A Hybridization of Gravitational Search Algorithm and Particle Swarm Optimization for Odor Source Localization

A Hybridization of Gravitational Search Algorithm and Particle Swarm Optimization for Odor Source Localization

Upma Jain (ABV-IIITM Gwalior, India), W. Wilfred Godfrey (ABV-IIITM Gwalior, India) and Ritu Tiwari (Robotics and Intelligent System Design Lab ABV-IIITM Gwalior, India)
Copyright: © 2020 |Pages: 15
DOI: 10.4018/978-1-7998-1754-3.ch072
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This paper concerns with the problem of odor source localization by a team of mobile robots. The authors propose two methods for odor source localization which are largely inspired from gravitational search algorithm and particle swarm optimization. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. As robots enter in the vicinity of plume area they form groups using K-nearest neighbor algorithm. The problem of local optima is handled through the use of search counter concept. The proposed approaches are tested and validated through simulation.
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Target search is the process of localizing an object of interest on the basis of current information available about the environment. This problem has been studied under various scenarios such as static or dynamic target, constant or variable plume intensity, single or multiple targets etc. Depending on the target, a broader spectrum of target locating problems has emerged. For instance, odor source localization (Jatmiko et al. 2011), target localization and tracking (Ramya et al., 2012), search and rescue (Liu & Nejat, 2013) and so on.

Research on locating the odor source using robots began in 1990s (Sandini et al., 1993). Problem of odor source localization has already been studied by many researchers (Russell et al., 2003; Marques et al., 2006; Jatmiko et al., 2011; Marjovi et al., 2011) Several methods have been proposed namely: Hex-path algorithm (Russell, 2003; Lilienthal et al., 2003), Gradient following (Li et al., 2007), the Zigzag path search strategy (Holland & Melhuish, 1996), rule based strategy (Zarzhitsky et al., 2004), Fluxotaxis (Gong et al., 2011), Odor tracking

(Vergassola et al., 2007), Infotaxis (Kuwana & Shimoyama, 1998), combination of Chemotaxis and Anemotaxis (Hayes et al. 2002), cooperation based on swarm intelligence (Hayes et al., 2003), etc.

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