Cooperative chemical concentration map building using Decentralized Asynchronous Particle Swarm Optimization based search by mobile robots

In this article the main objective is to perform a search in an unknown area with multiple robots in order to determine the region with highest chemical gas concentration as well as to build the chemical gas concentration map. The searching and map building tasks are accomplished by using mobile robots equipped with smart transducers for gas sensing. Robots perform the search autonomously by using their own data and the information (position information and sensor readings) obtained from the other robots. Moreover, simultaneously the robots send their sensor readings of the chemical concentration and their position data to a remote computer (a base station), where the data is combined, interpolated, and filtered to form an real-time map of the chemical gas concentration in the environment. To achieve this task as a high-level path planning algorithm we use a decentralized and asynchronous version of the Particle Swarm Optimization (PSO) algorithm which also allows for time-varying neighborhood.

[1]  A. Tomescu,et al.  Calibration procedure for SnO2-based gas sensors , 1995 .

[2]  Veysel Gazi,et al.  Particle swarm optimization with dynamic neighborhood topology: Three neighborhood strategies and preliminary results , 2008, 2008 IEEE Swarm Intelligence Symposium.

[3]  Veysel Gazi,et al.  Decentralized asynchronous particle swarm optimization , 2008, 2008 IEEE Swarm Intelligence Symposium.

[4]  Veysel Gazi,et al.  Swarm aggregations using artificial potentials and sliding mode control , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

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

[6]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

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

[8]  L. Marques,et al.  Olfactory sensory system for odour-plume tracking and localization , 2003, Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498).

[9]  James M. Hereford A Distributed Particle Swarm Optimization Algorithm for Swarm Robotic Applications , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[10]  James Hereford,et al.  Multi-robot search using a physically-embedded Particle Swarm Optimization , 2008 .

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

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

[13]  Lino Marques,et al.  Finding Odours Across Large Search Spaces: A Particle Swarm-Based Approach , 2005 .

[14]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[15]  Silvia Coradeschi,et al.  Gas distribution mapping of multiple odour sources using a mobile robot , 2009, Robotica.

[16]  Veysel Gazi,et al.  Asynchronous Particle Swarm Optimization Based Search with a Multi-Robot System: Simulation and Impl , 2010 .

[17]  Ganesh K. Venayagamoorthy,et al.  Optimal PSO for collective robotic search applications , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[18]  Lino Marques,et al.  kheNose - A SMART TRANSDUCER FOR GAS SENSING , 2008 .

[19]  Tom Duckett,et al.  Building gas concentration gridmaps with a mobile robot , 2003, Robotics Auton. Syst..

[20]  Toshio Fukuda,et al.  A PSO-based Mobile Sensor Network for Odor Source Localization in Dynamic Environment: Theory, Simulation and Measurement , 2006, 2006 IEEE International Conference on Evolutionary Computation.

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