Punctuated anytime learning to evolve robot control for area coverage

Studies in Computational Intelligence

Volumn 177, Issue , 2009, Pages 255-277

  Parker, Gary B ^a  

^a The Holleran Center for Community Action and Public Policy   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 78049237761     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-89933-4_13     Document Type: Article

References (32)

1. Jakobi, N.: Half-baked, ad-hoc and noisy: minimal simulations for evolutionary robotics. In: Advances in artificial life: Proceedings of the 4th european conference on artificial life (1997)
2. Schultz, A., Grefenstette, J., Adams, W.: RoboShepherd: learning a complex behavior. In: Proceedings of the robots and learning workshop (RoboLearn) (1996)
3. Lee, W.-P., Hallam, J., Lund, H.: Applying genetic programming to evolve behavior primitives and arbitrators for mobile robots. In: Proceedings of IEEE 4th international conference on evolutionary computation (1997)
4. Harvey, I.: Artificial evolution and real robots. In: Proceedings of international symposium on artificial life and robotics (AROB), pp. 138-141 (1996)
5. Mondada, F., Floreano, D.: Evolution of neural control structures: some experiments on mobile robots. Robotics and autonomous systems 16 (1995)
6. Mahdavi, S., Bentley: An evolutionary approach to damage recovery of robot motion with muscles. In: Seventh European conference on artificial life (2003)
7. Lund, H., Miglino, O.: From simulated to real robots. In: Proceedings of IEEE 3rd international conference on evolutionary computation (1996)
8. Miglino, O., Lund, H., Nolfi, S.: Evolving mobile robots in simulated and real environments. Technical report, Institute of Psychology, CNR, Rome (1995)
9. Floreano, D., Urzelai, J.: Evolution of plastic control networks. Autonomous robots 11(3), 311-317 (2001)
10. Watson, R., Ficici, S., Pollack, J.: Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robotics and autonomous systems 39(1), 1-18 (2002)
11. Husbands, P., Harvey, I., Cliff, D., Miller, G.: Artificial evolution: a new path for artificial intelligence? Brain and cognition 34, 130-159 (1997)
12. Mataric, M., Cliff, D.: Challenges in evolving controllers for physical robots. Evolutional robotics, special issue of robotics and autonomous systems 19(1), 67-83 (1996)
13. Nolfi, S., Florano, D., Miglino, O., Mondada, F.: How to evolve autonomous robots: different approaches in evolutionary robotics. In: Proceedings of the international workshop on the synthesis and simulation of living systems (1994)
14. Zelinsky, A., Jarvis, R., Byrne, J., Yuta, S.: Planning paths of complete coverage of an unstructured environment by a mobile robot. In: Proceedings of international conference on advanced robotics, pp. 533-538 (1993)
15. Choset, H., Pignon, P.: Coverage path planning: the boustrophedon cellular decomposition. In: Proceedings of the international conference on field and service robotics (1997)
16. Acar, E., Zhang, Y., Choset, H., Schervish, M., Costa, A., Melamud, R., Lean, D., Graveline, A.: Path planning for robotic demining and development of a test platform. In: Proceedings of the 3rd international conference on field and service robots (FSR), pp. 161-168 (2001)
17. Acar, E., Choset, H., Rizzi, A., Atkar, P., Hull, D.: Morse decompositions for coverage tasks. The international journal of robotics research 21(4) (2002)
18. Ollis, M., Stentz, A.: Vision-based perception for an autonomous harvester. In: Proceedings of the IEEE/RSJ international conference on intelligent robotic systems (1997)
19. Hofner, C., Schmidt, G.: Path planning and guidance techniques for autonomous mobile cleaning robot. Robotics and autonomous systems 14 (1995)
20. Icking, C., Kamphans, T., Klein, R., Langetepe, E.: Exploring simple grid polygons. In: Proceedings of the 11th international computing and combinatorics conference (2005)
21. Jager, M., Nebel, B.: Dynamic decentralization area partitioning for cooperating cleaning robots. In: IEEE international conference on robotics and automation (ICRA 2002) (2002)
22. Parker, G.: Evolving gaits for hexapod robots using cyclic genetic algorithms. International journal of general systems 34(3) (June 2005)
23. Parker, G., Rawlins, G.: Cyclic genetic algorithms for the locomotion of hexapod robots. In: Proceedings of the world automation congress (WAC 1996), Robotic and Manufacturing Systems, vol. 3, pp. 617-622 (1996)
24. Holland, J.: Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor (1975)
25. Parker, G.: Evolving Cyclic control for a hexapod robot performing area coverage. In: Proceedings of 2001 IEEE international symposium on computational intelligence in robotics and automation (CIRA 2001), pp. 561-566 (2001)
26. Dean, T., Boddy, M.: An analysis of time-dependent planning. In: Proceedings of the seventh national conference on AI (AAAI 1988) (1988)
27. Grefenstette, J., Ramsey, C.: An approach to anytime learning. In: Proceedings of the ninth international conference on machine learning (1992)
28. Lund, H., Cuenca, V., Hallam J.: A simple real-time mobile robot tracking system. Technical paper 41, Dept of AI, University of Edinburgh (1996)
29. Parker, G., Mills, J.: Adaptive hexapod gait control using anytime learning with fitness biasing. In: Proceedings of the genetic and evolutionary computation conference (GECCO 1999), pp. 519-524 (1999)
30. Parker, G.: Fitness biasing to produce adaptive gaits for hexapod robots. In: Proceedings of the 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS 2004), pp. 2843-2848 (2004)
31. Parker, G.: Co-Evolving model parameters for anytime learning in evolutionary robotics. Robotics and autonomous systems 33(1) (October 31, 2000)
32. Goldberg, D., Deb, K., Korb, B.: Don't worry, be messy. In: Proceedings of the fourth international conference in genetic algorithms and their applications (1991)