Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-04T21:22:40.321Z Has data issue: false hasContentIssue false
  • English
  • Français

Robotic experiments with cooperative Aerobots and underwater swarms

Published online by Cambridge University Press:  01 January 2009

Ehsan Honary ,
Frank McQuade ,
Roger Ward ,
Ian Woodrow ,
Andy Shaw ,
Dave Barnes  and
Matthew Fyfe
Ehsan Honary*
Affiliation:
SciSys, Clothier Road, Bristol BS4 5SS, UK.
Frank McQuade
Affiliation:
SciSys, Clothier Road, Bristol BS4 5SS, UK.
Roger Ward
Affiliation:
SciSys, Clothier Road, Bristol BS4 5SS, UK.
Ian Woodrow
Affiliation:
Systems Engineering & Assessment (SEA) Ltd., Beckington Castle, Castle Corner, Beckington, Frome BA11 6TB, UK.
Andy Shaw
Affiliation:
SciSys, Clothier Road, Bristol BS4 5SS, UK.
Dave Barnes
Affiliation:
University of Wales Aberystwyth, Computer Science Department, Penglais, Aberystwyth, Ceredigion, SY23 3DB, Wales, UK.
Matthew Fyfe
Affiliation:
Systems Consultants Services (SCS) Limited, Henley-on-Thames, Oxfordshire, England, RG9 2JN.
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

SciSys has been involved in the development of Planetary Aerobots (arial robots) funded by the European Space Agency for use on Mars and has developed image-based localisation technology as part of the activity. However, it is possible to use Aerobots in a different environment to investigate issues regarding robotics behaviour, such as data handling, limited processing power, and limited sensors. This paper summarises the activity where an Aerobot platform was used to investigate the use of multiple autonomous unmanned underwater vehicles (UUVs) by simulating their movement and behaviour. It reports on the computer simulations and the real-world tests carried out and the lessons learned from these experiments.

Keywords

Collective roboticsFormation flyingSwarmsAerobotUnderwater robotics
Type
Article
Information
Robotica , Volume 27 , Issue 1 , January 2009 , pp. 37 - 49
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.Werger, B. B., “Cooperation without deliberation: A minimal behavior-based approach to multi-robot teams,” Art. Intell. 110, 293–320 (1999).CrossRefGoogle Scholar
2.Melhuish, C. R., “Strategies for collective minimalist mobile robots,” Ph.D. Thesis (Bristol, UK: University of West of England, 1999).Google Scholar
3.Healey, A. J., “Application of formation control for multi-vehicle robotic minesweeping,” Proceedings of the IEEE CDC Conference, Paper No. CDC01-INV3103 (2003).Google Scholar
4.Honary, E., “Flock distortion: A collective approach to 3D trajectory mapping” Ph.D. Thesis (Bristol, UK: University of West of England, 2004).Google Scholar
5.Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm Intelligence: From Natural to Artificial Systems (Oxford: Oxford University Press, 1999).CrossRefGoogle Scholar
6.Rekleitis, I., Lee-Shue, V., New, A. P. and Choset, H., “Limited communication, multi-robot team based coverage,” Proceedings of the 2004 Conference on Robotics & Automation, New Orleans, LA (Apr. 2004).CrossRefGoogle Scholar
7.Sheng, W., Yang, Q., Tan, J. and Xi, N., “Risk and efficiency: A distributed bidding algorithm for multi-robot coordination,” 5th World Congress on Intelligent Control and Automation, Hangzhou, China (2004).Google Scholar
8.Rekleitis, I. M., Dudek, G. and Milios, E., “Multi-robot exploration of an unknown environment, efficiently reducing the odometry error,” Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), vol. 2, Nagoya, Japan (1997).Google Scholar
9.Tan, J. and Xi, N., “Peer-to-peer model for the area coverage and cooperative control of mobile sensor networks,” Proceedings of SPIE Symposium on Defense and Security, Orlando, FL, USA (2004).CrossRefGoogle Scholar
10.Fredslund, J. and Mataric, M., “A general algorithm for robot formations using local sensing and minimal communication,” IEEE Trans. Robot. Autom. 18 (5), 837–846, (Oct. 2002).CrossRefGoogle Scholar
11.Wessnitzer, J., “Strategies for structural self-organisation in wireless networks of mobile robots,” Ph.D. Thesis (Bristol, UK: University of the West of England, 2004).Google Scholar
12.Balch, T. and Arkin, R. C., “Behaviour-based formation control for multi-robot teams,” IEEE Trans. Robot. Autom. 14 (6), 926–939 (1998).CrossRefGoogle Scholar
13.Fiorelli, E., Leonard, N. E., Bhatta, P., Paley, D., Bachmayer, R. and Fratantoni, D. M., “Multi-AUV control and adaptive sampling in Monterey Bay”, Proceedings of IEEE Autonomous Underwater Vehicles 2004: Workshop on Multiple AUV Operations (AUV2004), Sebasco, ME (June 2004).CrossRefGoogle Scholar
14.Reynolds, C. W., “Flocks, herds, and schools: A distributed behavioral model,” Comput. Graph. (SIGGRAPH '87 Conference Proceedings) 21 (4), 25–34 (1987).CrossRefGoogle Scholar
15.Rosenblatt, J., “DAMN: A Distributed Architecture for Mobile Navigation,” AAAI Spring Symposium on Software Architectures for Physical Agents, Stanford, CA, (1995).Google Scholar
16.Barnes, D., Shaw, A., Summers, P., Ward, R., Woods, M., Evans, M., Paar, G. and Sims, M., “Imaging and localisation software demonstrator for Planetary Aerobots,” Acta Astronautica, 59 (8–11), 1062–1070 (October to December 2006).CrossRefGoogle Scholar