Abstract
A biologically inspired control algorithm for robot control was introduced in a previous work. The algorithm is robust to noisy sensor information and hardware failures. In this paper a new version of the algorithm is presented. The new version is able to cope with highly non-linear systems and presents an improved robustness to low-pass filter effects and dead-times. Automatic tuning of the parameters is also introduced, providing a completely parameterless algorithm.
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References
Adler, J.: The sensing of chemicals by bacteria. Scientific American 234, 40–47 (1976)
Baker, M.D., Wolanin, P.M., Stock, J.B.: Systems biology of bacterial chemotaxis. Current Opinion in Microbiology 9(2), 187–192 (2006); cell Regulation / Edited by Werner Goebel and Stephen Lory
Benzi, R., Sutera, A., Vulpiani, A.: The mechanism of stochastic resonance. Journal of Physics A: mathematical and general 14, 453–457 (1981)
DallaLibera, F., Ikemoto, S., Minato, T., Ishiguro, H., Menegatti, E., Pagello, E.: Biologically inspired mobile robot control robust to hardware failures and sensor noise. In: Robocup 2010, Singapore (2010)
Dhariwal, A., Sukhatme, G.S., Requicha, A.A.G.: Bacterium-inspired robots for environmental monitoring. In: 2004 IEEE International Conference on Robotics and Automation (ICRA 2004), New Orleans, USA, pp. 1436–1443 (2004)
Gammaitoni, L., Hänggi, P., Jung, P., Marchesoni, F.: Stochastic resonance. Reviews of Modern Physics 70(1), 223–287 (1998)
Jiang, L., Ouyang, Q., Tu, Y.: Quantitative modeling of escherichia coli chemotactic motion in environments varying in space and time. PLoS Comput. Biol. 6(4), e1000735 (2010)
Rao, C.V., Kirby, J.R., Arkin, A.P.: Design and diversity in bacterial chemotaxis: A comparative study in escherichia coli and bacillus subtilis. PLoS Biol. 2(2), e49 (2004)
Schnitzer, M.J.: Theory of continuum random walks and application to chemotaxis. Phys. Rev. E 48(4), 2553–2568 (1993)
Segall, J.E., Block, S.M., Berg, H.C.: Temporal comparisons in bacterial chemotaxis. Proceedings of the National Academy of Sciences of the United States of America 83(23), 8987–8991 (1986)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning). The MIT Press, Cambridge (March 1998)
Wiesenfeld, K., Moss, F.: Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs. Nature 373(6509), 33–36 (1995)
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DallaLibera, F., Ikemoto, S., Minato, T., Ishiguro, H., Menegatti, E., Pagello, E. (2010). A Parameterless Biologically Inspired Control Algorithm Robust to Nonlinearities, Dead-Times and Low-Pass Filtering Effects. In: Ando, N., Balakirsky, S., Hemker, T., Reggiani, M., von Stryk, O. (eds) Simulation, Modeling, and Programming for Autonomous Robots. SIMPAR 2010. Lecture Notes in Computer Science(), vol 6472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17319-6_34
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DOI: https://doi.org/10.1007/978-3-642-17319-6_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17318-9
Online ISBN: 978-3-642-17319-6
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