is generated which includes some parameters such as motion time, dynamic disturbance, and jerk, and so on. Then a number of relative parameters can be simultaneously optimized through searching in the parameter space using MOPSO algorithms. The simulation results attest that MOPSO algorithm has satisfactory performance and real value in fact.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings
Publisher	Springer Verlag
Pages	171-179
Number of pages	9
Edition	PART 2
ISBN (Print)	3540877339, 9783540877332
DOIs	https://doi.org/10.1007/978-3-540-87734-9_20
State	Published - 2008
Event	5th International Symposium on Neural Networks, ISNN 2008 - Beijing, China Duration: 24 Sep 2008 → 28 Sep 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	5264 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Symposium on Neural Networks, ISNN 2008
Country/Territory	China
City	Beijing
Period	24/09/08 → 28/09/08

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10.1007/978-3-540-87734-9_20

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Huang, P., Liu, G., Yuan, J., & Xu, Y. (2008). Multi-objective optimal trajectory planning of space robot using particle swarm optimization. In Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings (PART 2 ed., pp. 171-179). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5264 LNCS, No. PART 2). Springer Verlag. https://doi.org/10.1007/978-3-540-87734-9_20

Huang, Panfeng ; Liu, Gang ; Yuan, Jianping et al. / Multi-objective optimal trajectory planning of space robot using particle swarm optimization. Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 2. ed. Springer Verlag, 2008. pp. 171-179 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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abstract = "Space robots are playing significant roles in the maintenance and repair of space station and satellites and other future space services. The motion trajectory planning is a key problem for accomplishing above missions. In order to obtain the high efficiency, safety motion trajectory of space robot, the motion trajectory should be optimized in advance. This paper describes the multi-objective optimization for optimizing the motion trajectory of space robot using a multi-objective particle swarm optimization (MOPSO). In this formulation, the multi-objective function is generated which includes some parameters such as motion time, dynamic disturbance, and jerk, and so on. Then a number of relative parameters can be simultaneously optimized through searching in the parameter space using MOPSO algorithms. The simulation results attest that MOPSO algorithm has satisfactory performance and real value in fact.",

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booktitle = "Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings",

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Huang, P, Liu, G, Yuan, J & Xu, Y 2008, Multi-objective optimal trajectory planning of space robot using particle swarm optimization. in Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5264 LNCS, Springer Verlag, pp. 171-179, 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, 24/09/08. https://doi.org/10.1007/978-3-540-87734-9_20

Multi-objective optimal trajectory planning of space robot using particle swarm optimization. / Huang, Panfeng; Liu, Gang; Yuan, Jianping et al.
Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 2. ed. Springer Verlag, 2008. p. 171-179 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5264 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Space robots are playing significant roles in the maintenance and repair of space station and satellites and other future space services. The motion trajectory planning is a key problem for accomplishing above missions. In order to obtain the high efficiency, safety motion trajectory of space robot, the motion trajectory should be optimized in advance. This paper describes the multi-objective optimization for optimizing the motion trajectory of space robot using a multi-objective particle swarm optimization (MOPSO). In this formulation, the multi-objective function is generated which includes some parameters such as motion time, dynamic disturbance, and jerk, and so on. Then a number of relative parameters can be simultaneously optimized through searching in the parameter space using MOPSO algorithms. The simulation results attest that MOPSO algorithm has satisfactory performance and real value in fact.

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Huang P, Liu G, Yuan J, Xu Y. Multi-objective optimal trajectory planning of space robot using particle swarm optimization. In Advances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. PART 2 ed. Springer Verlag. 2008. p. 171-179. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-540-87734-9_20

Multi-objective optimal trajectory planning of space robot using particle swarm optimization

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