Modified PSO Algorithm Based on Flow of Wind for Odor Source Localization Problems in Dynamic Environments

A new algorithm based on Modified Particle Swarm Optimization (MPSO) in order to control autonomous vehicles for solving odor source localization in dynamic advection-diffusion environment have been developed. Furthermore an improvements of the MPSO for odor source localization, which follows a local gradient of the chemical concentration within a plume is investigated. Another popular biomimetic approach in odor source localization problem is anemotaxis. An anemotaxis-driven agent measures the direction of the fluid’s velocity and navigates “upstream” within the plume. In this paper, the combination of chemotaxis “MPSO”-based algorithm and anemotaxis will be described. This method is well known in the animal kingdom as odorgated rheotaxis (OGR). On the other hand, in real world, the odor distribution is multi peaks especially in obstacle environments. For that reason, a new environment with obstacle will be developed. The purpose of developing the environment is to bridge the gap between very complex, hard-to-understand real world problems (odor dispersion model) and overly simplistic-toy-problem (dynamic bit matching or moving parabola). Simulations illustrate that the new approach can solve Advection-Diffusion odor model problems in such a dynamic odor with obstacle-filled environments. Key-Words: MPSO, Anemotaxis, Chemotoxis, OGR, Advection-Diffsuion, Odor Source