Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-19T00:42:46.810Z Has data issue: false hasContentIssue false
  • English
  • Français

Distributed Particle Swarm Optimization for limited-time adaptation with real robots

Published online by Cambridge University Press:  22 November 2013

Ezequiel Di Mario  and
Alcherio Martinoli
Ezequiel Di Mario*
Affiliation:
Distributed Intelligent Systems and Algorithms Laboratory, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
Alcherio Martinoli
Affiliation:
Distributed Intelligent Systems and Algorithms Laboratory, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation.

Keywords

DARS2012Multi-robot systemsMobile robotsParticle Swarm OptimizationDistributed learning
Type
Articles
Information
Robotica , Volume 32 , Issue 2: Distributed Autonomous Robotic Systems (DARS). , March 2014 , pp. 193 - 208
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Liang, J. J., Qu, B. Y., Suganthan, P. N. and Hernández-Díaz, A. G., “Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization”, 2013.Google Scholar
2.Floreano, D. and Mondada, F., “Evolution of homing navigation in a real mobile robot”, IEEE Trans. Syst. Man Cybern. 26 (3), 396–407 (1996).Google Scholar
3.Watson, R. a., Ficici, S. G. and Pollack, J. B., “Embodied Evolution: Distributing an evolutionary algorithm in a population of robots”, Robot. Auton. Syst. 39 (1–18) (Apr. 2002).Google Scholar
4.Kennedy, J. and Eberhart, R., “Particle Swarm Optimization”, IEEE International Conference on Neural Networks, vol.4 (1995) pp. 1942–1948.Google Scholar
5.Poli, R., “Analysis of the publications on the applications of particle swarm optimisation”, J. Artif. Evol. Appl. 2008 (2), 1–10 (2008).Google Scholar
6.Akat, S. B. and Gazi, V., “Decentralized Asynchronous Particle Swarm Optimization”, IEEE Swarm Intelligence Symposium, DOI: 10.1109/SIS.2008.4668304 (Sep. 2008).Google Scholar
7.Rada-Vilela, J., Zhang, M. and Seah, W., “Random Asynchronous PSO”, The 5th International Conference on Automation, Robotics and Applications (Dec. 2011) pp. 220–225.Google Scholar
8.Turduev, M. and Atas, Y., “Cooperative Chemical Concentration Map Building Using Decentralized Asynchronous Particle Swarm Optimization Based Search by Mobile Robots”, IEEE/RSJ International Conference on Intelligent Robots and Systems (2010) pp. 4175–4180.Google Scholar
9.Marques, L., Nunes, U. and Almeida, A. T., “Particle swarm-based olfactory guided search”, Auton. Robots 20, 277–287 (May 2006).Google Scholar
10.Hereford, J. and Siebold, M., “Using the Particle Swarm Optimization Algorithm for Robotic Search Applications”, IEEE Swarm Intelligence Symposium (2007) pp. 53–59.Google Scholar
11.Jin, Y. and Branke, J., “Evolutionary optimization in uncertain environments: A survey”, IEEE Trans. Evolutionary Comput. 9, 303–317 (June 2005).Google Scholar
12.Arnold, D. and Beyer, H.-G., “A general noise model and its effects on evolution strategy performance”, IEEE Trans. Evolutionary Comput. 10, 380–391 (Aug. 2006).Google Scholar
13.Parsopoulos, K. E. and Vrahatis, M. N., “Particle Swarm Optimizer in Noisy and Continuously Changing Environments”, Artificial Intelligence and Soft Computing (Hamza, M. H., ed.) (IASTED/ACTA Press, 2001) pp. 289–294.Google Scholar
14.Pan, H., Wang, L. and Liu, B., “Particle swarm optimization for function optimization in noisy environment”, Appl. Math. Comput. 181, 908–919 (Oct. 2006).Google Scholar
15.Pugh, J., Zhang, Y. and Martinoli, A., “Particle Swarm Optimization for Unsupervised Robotic Learning”, IEEE Swarm Intelligence Symposium (2005) pp. 92–99.Google Scholar
16.Pugh, J. and Martinoli, A., “Distributed scalable multi-robot learning using particle swarm optimization”, Swarm Intell. 3, 203–222 (May 2009).Google Scholar
17.Di Mario, E., Talebpour, Z. and Martinoli, A., “A Comparison of PSO and Reinforcement Learning for Multi-Robot Obstacle Avoidance”, IEEE Conference on Evolutionary Computation, vol. 1 (June 2013) pp. 149–156.Google Scholar
18.De Jong, K. A., An Analysis of the Behavior of a Class of Genetic Adaptive Systems Ph.D. Thesis (University of Michigan, 1975).Google Scholar
19.Michel, O., “Webots: Professional mobile robot simulation”, Adv. Robot. Syst. 1 (1), 39–42 (2004).Google Scholar
20.Lochmatter, T., Roduit, P., Cianci, C., Correll, N., Jacot, J. and Martinoli, A., “Swistrack-a Flexible Open Source Tracking Software for Multi-Agent Systems”, IEEE/RSJ International Conference on Intelligent Robots and Systems (2008) pp. 4004–4010.Google Scholar
21.Di Mario, E., Mermoud, G., Mastrangeli, M. and Martinoli, A., “A Trajectory-Based Calibration Method for Stochastic Motion Models”, IEEE/RSJ International Conference on Intelligent Robots and Systems (Sep. 2011) pp. 4341–4347.Google Scholar
22.Bratton, D. and Kennedy, J., “Defining a Standard for Particle Swarm Optimization”, IEEE Swarm Intelligence Symposium (Apr. 2007) pp. 120–127.Google Scholar