Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-25T16:45:37.293Z Has data issue: false hasContentIssue false
  • English
  • Français

Local navigation strategies for a team of robots

Published online by Cambridge University Press:  02 March 2021

Antonio Sgorbissa  and
Ronald C. Arkin
Antonio Sgorbissa*
Affiliation:
Laboratorium, DIST – University of Genova (Italy)
Ronald C. Arkin*
Affiliation:
Mobile Robot Laboratory, College Of Computing – GaTech (USA)
*
Email: antonio.sgorbissa@unige.it
Email: arkin@cc.gatech.edu
Get access Rights & Permissions [Opens in a new window]

Summary

Whenever a mobile robot has to deal with an environment that is totally or partially unknown or dynamically changing, local navigation strategies are very important for the robot to successfully achieve its goals. Unfortunately, local navigation algorithms that have been proposed in the literature offer poor performance (or even fail) whenever the geometry of the free space in which the robot is requested to operate increases its complexity. In this paper, we deal with a team composed of many robots, and we show how robots navigating within an unknown environment with local communication capabilities (only line-of-sight communication is allowed) can cooperate by helping each other to achieve their own goals.

Keywords

Navigation algorithmsMulti-robot explorationMulti-robot cooperation
Type
Research Article
Information
Robotica , Volume 21 , Issue 5 , October 2003 , pp. 461 - 473
Copyright
Copyright © Cambridge University Press 2003

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. Arkin, R.C., “Motor Schema-Based Mobile Robot Navigation”, Int. J. Robotics Research 8, No. 4, 92–112 (August, 1989).CrossRefGoogle Scholar
2. Gat, E., “Integrating planning and reacting in a heterogeneous asynchronous architecture for controlling real-world mobile robots”, Proc. AAAI-92, San Jose, CA (1992), pp. 809–815.Google Scholar
3. Evans, J.M., “HelpMate: a Service Robot Success Story”, ServiceRobot: An International Journal 1, No. 1, 19–21 (1995).Google Scholar
4. Everett, H.R. and Gage, D.W., “A Third Generation Security Robot”, Proc. SPIE Mobile Robot and Automated Vehicle Control Systems, Boston, MA (1996) Vol. 2903, pp. 20–21.Google Scholar
5. Blitch, J., “Tactical Mobile Robots for Complex Urban Environment”, Proc. Mobile Robots XIV, Boston, MA (September, 1999), pp. 116–128.CrossRefGoogle Scholar
6. Koren, Y. and Borenstein, J., “Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation”, Proceedings of the IEEE International Conference on Robotics and Automation, Sacramento, California (April 7–12, 1991), pp. 1398–1404.Google Scholar
7. Lumelsky, V.J. and Skewis, T., “Incorporating range sensing in the robot navigation function”, IEEE Trans. On System Man and Cybernetics 20(5), 1058–1068 (1990).CrossRefGoogle Scholar
8. Balch, T. and Arkin, R.C., “Communication in Reactive Multiagent Robotic Systems”, Autonomous Robots 1, No. 1, 27–52 (1994).CrossRefGoogle Scholar
9. Itti, L. and Koch, C., “A saliency-based search mechanism for overt and covert shifts of visual attention”, Vision Research 40, 1489–1506 (2000).CrossRefGoogle ScholarPubMed
10. Olshausen, B. and Koch, C., “Selective Visual Attention”, In: The Handbook of Brain Theory and Neural Networks (ed. M. Arbib) (MIT Press, Cambridge, MA, 1995), pp. 837–840.Google Scholar
11. Parker, L.E., “Cooperative Robotics for Multi-Target Observation”, Intelligent Automation and Soft Computing (Special issue on Robotics Research at Oak Ridge National Laboratory 5(1), 5–19, 1999).CrossRefGoogle Scholar
12. Kurazume, R. and Hirose, S., “Study on Cooperative Positioning System – Optimum Moving Strategies for CPS-III”, Proc. IEEE Int. Conf. On Robotics and Automation (1998), Vol. 4, pp. 2896–2903.10.1109/ROBOT.1998.680642CrossRefGoogle Scholar
13. Dudek, G., Jenkin, M., Milios, E. and Wilkes, D., “Experiments in sensing and communication for robot convoy navigation”, Proceedings IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Pittsburgh, PA (August, 1995), Vol. 2, pp. 268–273.Google Scholar
14. O’Rourke, J., Art Gallery Theorems and Algorithms (Oxford University Press, New York, NY, 1987).Google Scholar
15. Guibas, L.J., Latombe, J.C., LaValle, S.M., Lin, D. and Motwani, R., “A Visibility-Based Pursuit-Evasion Problem”, Int. J. of Computational Geometry & Applications 9, Nos. 4 & 5, 471–493 (1999).CrossRefGoogle Scholar
16. Arkin, R.C., Collins, T.R. and Endo, Y., “Tactical Mobile Robot Mission Specification and Execution”, Proc. Mobile Robots XIV, Boston, MA (Sept. 1999), pp. 150–163.CrossRefGoogle Scholar
17. Balch, T. and Arkin, R.C., “Avoiding the Past: A Simple but Effective Strategy for Reactive Navigation”, Proc. 1993 IEEE International Conference on Robotics and Automation, Atlanta, GA (May, 1993) Vol. 1, pp. 678–685.Google Scholar