Frederick Ducatelle
ABSTRACT
We study how a swarm robotic system consisting of two different types of robots can solve a foraging task. The first type of robots are small wheeled robots, called foot-bots, and the second type are flying robots that can attach to the ceiling, called eye-bots. While the foot-bots perform the actual foraging, i.e. they move back and forth between a source and a target location, the eye-bots are deployed in stationary positions against the ceiling, with the goal of guiding the foot-bots. The key component of our approach is a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt the instructions they give. Through a simulation study, we show that this process allows the system to find a path for foraging in a cluttered environment. Moreover, it is able to converge onto the shortest of two paths, and spread over different paths in case of congestion. Since our approach involves mutual adaptation between two sub-swarms of different robots, we refer to it as cooperative self-organization. This is to our knowledge the first work that investigates such a system in swarm robotics.
- M. Batalin and G. Sukhatme. Coverage, exploration and deployment by a mobile robot and communication network. In Proc. of the International Workshop on Information Processing in Sensor Networks, 2003. Google Scholar
Digital Library
- M. Batalin, G. Sukhatme, and M. Hattig. Mobile robot navigation using a sensor network. In Proceedings of IEEE ICRA, 2004.Google ScholarCross Ref
- R. Bellman. On a routing problem. Quarterly of Applied Mathematics, 16(1):87--90, 1958.Google ScholarCross Ref
- E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, 1999. Google ScholarDigital Library
- M. Dorigo and E. Sahin. Guest editorial: Swarm robotics. Autonomous Robotics, 17(2-3), 2004. Google ScholarDigital Library
- A. Dussutour, V. Fourcassié, D. Helbing, and J.-L. Deneubourg. Optimal traffic organization in ants under crowded conditions. Nature, 428:70--73, March 2004.Google ScholarCross Ref
- R. Fujisawa, S. Dobata, D. Kubota, H. Imamura, and F. Matsuno. Dependency by concentration of pheromone trail for multiple robots. In Proceedings of the 6th International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS), 2008. Google ScholarDigital Library
- S. Garnier, F. Tache, M. Combe, A. Grimal, and G. Theraulaz. Alice in pheromone land: An experimental setup for the study of ant-like robots. In Proceedings of the IEEE Swarm Intelligence Symposium (SIS), April 2007.Google ScholarDigital Library
- S. Goss, S. Aron, J. L. Deneubourg, and J. M. Pasteels. Self-organized shortcuts in the Argentine ant. Naturwissenschaften, 76:579--581, 1989.Google ScholarCross Ref
- D. Helbing. Traffic and related self-driven many-particle systems. Reviews of Modern Physics, 73, October 2001.Google ScholarCross Ref
- R. Johansson and A. Saffiotti. Navigation by stigmergy: A realization on an RFID floor for minimalistic robots. In Proceedings of IEEE ICRA, 2009. Google ScholarDigital Library
- M. Mamei and F. Zambonelli. Physical deployment of digital pheromones through RFID technology. In Proceedings of the fourth international joint conference on Autonomous agents and multi-agent systems (AAMAS), pages 1353--1354, 2005. Google ScholarDigital Library
- K. O'Hara and T. Balch. Pervasive sensor-less networks for cooperative multi-robot tasks. In Proceedings of DARS-04, 2004.Google Scholar
- K. O'Hara, V. Bigio, S. Whitt, D. Walker, and T. Balch. Evaluation of a large scale pervasive embedded network for robot path planning. In Proceedings 2006 IEEE International Conference on Robotics and Automation (ICRA), May 2006.Google ScholarCross Ref
- L. Panait and S. Luke. Ant foraging revisited. In Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9), 2004.Google Scholar
- D. Payton, M. Daily, R. Estowski, M. Howard, and C. Lee. Pheromone robotics. Autonomous Robots, 11(3), November 2001. Google ScholarDigital Library
- J. Roberts, T. Stirling, J. Zufferey, and D. Floreano. 2.5d infrared range and bearing system for collective robotics. In IEEE, editor, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2009), St. Louis, October 2009. Google ScholarDigital Library
- T. Sharpe and B. Webb. Simulated and situated models of chemical trail following in ants. In Proceedings of SAB'98, 1998. Google ScholarDigital Library
- T. Sit, Z. Liu, M. Ang Jr., and W. Seah. Multi-robot mobility enhanced hop-count based localization in ad hoc networks. Robotics and Autonomous Systems, 55(3):244--252, March 2007. Google ScholarDigital Library
- R. Smith. Open Dynamics Engine v0.5 User Guide, 2006.Google Scholar
- T. Stirling, S. Wischmann, and D. Floreano. Energy-efficient indoor search by swarms of simulated flying robots without global information. Swarm Intelligence, 4(2):117--144, 2010.Google ScholarCross Ref
- K. Sugawara, T. Kazama, and T. Watanabe. Foraging behavior of interacting robots with virtual pheromone. In Proceedings of IEEE/RSJ IROS, 2004.Google ScholarCross Ref
- Swarmanoid. Final swarmanoid hardware. Deliverable D13 of IST-FET Project Swarmanoid funded by the European Commission under Framework FP6, 2009.Google Scholar
- Swarmanoid. Final swarmanoid simulator. Deliverable D12 of IST-FET Project Swarmanoid funded by the European Commission under Framework FP6, 2009.Google Scholar
- R. Vaughan, K. Støy, G. Sukhatme, and M. Mataric. Whistling in the dark: Cooperative trail following in uncertain localization space. In Proceedings of the Fourth International Conference on Autonomous Agents, 2000. Google ScholarDigital Library
- C. Vigorito. Distributed path planning for mobile robots using a swarm of interacting reinforcement learners. In Proceedings of AAMAS, 2007. Google ScholarDigital Library
- U. Witkowski, M. El-Habbal, S. Herbrechtsmeier, A. Tanoto, J. Penders, L. Alboul, and V. Gazi. Ad-hoc network communication infrastructure for multi-robot systems in disaster scenarios. In Proceedings of the International Workshop on Robotics for Risky Interventions and Surveillance of the Environment, 2008.Google Scholar
- M. Wodrich and G. Bilchev. Cooperative distributed search: The ants' way. Control and Cybernetics, 26, 1997.Google Scholar
Index Terms
- Cooperative self-organization in a heterogeneous swarm robotic system
Recommendations
Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm
This article presents a bio-inspired communication strategy for large-scale robotic swarms. The strategy is based purely on robot-to-robot interactions without any central unit of communication. Thus, the emerging swarm regulates itself in a purely self-...
Self-organized flocking with a mobile robot swarm: a novel motion control method
In flocking, a swarm of robots moves cohesively in a common direction. Traditionally, flocking is realized using two main control rules: proximal control, which controls the cohesion of the swarm using local range-and bearing information about ...
Behavioral specialization emerges from the embodiment of a robotic swarm
AbstractThis paper focuses on the effect of the embodiment of robots on collective behavior in robotic swarms. The research field of swarm robotics emphasizes the importance of the embodiment of robots; however, only a few studies have discussed how it ...
Comments