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 odor-gated 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.
|Number of pages||8|
|Journal||WSEAS Transactions on Systems|
|Publication status||Published - 1 Feb 2008|
- Odor source