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Nonholonomic motion planning for minimizing base disturbances of space manipulators based on multi-swarm PSO

Published online by Cambridge University Press:  06 November 2015

Qiang Zhang ,
Lu Ji ,
Dongsheng Zhou  and
Xiaopeng Wei
Qiang Zhang*
Affiliation:
Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian, 116622, P. R. China. E-mail: [email protected]
Lu Ji
Affiliation:
Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian, 116622, P. R. China. E-mail: [email protected]
Dongsheng Zhou
Affiliation:
Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian, 116622, P. R. China. E-mail: [email protected]
Xiaopeng Wei
Affiliation:
Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian, 116622, P. R. China. E-mail: [email protected] College of Computer Science, Dalian University of Technology, Dalian, 116024, P. R. China. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Because space manipulators must satisfy the law of conservation of momentum, any motion of a manipulator within a space-manipulator system disturbs the position and attitude of its free-floating base. In this study, the authors have designed a multi-swarm particle swarm optimization (PSO) algorithm to address the motion planning problem and so minimize base disturbances for 6-DOF space manipulators. First, the equation of kinematics for space manipulators in the form of a generalized Jacobian matrix (GJM) is introduced. Second, sinusoidal and polynomial functions are used to parameterize joint motion, and a quaternion representation is used to represent the attitude of the base. Moreover, by transforming the planning problem into an optimization problem, the objective function is analyzed and the proposed algorithm explained in detail. Finally, numerical simulation results are used to verify the validity of the proposed algorithm.

Keywords

Space manipulatorsFree-floating baseMulti-swarm PSOMinimizing base disturbanceNonholonomic motion planning
Type
Articles
Information
Robotica , Volume 35 , Issue 4 , April 2017 , pp. 861 - 875
Copyright
Copyright © Cambridge University Press 2015 

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References

1. Huang, P. F., Xu, Y. S. and Liang, B., “Dynamic balance controlof multi-arm free-floating space robots,” Int. J. Adv. Robot. Syst. 2, 117–125 (2005).CrossRefGoogle Scholar
2. Nakamura, Y. and Mukherjee, R., “Nonholonomic path planning of space robots via a bidirectional approach,” IEEE Trans. Robot. Autom. 7, 500–514 (1991).CrossRefGoogle Scholar
3. Papadopoulos, E., Papadimitriou, I. and Poulakakis, I., “Polynomial-based obstacle avoidance techniques for nonholonomic mobile manipulator systems,” Robot. Auton. Syst. 51, 229–247 (2005).CrossRefGoogle Scholar
4. Xu, W., Liang, B., Li, C., Qiang, W., Xu, Y. and Lee, K. K., “Non-Holonomic Path Planning of Space Robot based on Genetic Algorithm,” IEEE International Conference on, Robotics and Biomimetics ROBIO'06, Kunming, China, (2006) pp. 1471–1476.Google Scholar
5. Dubowsky, S., Vance, E. E. and Torres, M. A., “The Control of Space Manipulators Subject to Spacecraft Attitude Control Saturation Limits,” Proceedings of NASA Conference Space Telerobotics, Cambridge, MA, (1989) pp. 409–418.Google Scholar
6. Vafa, Z. and Dubowsky, S., “Minimization of Spacecraft Disturbances in Space Robotic Systems,” Advances in the Astronautical Science, Cambridge, MA, (1988) pp. 91–108.Google Scholar
7. Dubowsky, S. and Torres, M., “Path Planning for Space Manipulators to Minimize Spacecraft Attitude Disturbances,” IEEE International Conference on Robotics and Automation, Cambridge, MA, USA, (1991) pp. 2522–2528.Google Scholar
8. Papadoupolous, E. and Dubowsky, S., “Coordinated Manipulator/Spacecraft Motion Control for Space Robotic Systems,” IEEE International Conference on Robotics and Automation, Cambridge, MA, (1991) pp. 1696–1701.Google Scholar
9. Nenchev, D., Yoshida, K. and Umetani, Y., “Analysis, Design and Control of Free-Flying Space Robots Using Fixed-Attitude-Restricted Jacobian Matrix[C],” The fifth international symposium on Robotics research. MIT Press, (1991) pp. 251–258.Google Scholar
10. Yoshida, K., Hashizume, K. and Abiko, S., “Zero Reaction Maneuver: Flight Validation with ETS-VII Space Robot and Extension to Kinematically Redundant Arm,” Proceedings of the IEEE International Conference on Robotics and Automation, Piscataway, USA, (2001) pp. 441–446.Google Scholar
11. Yoshida, K., Kurazume, R. and Umetani, Y., “Dual Arm Coordination in Space Free-Flying Robot,” Proceedings of the IEEE International Conference on Robotics and Automation, Sacramento, CA, (1991) pp. 2516–2521.Google Scholar
12. Yamada, K., “Arm Path Planning for a Space Robot,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Yokohama, Japan, (1993) pp. 2049–2055.Google Scholar
13. Suzuki, T. and Nakamura, Y., “Planning Spiral Motion of Nonholonomic Space Robots,” Proceedings of the IEEE International Conferences on Robotics and Automation, Minneapolis, Minnesota, (1996) pp. 718–725.Google Scholar
14. Wang, C. H., Feng, B. M. and Ma, G. C., “Robust Tracking Control of Space Robots using Fuzzy Neural Network,” Proceedings IEEE International Symposiumon Computational Intelligence in Robotics and Automation, Espoo, (2005) pp. 181–185.Google Scholar
15. Senda, K., Murotsu, Y. and Ozaki, M., “A method of attitude control for space robots: An approach using a neural network,” Trans. Japan Soc. Mech. Eng. Ser. C 57, 2356–2362 (1991).CrossRefGoogle Scholar
16. Akiyama, T. and Sakawa, Y., “Path planning of space robots by using nonlinear optimization technique,” Trans. Soc. Instrum. Control Eng. 31, 193–197 (1995).CrossRefGoogle Scholar
17. Zappa, B., Legnani, G. and Adamini, R., “Path planning of free-flying space manipulators: An exact solution for polar robots,” Mech. Mach. Theory 40, 806–820 (2005).CrossRefGoogle Scholar
18. Belousov, I., Estevs, C., Laumond, J. and Ferré, E., “Motion Planning for the Large Space Manipulators with Complicated Dynamics,” Proceeding IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, (2005) pp. 3713–3719.Google Scholar
19. Yamada, K., Yoshikawa, S. and Fujita, Y., “Arm path planning of a space robot with angular momentum,” Adv. Robot. 9, 693–709 (1995).CrossRefGoogle Scholar
20. Nenchev, D. N., “Controller for A Redundant Free-flying Space Robot with Spacecraft Atitude/Manipulator Motion Coordination,” Proceedings International Conference on Intelligent Robots and Systems, Yokohama, (1993) pp. 2108–2114.Google Scholar
21. Akbarimajd, A., “Optimal motion planning of juggling by 3-DOF manipulators using adaptive PSO algorithm,” Robotica 32, 967–984 (2014).CrossRefGoogle Scholar
22. Rana, A. and Zalzala, A., “Near time-optimal collision-free motion planning of robotic manipulators using an evolutionary algorithm,” Robotica 14, 621–632 (1996).CrossRefGoogle Scholar
23. Rigatos, G. G., “Distributed gradient and particle swarm optimization for multi-robot motion planning,” Robotica 26, 357–370 (2008).CrossRefGoogle Scholar
24. Wang, S., Bao, J. and Fu, Y., “Real-time motion planning for robot manipulators in unknown environments using infrared sensors,” Robotica 25, 201–211 (2007).CrossRefGoogle Scholar
25. Umetani, Y. and Yoshida, K., “Resolved motion rate control of space manipulators with generalized jacobian matrix,” IEEE Trans. Robot. Autom. 5, 303–314 (Jun. 1989).CrossRefGoogle Scholar
26. Shuzhen, Z., Jianlong, H., Minxiu, K. and Lining, S., “Inverse Kinematics and Application of a Type of Motion Chain Based on Screw Theory and Analytic Geometry,” IEEE International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, China, (2010), pp. 680–684.Google Scholar
27. Xie, J., Qiang, W., Liang, B. and Li, C., “Inverse Kinematics Problem for 6-DOF Space Manipulator Based On the Theory of Screws,” Proceedings of the 2007 IEEE International Conference on Robotics and Biomimetics, Sanya, China, (Dec. 15–18, 2007) pp. 1659–1663.Google Scholar
28. Chou, J. C. K., “Quaternion kinematic and dynamic differential equations,” IEEE Trans. Robot. Autom. 8, 53–64 (Feb. 1992).CrossRefGoogle Scholar
29. Yuan, J. S. C., “Closed-loop manipulator control using quaternion,” IEEE J. Robot. Autom. 4, 434–440 (Aug. 28, 1988).CrossRefGoogle Scholar
30. Xu, W., Liang, B., Li, C., Xu, Y. and Qiang, W., “Path planning of free floating robot in cartesian space using direct kinematics,” Int. J. Adv. Robot. Syst. 4, 17–26 (2007).CrossRefGoogle Scholar
31. Zhang, F., Fu, Y., Hua, L., Chen, H., Wang, S. and Guo, B., “Point-to-point Planning for Free-floating Space Manipulator with Zero-disturbance Spacecraft Attitude,” Proceeding of the IEEE International Conference on Information and Automation, Shenyang, China, (Jun, 2012) pp. 142–147.CrossRefGoogle Scholar
32. Wu, J., Shi, S., Wang, B., Jiang, Z. and Liu, H., “Path Planning for Minimizing BaseReaction of Space Robot and its GroundExperimental Study,” Proceedings of the 2009 IEEE International Conferenceon Mechatronics and Automation, Changchun, China, (Aug. 9–12, 2009) pp. 4627–4632.Google Scholar
33. Kennedy, J., “Particle Swarm Optimization,” In: Encyclopedia of Machine Learning, Yoshida, Kazuya (eds.) (Springer, 2010) pp. 760–766.Google Scholar
34. Huo, X., Cheng, Y., Wang, Y. and Hu, Q., “PSO based Trajectory Planning of Free Floating Space Robot for Minimizing Spacecraft Attitude Disturbance,” IEEE Chinese Control and Decision Conference (CCDC), Mianyang, (2011) pp. 819–822.Google Scholar
35. Huang, P. and Xu, Y., “PSO-Based Time-Optimal Trajectory Planning for Space Robot with Dynamic Constraints,” Proceedings of the 2006 IEEE International Conference on Robotics and Biomimetics, Kunming, (Dec. 17–20, 2006) pp. 1402–1407.CrossRefGoogle Scholar
36. Xu, W., Li, C., Liang, B., Liu, Y. and Xu, Y., “The cartesian path planning of free-floating space robot using particle swarm optimization,” Int. J. Adv. Robot. Syst. 5, 301–310 (2008).CrossRefGoogle Scholar
37. Qiang, Z. and Fang, G., “A Two-Phase Multi-population Particle Swarm Algorithm for Global Optimization,” Proceedings of 2006 International Conference on artificial Intelligence - 50 Years' Achievements, Future Directions and Social Impacts, Beijing, (Aug. 2006) pp. 271–274.Google Scholar
38. Shimizu, M., Hiraoka, T., Fujishima, K., Kurosu, A., Hashizume, K. and Yoshida, K., “The Spacedyn: A MATLAB Toolbox for Space and Mobile Robots,” http://www.astro.mech.tohoku.ac.jp/spacedyn/, (1999).Google Scholar