Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-16T05:22:09.619Z Has data issue: false hasContentIssue false
  • English
  • Français

Distributed gradient and particle swarm optimization for multi-robot motion planning

Published online by Cambridge University Press:  01 May 2008

Gerasimos G. Rigatos
Gerasimos G. Rigatos*
Affiliation:
Unit of Industrial Automation, Industrial Systems Institute, 26504 Rion Patras, Greece.
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Two distributed stochastic search algorithms are proposed for motion planning of multi-robot systems: (i) distributed gradient, (ii) swarm intelligence theory. Distributed gradient consists of multiple stochastic search algorithms that start from different points in the solutions space and interact with each other while moving toward the goal position. Swarm intelligence theory is a derivative-free approach to the problem of multi-robot cooperation which works by searching iteratively in regions defined by each robot's best previous move and the best previous move of its neighbors. The performance of both approaches is evaluated through simulation tests.

Keywords

Multi-robot systemMotion planningStochastic search algorithmsDistributed gradientLyapunov stabilitySwarm intelligence
Type
Article
Information
Robotica , Volume 26 , Issue 3 , May 2008 , pp. 357 - 370
Copyright
Copyright © Cambridge University Press 2008

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.)

References

1.Groß, R., Bonani, M., Mondada, F. and Dorigo, M., “Autonomous self-assembly in swarm-bots,” IEEE Trans. Robot. 22 (6), 1115–1130 (2006).Google Scholar
2.Rigatos, G. G., Tzafestas, S. G. and Evangelidis, G. J., “Reactive Parking Control of a non-holonomic vehicle via a fuzzy learning automaton,” IEE Procceedings on Control Theory and Applications 148 (2), 169–180 (2001).CrossRefGoogle Scholar
3.Bishop, B. E., “On the use of redundant manipulator techniques for control of platoons of cooperative robotic vehicles,” IEEE Trans. syst. Man Cybern.–Part A, 33 (5), 608–615 (2003).CrossRefGoogle Scholar
4.Hong, Y., Gao, L., Cheng, D. and Hu, J., “Luapunov-based approach to multi-agent systems with switching jointly connected interconnection,” IEEE Trans. Autom. Control 52 (5), 943–948 (2007).CrossRefGoogle Scholar
5.Guo, Y. and Parker, L. E., A distributed and optimal motion planning approach for multiple mobile robots, In: Procceedings 2002 IEEE International Conference on Robotics and Automation, Washington DC, (May 2002), pp. 2612–2619.Google Scholar
6.Sinha, A. and Ghose, D., “Generalization of linear cyclic pursuit with application to rendezvous of multiple autonomous agents,” IEEE Trans. Autom. Control 51 (11), 1819–1824 (2006).CrossRefGoogle Scholar
7.Pagello, E., Angelo, A. D' and Menegatti, E., “Cooperation issues and distributed sensing for multi-robot systems,” Proceedings of the IEEE 94 (7), 1–12 (2006) 1370–1383.CrossRefGoogle Scholar
8.Sepulchre, R., Paley, D. A. and Leonard, N. E., “Stabilization of planar collective motion: all to all communication,” IEEE Trans. Autom. Control 52 (5), 811–824 (2007).CrossRefGoogle Scholar
9.Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5 (1)90–99 (1986).CrossRefGoogle Scholar
10.Rimon, E. and Koditscheck, D. E., “Exact robot navigation using artifical potential functions,” IEEE Trans. Robot. Autom. 8, 501–518 (1991).CrossRefGoogle Scholar
11.Masoud, S. A. and Masoud, A. A., “Motion planning in the presence of directional and regional avoidance constraints using nonlinear, nisotropic, harmonic potential fields: a physical metaphor,” IEEE Trans. Syst. Man Cybern.—Part A 32 (6), 705–723 (2002).CrossRefGoogle Scholar
12.Reif, J. H. and Wang, H., “Social potential fields: A distributed behavioral control for autonomous robots,” Robot. Auton. Syst., Elsevier, 27, 171–194 (1999).Google Scholar
13.Comets, F. and Meyre, T., “Calcul stochastique et modèles de diffusions,” Dunod, Paris, 2006.Google Scholar
14.Rigatos, G. G., Tzes, A. P. and Tzafestas, S. G., “Distributed Stochastic Search for Multi-robot Cooperative Behavior, IMACS 2005 International Conference, Paris, France (July 2005).Google Scholar
15.Jatmitko, W., Sekiyama, K. and Fukuda, T., “A PSO-based mobile robot for odour source localization in dynamic advection-diffusion with obstacles environment,” IEEE Comput. Intell. Mag. 2 (2), 37–42 (2007).CrossRefGoogle Scholar
16.Levine, H. and Rappel, W. J., “Self-organization in systems of self-propelled particles,” Phys. Rev. E 63 (2000).CrossRefGoogle ScholarPubMed
17.Clerk, M. and Kennedy, J., “The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space,” IEEE Trans. Evol. Comput. 6 (1), 58–73, (2002).CrossRefGoogle Scholar
18.Duflo, M., Algorithmes stochastiques, Mathématiques et Applications Springer (1996).Google Scholar
19.Benveniste, A., Métivier, M. and Priouret, P., “Adaptive algorithms and stochastic approximations,” Springer (1990).CrossRefGoogle Scholar
20.Gazi, V. and Passino, K., “Stability analysis of social foraging swarms,” IEEE Trans. Sys. Man Cybern.—Part B, Cybernetics, 34 (1), 539–557 (2004).Google ScholarPubMed
21.Khalil, H., Nonlinear Systems (Prentice Hall), 1996.Google Scholar