Abstract
In this paper, we discuss strengths and limitations of different abstraction levels for distributed robotics experiments. We support the discussion with a concrete case study which has been investigated at four different levels: real robots, embodied simulations, microscopic modeling, and macroscopic modeling. Both modeling methodologies presented represent the collective dynamics of the experiment as a set of stochastic events based on simple geometrical considerations and systematic tests with one or two real robots instead of computing trajectories and sensory information like an embodied simulator would do. The case study we describe is concerned with pulling sticks out of the ground - an action which requires the collaboration of two robots to be successful. Experiments were carried out with teams consisting of two to 24 individuals endowed with simple reactive controllers. In addition to showing that models can deliver both qualitatively and quantitatively correct predictions in time lapses that are three or four orders of magnitude smaller than those required by embodied simulations, we discuss differences, assumptions, and subtle numerical effects of the current modeling methodologies.
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References
Bonabeau, E., Dorigo, M., Theraulaz, G.(1999) Swarm Intelligence: From Natural to Artificial Systems. SFI Studies in the Science of Complexity, Oxford University Press, New York, NY
Martinoli, A., Ijspeert, A. J., Gambardella, L. M. (1999) A Probabilistic Model for Understanding and Comparing Collective Aggregation Mechanisms. In: Floreano, D., Mondada, F., Nicoud, J.-D. (Eds.) Proc. of the Fifth European Conf. on Artificial Life, September, Lausanne, Switzerland, Lectures Notes in Computer Science, 575–584
Ijspeert, A. J., Martinoli, A., Billard, A., Gambardella, L.M. (2001) Collaboration through the Exploitation of Local Interactions in Autonomous Collective Robotics: The Stick Pulling Experiment. Autonomous Robots 11(2), 149–171
Agassounon, W., Martinoli, A., Goodman, R. M. (2001) A Scalable, Distributed Algorithm for Allocating Workers in Embedded Systems. In: Proc. of the IEEE Conf. on System, Man and Cybernetics SMC-01, October 2001, Tucson, AR, USA, 3367–3373
Lerman, K., Galstyan, A., Martinoli, A., Ijspeert, A. J. (2001) A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems. Artificial Life 7(4), 375–393
Sugawara, K., Sano, M. (1997) Cooperative Acceleration of Task Performance: Foraging Behavior of Interacting Multi-Robots System. Physica D 100, 343–354
Martinoli, A., Mondada, F. (1995) Collective and Cooperative Group Behaviours: Biologically Inspired Experiments in Robotics. In: Khatib, O., Salisbury, J. K. (Eds.) Proc. of the Fourth Int. Symp. on Experimental Robotics, Stanford, June, Lecture Notes in Control and Information Sciences, 3–10
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© 2003 Springer-Verlag Berlin Heidelberg
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Martinoli, A., Easton, K. (2003). Modeling Swarm Robotic Systems. In: Siciliano, B., Dario, P. (eds) Experimental Robotics VIII. Springer Tracts in Advanced Robotics, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36268-1_26
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DOI: https://doi.org/10.1007/3-540-36268-1_26
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