Abstract
Embryonic development of multicellular organisms, also known as morphogenesis, is regarded as a robust self-organization process for pattern generation. Inspired by the recent findings in biology indicating that morphogen gradients, together with a Gene Regulatory Network (GRN), play a key role in biological patterning, we propose a framework for self-organized multirobot pattern formation and boundary coverage based on an artificial GRN model. The proposed framework does not need a global coordinate system, which makes it more practical to be implemented in a physical robotic system. Moreover, an adaptation mechanism is included in the framework so that the self-organization algorithm is robust to changes in the number of robots. Various case studies of multirobot pattern formation and boundary coverage show the effectiveness of the framework.
- Alon, U. 2006. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC.Google Scholar
- Bahceci, E., Soysal, O., and Sahin, E. 2003. A review: Pattern formation and adaptation in multi-robot systems. Tech. rep. CMU-RI-TR-03-43, Robotics Institute, Carnegie Mellon University. October.Google Scholar
- Ben-Amor, H., Cadau, S., Elena, A., Dhouailly, D., and Demongeot, J. 2009. Regulatory networks analysis: Robustness in morphogenesis. In Proceedings of the International Advanced Information Networking and Application Workshops. 924--928. Google Scholar
Digital Library
- Bloom, R., Chang, C., and Kondacs, A. 2003. Compilation and biological-inspired self-assembly of two-dimensional shapes. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'03). 633--638.Google Scholar
- Bohm, W., Farin, G., and Kahmann, J. 1984. A survey of curve and surface methods in CAGD. Comput.-Aid. Geom. Des. 1, 1, 1--60. Google Scholar
Digital Library
- Boonpinon, N. and Sudsang, A. Heterogeneity driven circular formation. In Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO'06). 971--976.Google Scholar
- Chen, S. and Song, Q. 2005. Perimeter-Based defense against high bandwidth DDoS attacks. IEEE Trans. Parallel Distrib. Syst. 16, 6, 526--537. Google Scholar
Digital Library
- Chen, Y. and Tian, Y. 2009. A backstepping design for directed formation control of three-coleader agents in the plane. Int. J. Robust Nonlin. Control 19, 7, 729--745.Google Scholar
Cross Ref
- Choi, J. and Kim, Y. 2007. Fuel efficient three dimensional controller for leader-follower UAV formation flight. In Proceedings of the International Conference on Control, Automation and Systems (ICCAS'07). 806--811.Google Scholar
- Choset, H. 2001. Coverage for robotics- A survey of recent results. Ann. Math. Artif. Intell. 31, 113--126. Google Scholar
Digital Library
- Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolut. Comput. 6, 2, 182--197. Google Scholar
Digital Library
- De Jong, H. 2002. Modeling and simulation of genetic regulatory systems: A literature review. J. Comput. Biol. 9, 1, 67--103.Google Scholar
Cross Ref
- Easton, K. and Burdick, J. 2005. A coverage algorithm for multirobot boundary inspection. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'05). 727--734.Google Scholar
- Ekanayake, S. W. and Pathirana, P. N. 2007. Geometric formations in swarm aggregation: An artificial formation force based approach. In Proceedings of the 3rd International Conference on Information and Automation for Sustainability. 82--87.Google Scholar
- Endy, D. and Brent, R. 2001. Modeling cellular behavior. Nature 409, 391--395.Google Scholar
Cross Ref
- Francis, P. 2006. An ip perimeter defense architecture. Tech. rep. cul.cis/TR2006-2060, Cornell University, Ithaca, New York.Google Scholar
- Guo, H., Meng, Y., and Jin, Y. 2009a. Self-Adaptive multirobot construction using gene regulatory networks. In Proceedings of the IEEE Symposium on Artificial Life (ALIFE'09). 53--60.Google Scholar
- Guo, H., Meng, Y., and Jin, Y. 2009b. A cellular mechanism for multirobot construction via evolutionary multi-objective optimization of a gene regulatory network. Biosyst. 98, 3, 193--203.Google Scholar
Cross Ref
- Guo, Y., Parker, L., and Madhavan, R. 2004. Towards collaborative robots for infrastructure security applications. In Proceedings of the International Symposium on Collaborative Technologies and Systems. 235--240.Google Scholar
- Hasty, J., McMillen, D., Isaacs, F., and Collins, J. J. 2001. Computational studies of gene regulatory networks. In numero molecular biology. Nat. Rev. Genet. 2, 268--279.Google Scholar
Cross Ref
- Hazon, N., Mieli, F., and Kaminka, G. 2006. Towards robust online multirobot coverage. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'06). 1710--1715.Google Scholar
- Hsieh, M., Kumar, V., and Chainowicz, L. 2008. Decentralized controllers for shape generation with robotic swarms. Robotica 26, 5, 691--701. Google Scholar
Digital Library
- Hughes-Hallett, D., Lock, P. F., Gleason, A. M., Flath, D. E., Gordon, S. P., Lomen, D. O., et al. 2005. Applied Calculus, 3rd ed. John Wiley and Sons.Google Scholar
- Jadbabaie, A., Lin, J., and Morse, A. S. 2003. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Autom. Control 48, 6, 988--1001.Google Scholar
Cross Ref
- Jin, Y., Guo, H., and Meng, Y. 2009. Robustness analysis and failure recovery for a bio-inspired self-organizing multi-robot system. In Proceedings of the 3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO'09). 154--164. Google Scholar
Digital Library
- Kanjanawanishkul, K. and Zell, A. 2008. A model-predictive approach to formation control of omni-directional mobile robots. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'08). 2771--2776.Google Scholar
- Kelly, I. and Martinoli, A. 2004. A scalable, on-board localization and communication system for indoor multirobot experiments. Sensor Rev. Bradford 24, 2, 167--182.Google Scholar
Cross Ref
- Kelly, K. 1994. Out of Control- The New Biology of Machines. Basic Books, New York. Google Scholar
Digital Library
- Khalil, K. 2002. Nonlinear Systems, 3rd ed. Prentice Hall.Google Scholar
- Mai, C. and Lian, F. 2006. Analysis of formation control and communication pattern in multirobot systems. In Proceedings of the SICE-ICASE International Joint Conference. 640--645.Google Scholar
- Mamei, M., Vasirani, M., and Zambonelli, F. 2005. Self-Organizing spatial shapes in mobile particles: The TOTA approach. In Engineering Self-Organizing Systems. Lecture Notes in Computer Science, vol. 3464. Springer, 138--153. Google Scholar
Digital Library
- Martin, F., Perez-Garijo, A., Moreno, E., and Morata, G. 2004. The brinker gradient controls wing growth in Drosophila. Devel. 131, 4921--4930.Google Scholar
- Masoud, A. 2007. Decentralized self-organizing potential field-based control for individually motivated mobile agents in a cluttered environment: A vector-harmonic potential field approach. IEEE Trans. Syst. Man Cybernet. A37, 3, 372--390. Google Scholar
Digital Library
- McAdams, H. and Arkin, A. 1998. Simulation of prokaryotic genetic circuits. Ann. Rev. Biophys. Biomol. Struct. 27, 199--224.Google Scholar
Cross Ref
- Muller, B., Hartmann, B., Pyrowolakis, G., Affolter, M., and Basler, K. 2003. Conversion of an extracellular Dpp/BMP morphogen gradient into an inverse transcriptional gradient. Cell 113, 221--233.Google Scholar
Cross Ref
- Piegl, L. 1991. On NURBS: A survey. IEEE Comput. Graph. Appl. 11, 1, 55--71. Google Scholar
Digital Library
- Shao, J., Xie, G., Yu, J., and Wang, L. 2005. A tracking controller for motion coordination of multiple mobile robots. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'05). 783--788.Google Scholar
- Shen, W. M., Will, P., Galstyan, A., and Chuong, C. M. 2004. Hormone inspired self-organization and distributed control of robotic swarms. Auton. Robots 17, 93--105. Google Scholar
Digital Library
- Smolen, P., Baxter, D. A., and Byrne, J. H. 2000. Modeling transcriptional control in gene networks: Methods, recent results, and future directions. Bull. Math. Biol. 62, 247--292.Google Scholar
Cross Ref
- Spencer, F., Hoffman, F. M., and Gelbart, W. M. 1982. Decapentaplegic: A gene complex affecting morphogenesis in Drosophila melanogaster. Cell 28, 451--461.Google Scholar
Cross Ref
- Taylor, T. 2004. A genetic regulatory network-inspired real-time controller for a group of underwater robots. In Proceedings of the 8th Conference on Intelligent Autonomous Systems (IAS-8). 403--412.Google Scholar
- Teleman, A. and Cohen, S. M. 2000. Dpp gradient formation in the Drosophila wing imaginal disc. Cell 103, 971--980.Google Scholar
Cross Ref
- Turing, A. 1952. The chemical basis of morphogenesis. Bull. Math. Biol. 52, 153--197.Google Scholar
Cross Ref
- Whitcomb, L. and Koditschek, D. E. 1991. Automatic assembly planning and control via potential functions. In Proceedings of the IEEE/RSJ International Workshops on Intelligent Robots and Systems (IROS'91). 17--23.Google Scholar
- Wikipedia. 2012. en.wikipedia.org/wiki/GradientGoogle Scholar
- William, M., Heil, R., and Zarzhitsky, D. 2005. Artificial physics for mobile robot formations. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 3. 2287--2292.Google Scholar
- Williams, K. and Burdick, J. 2006. Multirobot boundary coverage with plan revision. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'06). 1716--1723.Google Scholar
Index Terms
A morphogenetic framework for self-organized multirobot pattern formation and boundary coverage
Recommendations
A morphogenetic approach to flexible and robust shape formation for swarm robotic systems
Embryonic development of multi-cellular organisms is governed by gene regulatory networks (GRNs), which are a collection of genes that interact with one another and with other chemicals in the cell. Inspired by the morphogenesis of biological organisms, ...
Morphogenetic Robotics: An Emerging New Field in Developmental Robotics
Developmental robotics is also known as epigenetic robotics. We propose in this paper that there is one substantial difference between developmental robotics and epigenetic robotics, since epigenetic robotics concentrates primarily on modeling the ...
Morphogenesis through moving membranes
We present a methodology for the modelling of spatially-aware biological phenomena, based on the description of the movement of membranes in the Euclidean space. The time evolution of the system is described by an iterative algorithm, which determines ...






Comments