The Importance of Information Flow Regulation in Preferentially Foraging Robot Swarms
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Instead of committing to the first source of reward that it discovers, an agent engaged in “preferential foraging” continues to choose between different reward sources in order to maximise its foraging efficiency. In this paper, the effect of preferential source selection on the performance of robot swarms with different recruitment strategies is studied. The swarms are tasked with foraging from multiple sources in dynamic environments where worksite locations change periodically and thus need to be re-discovered. Analysis indicates that preferential foraging leads to a more even exploitation of resources and a more efficient exploration of the environment provided that information flow among robots, that results from recruitment, is regulated. On the other hand, preferential selection acts as a strong positive feedback mechanism for favouring the most popular reward source when robots exchange information rapidly in a small designated area, preventing the swarm from foraging efficiently and from responding to changes.
This work was supported by EPSRC grants EP/G03690X/1, EP/N509747/1 and EP/R0047571.
- 1.Bonani, M., et al.: The MarXbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In: Proceedings of 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), pp. 4187–4193. IEEE, Piscataway (2010)Google Scholar
- 9.Hecker, J.P., Moses, M.E.: Beyond pheromones: evolving error-tolerant, flexible, and scalable ant-inspired robot swarms. Swarm Intell. 9, 43–70 (2015)Google Scholar
- 10.Hoff, N., Sagoff, A., Wood, R.J., Nagpal, R.: Two foraging algorithms for robot swarms using only local communication. In: Proceedings of the 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO 2010), pp. 123–130. IEEE, Piscataway (2010)Google Scholar
- 11.Hrolenok, B., Luke, S., Sullivan, K., Vo, C.: Collaborative foraging using beacons. In: van der Hoek, W., Kaminka, G.A., Lesperance, Y., Luck, M., Sen, S. (eds.) Proceedings of 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 1197–1204. IFAAMAS, Richland (2010)Google Scholar
- 12.Jones, C., Mataric, M.J.: Adaptive division of labor in large-scale minimalist multi-robot systems. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 2, pp. 1969–1974. IEEE, Piscataway (2003)Google Scholar
- 21.Pitonakova, L., Crowder, R., Bullock, S.: Behaviour-data relations modelling language for multi-robot control algorithms. In: Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), pp. 727–732. IEEE, Piscataway (2017)Google Scholar
- 24.Sarker, M.O.F., Dahl, T.S.: Bio-Inspired communication for self-regulated multi-robot systems. In: Yasuda, T. (ed.) Multi-Robot Systems, Trends and Development, pp. 367–392. InTech (2011)Google Scholar
- 29.Valentini, G., Hamann, H., Dorigo, M.: Self-organized collective decision making: the weighted voter model. In: Proceedings of 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), pp. 45–52. ACM, New York (2014)Google Scholar
- 30.Wawerla, J., Vaughan, R.T.: A fast and frugal method for team-task allocation in a multi-robot transportation system. In: Proceedings of 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), pp. 1432–1437. IEEE, Piscataway (2010)Google Scholar