A distributed architecture with two layers for odor source localization in multi-robot systems

This paper deals with the problem of odor source localization by using multi-robot systems. A distributed architecture, which consists of two layers: artificial intelligence layer and control layer, is proposed. Firstly, in the artificial intelligence layer, evolutionary algorithms, which can tackle information from other robots via communication networks, are employed. By using these evolutionary algorithms, the next state of a robot can be derived. Secondly, in the control layer, a consensus algorithm is used to control the robot to complete state transition from the current state to the new state derived. Finally, odor source localization problems are used to illustrate the effectiveness of the distributed architecture with two layers.

[1]  Jay A. Farrell,et al.  Plume mapping via hidden Markov methods , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Alcherio Martinoli,et al.  A comparison of casting and spiraling algorithms for odor source localization in laminar flow , 2008, 2008 IEEE International Conference on Robotics and Automation.

[3]  W. Jatmiko,et al.  A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement , 2007, IEEE Computational Intelligence Magazine.

[4]  Lino Marques,et al.  Particle swarm-based olfactory guided search , 2006, Auton. Robots.

[5]  Rodney M. Goodman,et al.  Swarm robotic odor localization , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[6]  Alcherio Martinoli,et al.  Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[7]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  J. Farrell,et al.  Filament-Based Atmospheric Dispersion Model to Achieve Short Time-Scale Structure of Odor Plumes , 2002 .

[9]  Christodoulos Lytridis,et al.  A systematic approach to the problem of odour source localisation , 2006, Auton. Robots.

[10]  Roger D. Quinn,et al.  Moth-Inspired Plume Tracking Strategies In Three-Dimensions , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[11]  Shuo Pang,et al.  Chemical Plume Source Localization , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Rodney M. Goodman,et al.  Distributed odor source localization , 2002 .

[13]  R. Andrew Russell,et al.  A comparison of reactive robot chemotaxis algorithms , 2003, Robotics Auton. Syst..

[14]  Alcherio Martinoli,et al.  Distributed Adaptation in Multi-robot Search Using Particle Swarm Optimization , 2008, SAB.

[15]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[16]  Paolo Dario,et al.  SPIRAL: A novel biologically-inspired algorithm for gas/odor source localization in an indoor environment with no strong airflow , 2009, Robotics Auton. Syst..

[17]  Andreas Zell,et al.  Gas Source Tracing with a Mobile Robot Using an Adapted Moth Strategy , 2003, AMS.

[18]  Wei Ren,et al.  Distributed coordination architecture for multi-robot formation control , 2008, Robotics Auton. Syst..

[19]  Randal W. Beard,et al.  Distributed Consensus in Multi-vehicle Cooperative Control - Theory and Applications , 2007, Communications and Control Engineering.

[20]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.