Evolving Error Tolerance in Biologically-Inspired iAnt Robots

Proceedings of the 12th European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL 2013

Volumn , Issue , 2013, Pages 1025-1032

  Hecker, Joshua P ^a   Stolleis, Karl ^a   Swenson, Bjorn ^a   Letendre, Kenneth ^a,b   Moses, Melanie E ^a,b,c  

^a MSC01 1070   (United States)

^b UNIVERSITY OF NEW MEXICO   (United States)

^c SANTA FE INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL SYSTEMS; BIOMIMETICS; ERRORS; GENETIC ALGORITHMS; MULTI AGENT SYSTEMS;

BIOLOGICALLY-INSPIRED; BIOLOGICALLY-INSPIRED ROBOTICS; DETECTION ERROR; ERROR MODELING; ERROR PRONES; ERRORS TOLERANCE; MULTIAGENT TEAMS; PHYSICAL ROBOTS; SENSOR ERRORS; SIMULATED ROBOT;

ROBOTS;

EID: 85046080146     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.7551/978-0-262-31709-2-ch153     Document Type: Conference Paper

References (32)

1. Beverly, B. D., McLendon, H., Nacu, S., Holmes, S., and Gordon, D. (2009). How site fidelity leads to individual differences in the foraging activity of harvester ants. Behavioral Ecology, 20(3):633-638.
2. Bezzo, N., Hecker, J., Stolleis, K., Moses, M., and Fierro, R. (2013). Exploiting Heterogeneous Robotic Systems in Cooperative Missions. Robotica (in review). Brooks, R. A. (1991). New Approaches to Robotics. Science, 253(5025):1227-1232.
3. Brooks, R. A. (1992). Artifical Life and Real Robots. In Proceedings of ECAL, pages 3-10.
4. Curtis, S. A., Truszkowski,W., et al. (2003). ANTS for Human Exploration and Development of Space. In IEEE Aerospace Conference, volume 1, pages 1-161.
5. Flanagan, T. P., Letendre,K., Burnside,W. R., Fricke, G. M., and Moses, M. E. (2012). Quantifying the Effect of Colony Size and Food Distribution on Harvester Ant Foraging. PLoS ONE, 7(7):e39427.
6. Gage, D.W. (1995). Many-RobotMCMSearch Systems. In Autonomous Vehicles in Mine Countermeasures Symposium, number April, pages 4-7.
7. Hecker, J. P., Letendre, K., Stolleis, K., Washington, D., and Moses, M. E. (2012). Formica ex Machina: Ant Swarm Foraging From Physical to Virtual and Back Again. Swarm Intelligence, 7461:252-259.
8. Hecker, J. P. and Moses, M. E. (2013). An Evolutionary Approach for Robust Adaptation of Robot Behavior to Sensor Error. In GECCO 2013 (in press).
9. Jakobi, N. (1997). Half-baked , Ad-hoc and Noisy : Minimal Simulations for Evolutionary Robotics. In Proceedings of ECAL, pages 348-357.MIT Press.
10. Jakobi, N., Husbands, P., et al. (1995). Noise and The Reality Gap : The Use of Simulation in Evolutionary Robotics. Advances in Artificial Intelligence, 704-720.
11. Kitano, H., et al. (1999). Robocup rescue: Search and rescue in large-scale disasters as a domain for autonomous agents research. In IEEE SMC-99 Conference Proceedings, volume 6, pages 739-743. IEEE.
12. Kong, C. S., Peng, N. A., and Rekleitis, I. (2006). Distributed coverage with multi-robot system. In Proceedings of ICRA, numberMay, pages 2423-2429. Ieee.
13. Letendre, K. and Moses, M. E. (2013). Synergy in Ant Foraging Strategies : Memory and Communication Alone and In Combination. In GECCO 2013 (in press).
14. Mallon, E. B. and Franks, N. R. (2000). Ants estimate area using Buffon?s needle. Proceedings of the Royal Society B, 267(1445):765-770.
15. Mataric, M. J. (1994). Learning to Behave Socially. Proceedings of SAB, (617):453-462.
16. Mataric, M. J. (1997a). Behaviour-based control : examples from navigation , learning , group behavior. Journal of Experimental & Theoretical AI, 9(2-3):323-336.
17. Mataric,M. J. (1997b). Reinforcement learning in the multirobot domain. Autonomous Robots, 4:73-83.
18. Miglino, O., Lund, H. H., and Nolfi, S. (1995). Evolving Mobile Robots in Simulated and Real Environments. Artificial Life, 2(4):417-434.
19. Moses, M. E. (2005). Metabolic scaling from individuals to societies. PhD thesis, University of New Mexico.
20. Nelson, A., et al. (2004). Evolution of neural controllers for competitive game playing with teams of mobile robots. Robotics and Autonomous Systems, 46(135-150).
21. Nolfi, S., Floreano, D., et al. (1993). How to evolve autonomous robots: Different approaches in evolutionary robotics. Artificial Life, 4:190-197.
22. Panait, L. and Luke, S. (2004). Learning ant foraging behaviors. Proceedings of ALIFE9.
23. Parker, L. E. (1998). ALLIANCE: An architecture for fault tolerant multirobot cooperation. Robotics and Automation, IEEE Transactions on, 14(2):220-240.
24. Paz Flanagan, T., Letendre, K., Burnside, W., Fricke, G.M., andMoses, M. How Ants Turn Information into Food. IEEE Conference on ALife, pages 178-185, 2011.
25. Pinciroli, C., Trianni, et al. (2011). ARGoS : A Pluggable, Multi-Physics Engine Simulator for Heterogeneous Swarm Robotics. In IEEE/RSJ Proceedings of IROS, number December 2010, pages 5027-5034.
26. Pinciroli, C., Trianni, V., et al. (2012). ARGoS: A modular, parallel, multi-engine simulator for multi-robot systems. Swarm intelligence, 6:271-295.
27. Prabhakar, B., Dektar, K. N., et al. (2012). The Regulation of Ant Colony Foraging Activity without Spatial Information. PLoS Computational Biology, 8(8):e1002670.
28. Singh, M. and Parhi, D. (2011). Path optimisation of a mobile robot using an artificial neural network controller. Int?l Journal of Systems Science, 42(1):107-120.
29. Tunstel, E., Dolan, J., et al. (2008). Mobile Robotic Surveying Performance for Planetary Surface Site Characterization. In Proceedings of PerMIS-08, pages 200-205.
30. Vaughan, R. (2008). Massively multi-robot simulation in stage. Swarm Intelligence.
31. Watson, R. A., et al. (2002). Embodied Evolution: Distributing an evolutionary algorithm in a population of robots. Robotics and Autonomous Systems, 39(1):1-18.
32. Wittlinger, M., et al. (2006). The Ant Odometer: Stepping on Stilts and Stumps. Science, 312(5782):1965-1967.