Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-23T23:55:58.369Z Has data issue: false hasContentIssue false
  • English
  • Français

Efficient cooperative search of smart targets using UAV Swarms1

Published online by Cambridge University Press:  01 July 2008

Yaniv Altshuler ,
Vladimir Yanovsky ,
Israel A. Wagner  and
Alfred M. Bruckstein
Yaniv Altshuler*
Affiliation:
Computer Science Department, Technion, Haifa 32000Israel
Vladimir Yanovsky
Affiliation:
Computer Science Department, Technion, Haifa 32000Israel
Israel A. Wagner
Affiliation:
Computer Science Department, Technion, Haifa 32000Israel IBM Haifa Labs, MATAM, Haifa 31905Israel
Alfred M. Bruckstein
Affiliation:
Computer Science Department, Technion, Haifa 32000Israel
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This work examines the Cooperative Hunters problem, where a swarm of unmanned air vehicles (UAVs) is used for searching one or more “evading targets,” which are moving in a predefined area while trying to avoid a detection by the swarm. By arranging themselves into efficient geometric flight configurations, the UAVs optimize their integrated sensing capabilities, enabling the search of a maximal territory.

Keywords

Swarm AlgorithmCooperative SearchShape Factor
Type
Article
Information
Robotica , Volume 26 , Special Issue 4: Geometry in Robotics: Sensing , July 2008 , pp. 551 - 557
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.)

Footnotes

1

This research has been supported in part by the Ministry of Science Infrastructural Grant No. 3-942 and the Devorah fund and by the Russel–Berrie Nanotechnology Institute (RBNI).

References

1.Vincent, P. and Rubin, I., “A Framework and Analysis for Cooperative Search Using UAV Swarms,” In: ACM Symposium on Applied Computing, Nicosia, Cyprus (2004) pp. 79–86.Google Scholar
2.Hettiarachchi, S. and Spears, W., “Moving Swarm Formations Through Obstacle Fields,” In: Proceedings of the International Conference on Artificial Intelligence Las Vegas, Nevada, USA. (2005), pp. 97–103.Google Scholar
3.Wagner, I. A. and Bruckstein, A. M., “From Ants to A(ge)nts: A Special Issue on Ant–-Robotics,” Ann. Math. Artif. Intell. (Special Issue on Ant Robotics) 31 (1–4), 1–6 (2001).CrossRefGoogle Scholar
4.Steels, L., “Cooperation Between Distributed Agents Through Self-Organization,” In: Decentralized A.I-Proceedings of the First European Workshop on Modeling Autonomous Agents in Multi-Agents World (DeMazeau, Y. and Muller, J. P., eds.). Elsevier (1990) pp. 175–196.Google Scholar
5.Arkin, R. C., “Integrating Behavioral, Perceptual, and World Knowledge in Reactive Navigation,” Robot. Auton. Syst. 6, 105–122 (1990).Google Scholar
6.Mataric, M. J., “Designing Emergent Behaviors: From Local Interactions to Collective Intelligence,” In: Proceedings of the Second International Conference on Simulation of Adaptive Behavior, Honolulu, Hawaii, (Meyer, J., Roitblat, H., and Wilson, S., eds.). MIT Press (1992) pp. 432–441.Google Scholar
7.Haynes, T. and Sen, S., “Evolving Behavioral Strategies in Predators and Prey,” In: Adaptation and Learning in Multi-Agent Systems (Weiss, Gerard and Sandip, Sen, eds.). Springer (1986) pp. 113–126.Google Scholar
8.Alami, R., Fleury, S., Herrb, M., Ingrand, F. and Robert, F., “Multi-Robot Cooperation in the Martha Project,” IEEE Robot. Autom. Mag. 5 (1), 36–47 (1998).Google Scholar
9.Parker, L. E., “ALLIANCE: An Architecture for Fault-Tolerant Multi-Robot Cooperation,” IEEE Trans. Robot. Autom. 14 (2), 220–240 (1998).CrossRefGoogle Scholar
10.Mataric, M. J., Interaction and Intelligent Behavior, PhD Thesis (Massachusetts Institute of Technology, 1994).CrossRefGoogle Scholar
11.Mac Kenzie, D., Arkin, R. and Cameron, J., “Multiagent Mission Specification and Execution,” Auton. Robots 4 (1), 29–52 (1997).CrossRefGoogle Scholar
12.Candea, C., Hu, H., Iocchi, L., Nardi, D. and Piaggio, M., “Coordinating in Multi-Agent Robocup Teams,” Robot. Autonom. Syst. 36 (2–3), 67–86 (Aug. 2001).CrossRefGoogle Scholar
13.Yamashita, A., Fukuchi, M., Ota, J., Arai, T. and Asama, H., “Motion Planning for Cooperative Transportation of a Large Object by Multiple Mobile Robots in a 3D Environment,” In: Proceedings of IEEE International Conference on Robotics and Automation, San Francisco, California, USA. (2000) pp. 3144–3151.Google Scholar
14.Premvuti, S. and Yuta, S., “Consideration on the Cooperation of Multiple Autonomous Mobile Robots,” In: Proceedings of the IEEE International Workshop of Intelligent Robots and Systems, Tsuchiura, Japan (1990) pp. 59–63.Google Scholar
15.Gordon, N., Wagner, I. A. and Bruckstein, A. M., “Discrete Bee Dance Algorithms for Pattern Formation on a Grid,” In: The Proceedings of IEEE International Conference on Intelligent Agent Technology (IAT03) Halifax, Canada. (Oct. 2003) pp. 545–549.Google Scholar
16.Balch, T. and Arkin, R., “Behavior-Based Formation Control for Multi-Robot Teams,” In: IEEE Transactions on Robotics and Automation (Dec. 1998).Google Scholar
17.Rekleitis, I. M., Dudek, G. and Milios, E., “Experiments in Free-Space Triangulation Using Cooperative Localization,” In: Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS) Las Vegas, Nevada, USA. (2003).Google Scholar
18.Wagner, I. A. and Bruckstein, A. M., “Cooperative Cleaners: A Case of Distributed Ant-Robotics,” In: Communications, Computation, Control, and Signal Processing: A Tribute to Thomas Kailath (Kluwer Academic Publishers, The Netherlands, 1997) pp. 289–308.CrossRefGoogle Scholar
19.Altshuler, Y., Bruckstein, A. M. and Wagner, I. A., “Swarm Robotics for a Dynamic Cleaning Problem,” In: IEEE Swarm Intelligence Symposium 2005, Pasadena, California, USA. pp. 209–216.Google Scholar
20.Gerkey, B. P. and Mataric, M. J., “Sold! Market Methods for Multi-Robot Control,” In: IEEE Transactions on Robotics and Automation, Special Issue on Multi-robot Systems (2002).Google Scholar
21.Rabideau, G., Estlin, T., Chien, T. and Barrett, A., “A Comparison of Coordinated Planning Methods for Cooperating Rovers,” In: Proceedings of the American Institute of Aeronautics and Astronautics (AIAA) Space Technology Conference, Barcelona, Spain (1999).Google Scholar
22.Thayer, S. M., Dias, M. B., Digney, B. L., Stentz, A., Nabbe, B. and Hebert, M., “Distributed Robotic Mapping of Extreme Environments,” In: Proceedings of SPIE, Vol. 4195, Mobile Robots XV and Telemanipulator and Telepresence Technologies VII (2000).Google Scholar
23.Wellmanand, M. P. and Wurman, P. R., “Market-Aware Agents for a Multiagent World,” Robotics Autonom. Syst. 24, 115–125 (1998).CrossRefGoogle Scholar
24.Chevallier, D. and Payandeh, S., “On Kinematic Geometry of Multi-Agent Manipulating System Based on the Contact Force Information,” In: The 6th International Conference on Intelligent Autonomous Systems (IAS-6), Venice, Italy (2000) pp. 188–195.Google Scholar
25.Beni, G. and Wang, J., “Theoretical Problems for the Realization of Distributed Robotic Systems,” In: Proceedings of 1991 IEEE Internal Conference on Robotics and Automation, Sacramento, CA (Apr. 1991) pp. 1914–1920.CrossRefGoogle Scholar
26.Shucker, B. and Bennett, J. K., “Target Tracking With Distributed Robotic Macrosensors,” In: Military Communications Conference 2005 (MILCOM 2005), Atlantic City, New Jersey, USA. 4 (2005) pp. 2617–2623.Google Scholar
27.Kerr, W. and Spears, D., “Robotic Simulation of Gases for a Surveillance Task,” In: Intelligent Robots and Systems 2005 (IROS 2005), Edmonton, Alberta, Canada. pp. 2905–2910.Google Scholar
28.Rekleitisy, I., Lee-Shuey, V., Newz, A. Peng and Choset, H., “Limited Communication, Multi-Robot Team Based Coverage,” In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation, New Orleans, LA (Apr. 2004).Google Scholar
29.Passino, K., Polycarpou, M., Jacques, D., Pachter, M., Liu, Y., Yang, Y., Flint, M. and Baum, M., “Cooperative Control for Autonomous Air Vehicles,” In: Cooperative Control and Optimization (Murphey, R. and Pardalos, P., eds.) (Kluwer Academic Publishers, Boston, 2002).Google Scholar
30.Stone, L. D., Theory of Optimal Search (Academic Press, New York, 1975).Google Scholar
31.Koopman, B. O., “The Theory of Search II, Target Detection,” Oper. Res. 4, 5, 503–531 (Oct. 1956).Google Scholar
32.Koenig, S. and Liu, Y., “Terrain Coverage With Ant Robots: A Simulation Study,” In: AGENTS'01, Montreal, Quebec, Canada (May 28–June 1, 2001).Google Scholar
33.Svennebring, J. and Koenig, S., “Building Terrain-Covering ant Robots: A Feasibility Study,” Auton. Robots 16 (3), 313– 332 (2004).CrossRefGoogle Scholar
34.Bejar, R., Krishnamachari, B., Gomes, C. and Selman, B., “Distributed Constraint Satisfaction in a Wireless Sensor Tracking System,” In: Proceedings of the IJCAI-01 Workshop on Distributed Constraint Reasoning, Seattle, WA (2001).Google Scholar
35.Kirkpatrick, S. and Schneider, J. J., “How Smart Does an Agent Need to Be?”, Int J. Mod. Phys. C 16, 139–155 (2005).CrossRefGoogle Scholar
36.Dudek, G., Jenkin, M. R. M., Milios, E. and Wilkes, D., “A Taxonomy for Multi-Agent Robotics,” Autonom. Robots J. 3 (4), 375–397 (1996).Google Scholar
37.Alpern, S. and Gal, S., The Theory of Search Games and Rendezvous (Kluwer Academic Publishers, Boston, Massachusetts, 2003).Google Scholar
38.Koopman, B., Search and Screening: General Principles With Historical Applications (New York, Pergamon Press, 1980).Google Scholar
39.Thorndike, A., “Summary of Antisubmarine Warfare Operations in World War II,” NDRC Summary Report (1946).Google Scholar
40.Morse, P. M. and Kimball, G. E., Methods of Operations Research (Cambridge, Massachusetts, MIT Press and New York, Wiley, 1951).Google Scholar