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

Abstract

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.

Robot navigation and target capturing using nature-inspired approaches in a dynamic environment

“…For detection of a target, specific Gaussian Mixture Models are used in cyber cars [23]. The target may also live for some time and may disappear after that [9]. In [29], target tracking is done based on some patterns.…”