Robustness analysis and failure recovery of a bio-inspired self-organizing multi-robot system

SASO 2009 - 3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems

Volumn , Issue , 2009, Pages 154-164

  Jin, Yaochu ^a   Guo, Hongliang ^b   Meng, Yan ^b  

^a HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

^b Stevens Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIO-INSPIRED; BIOLOGICAL ORGANISMS; COMPLEX BEHAVIOR; DISTANCE DETECTION; EMPIRICAL ANALYSIS; ENVIRONMENTAL CHANGE; FAILURE RECOVERY; GENE REGULATORY NETWORKS; LOCAL INTERACTIONS; MOVING OBSTACLES; MULTI-ROBOT SYSTEMS; MULTIPLE ROBOT; PARTIAL SYSTEMS; PERFORMANCE VARIATIONS; POSITIONING ERROR; RECOVERY ALGORITHMS; ROBOT SYSTEM; ROBUSTNESS ANALYSIS; SELF ORGANIZING; SELF-LOCALIZATION; SELF-ORGANIZATION PROCESS; SELF-ORGANIZING SYSTEMS; SENSORY NOISE; SHAPE FORMATION; SIMULATION RESULT; SYSTEM'S PERFORMANCE; TRILATERATION;

BIOLOGICAL SYSTEMS; COMPLEXATION; CYBERNETICS; ERROR DETECTION; FAILURE ANALYSIS; INDUSTRIAL ROBOTS; MULTIPURPOSE ROBOTS; QUALITY ASSURANCE; ROBOT LEARNING; SYSTEMS ENGINEERING;

ALGORITHMS;

EID: 73649107656     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SASO.2009.19     Document Type: Conference Paper

References (31)

1. Alon, U.: An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC (2006)
2. Kondacs, A.: Biologically inspired self-assembly of two-dimensional shapes using global to local compilation. In: Int. Joint Conf. on Artificial Intelligence, pp. 633-638 (2003)
3. Cheng, J., Cheng, W., Nagpal, R.: Robust and self-repairing formation control for swarms of mobile agents. In: Twentieth National Conference on Artificial Intelligence. pp. 59-64 (2005)
4. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems, 9:115-148 (1995)
5. de Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology, 9:67-103(2002)
6. Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Robots. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26:29-41 (1996)
7. Endy, D., Brent, R.: Modeling cellular behavior. Nature, 409:391-395 (2001)
8. Geard, N.: Modeling gene regulatory networks: systems biology to complex systems. ACCS Technical Report (2004)
9. Gierer, A.: Generation of biological patterns and form: some physical, mathematical, and logical aspects. Prog. Biophys. Mol. Biol., 37:1-47 (1981)
10. Guo, H., Meng, Y., Jin, Y.: A Cellular Mechanism for Multi-Robot Formations via Evolving Gene Regulatory Networks. BioSystems (2009) (Accepted)
11. Hasty, J. and McMillen, D., Isaacs, F., Collins, J.J.: Computational studies of gene regulatory networks: in numero molecular biology. Nat. Rev. Genet., 2:268-279 (2001)
12. Jatmiko, W., Sekiyama, K., Fukuda, T.: A PSO-based Mobile Robot for Odor Source Localization in Dynamic Advection-Diffusion with Obstacles Environment: Theory, Simulation and Measurement. IEEE Computational Intelligence Magazine, 2:37-51 (2007)
13. Jin, Y., Meng, Y., Sendhoff, B.: Influence of Regulation Logic on the Easiness of Evolving Sustained Oscillation for Gene Regulatory Networks. In: IEEE Symposium on Artificial Life, pp.61-68 (2009)
14. Kauffman, S.A.: The Origins of Order: Self-organization and Selection in Evolution, Oxford University Press, New York (1993)
15. Kitano, K.: Biological robustness. Nature, 5:826-837 (2004)
16. Khoshnevis, B., Bekey, G.A.: Centralized sensing and control of multiple mobile robots. Computers in Industrial Engineering, 35:503-506 (1998)
17. Klavins, E.: Communication Complexity of Multi-Robot Systems. Springer Tracts in Advanced Robotics. 7:275-292 (2003)
18. Kwon, Y.-K., Cho, K.-H.: Analysis of feedback loops and robustness in network evolution based on Boolean models. BMC Bioinformatics, 8:430 (2007)
19. Maini, P., Painter, J.K., Nguyen, P.C.: Spatial pattern formation in chemical and biological systems. J. Chem. Soc., Garaday Trans., 93:3601-3610 (1997)
20. Mamei, M., Vasirani, M., Zambonelli, F.: Experiments in morphogenesis in swarms of simple robots. Applied Artificial Intelligence, 18, 9-10:903-919
21. Meng, Y., Gan, J.: LIVS: Local interaction via virtual stigmergy coordination in distributed search and collective cleanup. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1371-1376 (2007)
22. Meng, Y., Gan, J.: A Distributed Swarm Intelligence based Algorithm for a Cooperative Multi-Robot Construction Task. In: IEEE Swarm Intelligence Symposium, pp. 21-23 (2008)
23. Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines, MIT Press, Cambridge, MA (2000)
24. Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotics. Science, 318:1088-1093 (2007)
25. Pugh, J., Martinoli, A.: Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization, In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium, pp. 332-339 (2007)
26. Shen, W., Will,P., Galstyan, A: Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms, Autonomous Robots, 17:93-105 (2004)
27. Salazar-Ciudad, I., Garcia-Fernandez, H., Sole, R.V.: Gene networks capable of pattern formation: from induction to reaction-diffusion. Journal of Theoretical Biology, 205:587-603 (2000)
28. Smolen, P., Baxter, D.A., Byrne, J.H.: Modeling transcriptional control in gene networks: Methods, recent results, and future directions. Bull. Math. Biol., 62:247-292 (2000)
29. Taylor, T.: A Genetic Regulatory Network-Inspired Real-Time Controller for a Group of Underwater Robots. In: Proceedings of Eighth Conference on Intelligent Autonomous Systems, pp. 403-412 (2004)
30. Turing, A.M.: The chemical basis of morphogenesis. Philos Trans. R. Soc. London B, 237:37-72 (1952)
31. Werfel, J.: Building blocks for multi-robot construction. In: Seventh International Symposium on Distributed Autonomous Robotic Systems, pp. 285-294 (2004)