Abstract
The improvement of odometry systems in collective robotics remains an important challenge for several applications. In this work, we propose a localisation strategy in which robots have no access to centralised information. Each robot has an estimate of its own location and an associated confidence level that decreases with distance travelled. Robots use estimates advertised by neighbouring robots to correct their own location estimates at each time-step. This simple online social form of odometry is shown to allow a group of robots to both increase the quality of individuals’ estimates and efficiently improve their collective performance. Furthermore, social odometry produces a successful self-organised collective pattern.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wang, C.M.: Location estimation and uncertainty analysis for mobile robots. Autonomous Robot Vehicles 1(1), 90–95 (1990)
Larsen, T., Bak, M., Andersen, N., Ravn, O.: Location estimation for autonomously guided vehicle using an augmented Kalman filter to autocalibrate the odometry. In: FUSION 1998 Spie Conference, pp. 33–39. CSREA Press, Las Vegas (1998)
Thrun, S., Burgard, W., Fox, D.: A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 321–328. Robotics and Automation Society, NJ (2000)
Kurazume, R., Hirose, S.: An experimental study of a cooperative positioning system. Autonomous Robots 8(1), 43–52 (2000)
Grabowski, R., Navarro-Serment, L., Paredis, C., Khosla, P.: Heterogeneous teams of modular robots for mapping and exploration. Autonomous Robots 8(2), 293–308 (2000)
Nouyan, S., Campo, A., Dorigo, M.: Path formation in a robot swarm: Self-organized strategies to find your way home. Swarm Intelligence 2(1), 1–23 (2008)
Vaughan, R., Stoy, K., Sukhatme, G., Matarić, M.: LOST: Localization-space trails for robot teams. IEEE Transactions on Robotics and Automation 18(5), 796–812 (2002)
Balch, T.: Reward and diversity in multirobot foraging. In: IJCAI-1999 Workshop on Agents Learning About, From and With other Agents, pp. 15–21. Morgann Kaufman, San Francisco (1997)
Klarer, P.: Simple 2-d navigation for wheeled vehicles. Technical report, Sandia Report SAND88-0540, Sandia National Laboratories, Livermore, CA (1988)
Feng, L., Borenstein, J., Everett, H.: Where am I? Sensors and Methods for Autonomous Mobile Robot Positioning. University of Michigan Press, Ann Arbor (1994)
Borenstein, J., Feng, L.: Measurement and correction of systematic odometry errors in mobile robots. IEEE Transactions on Robotics and Automation 12, 869–880 (1999)
Chong, K., Kleeman, L.: Accurate odometry and error modelling for a mobile robot. In: IEEE International Conference on Robotics and Automation, pp. 2783–2788. Robotics and Automation Society, NJ (1997)
Santos, F.C., Pacheco, J.M., Lenaerts, T.: Cooperation prevails when individuals adjust their social ties. PLoS Computational Biology 2(10), e140 (2006)
Goldberg, D., Matarić, M.J.: Interference as a tool for designing and evaluating multi-robot controllers. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence, pp. 637–642. AAAI Press, Menlo Park (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gutiérrez, Á., Campo, A., Santos, F.C., Pinciroli, C., Dorigo, M. (2008). Social Odometry in Populations of Autonomous Robots. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-87527-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87526-0
Online ISBN: 978-3-540-87527-7
eBook Packages: Computer ScienceComputer Science (R0)