Localization of multiple odor sources using modified glowworm swarm optimization with collective robots

A multi-robot cooperation strategy based on a modified glowworm swarm optimization (M-GSO) is proposed. This strategy includes global random search of self-exploration, local search based on GSO algorithm and odor source declaration. And forbidden area setting is also introduced into the iteration process to achieve localization for multiple odor sources. This mechanism can ensure robots to start searching for the next odor source after the discovery of an odor source and ensure that other robots would not re-locate this odor source. Simulation results show that the proposed M-GSO can effectively enable the robot system to search and find all the odor sources existed in the indoor environment quickly and accurately.

[1]  Frank W. Grasso,et al.  Integration of Flow and Chemical Sensing for Guidance of Autonomous Marine Robots in Turbulent Flows , 2002 .

[2]  Jay A. Farrell,et al.  Biologically inspired chemical plume tracing on an autonomous underwater vehicle , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[3]  Paul Scerri,et al.  Transitioning multiagent technology to UAV applications , 2008, AAMAS.

[4]  Toshio Fukuda,et al.  A Particle Swarm-based Mobile Sensor Network for Odor Source Localization in a Dynamic Environment , 2006, DARS.

[5]  Jelle Atema,et al.  Toward the convergence: robot and lobster perspectives of tracking odors to their source in the turbulent marine environment , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

[6]  Debasish Ghose,et al.  Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations , 2008, Robotics Auton. Syst..

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

[8]  Giulio Sandini,et al.  Gradient driven self-organizing systems , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[9]  Paul Scerri,et al.  Geolocation of RF Emitters by Many UAVs , 2007 .

[10]  William M. Spears,et al.  Distributed robotics approach to chemical plume tracing , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Gaurav S. Sukhatme,et al.  Bacterium-inspired robots for environmental monitoring , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[12]  Adel Said Elmaghraby,et al.  A swarm approach for emission sources localization , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[13]  R. Andrew Russell,et al.  Ant trails - an example for robots to follow? , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[14]  J. Atema.  Chemical signals in the marine environment: dispersal, detection, and temporal signal analysis. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Debasish Ghose,et al.  Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..