Evolutionary Optimization of Robust Control Laws for Mobile Robots in Dynamic Environments

  • Rafael S. Del Lama USP
  • Raquel M. Candido USP
  • Luciana T. Raineri USP
  • Renato Tinós USP

Resumo


The problem of controlling mobile robots in dynamic environments is an interesting challenge. This paper investigates the problem of controlling mobile robots in dynamic environments through robust control laws defined by echo state networks (ESN). The output weights of the ESN are optimized by genetic algorithms (GAs). Different GAs developed for optimization in dynamic environments are compared in the problem of searching for robust solutions. Two approaches are investigated: through dynamic evolutionary optimization and robust evolutionary optimization. In the experiments, the GA evolved in the static environment produces good trajectories in environments that resemble the static environment (without obstacles). However, it presents unsatisfactory performance in environments that are very different from the static environment. Both GAs evolved in the dynamic and robust optimization approaches present good results in environments that differ from the static environment.

Referências


Beyer, H.-G. and Sendhoff, B. (2007). Robust optimization–a comprehensive survey. Computer methods in applied mechanics and engineering, 196(33-34):3190–3218.

Billard, A., Ijspeert, A. J., and Martinoli, A. (1999). A multi-robot system for adaptive exploration of a fast-changing environment: Probabilistic modeling and experimental study. Connection Science, 11(3-4):359–379.

Branke, J. (2002). Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers.

Cobb, H. G. and Grefenstette, J. J. (1993). Genetic algorithms for tracking changing environments. Technical report, Naval Research Lab Washington DC.

Floreano, D. and Nolfi, S. (2000). Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press/Bradford Books.

Fu, H., Sendhoff, B., and Tang, K. (2015). Robust optimization over time: Problem difficulties and benchmark problems. IEEE Transactions on Evolutionary Computation.

Jaeger, H. and Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. science, 304(5667):78–80.

Jin, Y. and Branke, J. (2005). Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on evolutionary computation, 9(3):303–317.

Mitchell, M. (1996). An introduction to genetic algorithms. MIT Press.

Romero, R. A. F., Prestes, E., Osório, F., and Wolf, D. (2014). Robótica Móvel. S˜ao Paulo: LTC.

Shimo, H. K., Roque, A. C., Tinós, R., Tejada, J., and Morato, S. (2010). Use of evolutionary robots as an auxiliary tool for developing behavioral models of rats in an elevated plus-maze. In Neural Networks (SBRN), 2010 Eleventh Brazilian Symposium on, pages 217–222. IEEE.

Siegwart, R., Nourbakhsh, I. R., and Scaramuzza, D. (2011). Introduction to autonomous mobile robots. MIT press.

Tinós, R. and de Carvalho, A. C. P. L. F. (2006). Use of gene dependent mutation probability in evolutionary neural networks for non-stationary problems. Neurocomputing, 70(1-3):44–54.

Tinós, R. and Yang, S. (2014). Analysis of fitness landscape modifications in evolutionary dynamic optimization. Information Sciences, 282:214–236.

Webb, B. (2001). Can robots make good models of biological behaviour? Behavioral and brain sciences, 24(6):1033–1050.

Publicado
22/10/2018
DEL LAMA, Rafael S.; CANDIDO, Raquel M.; RAINERI, Luciana T.; TINÓS, Renato. Evolutionary Optimization of Robust Control Laws for Mobile Robots in Dynamic Environments. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 461-472. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2018.4439.