Modified PSO Algorithm for Odor Source Localization Problems: Progress and Challenge

Abstract

Research using the Particle Swarm Optimization (PSO) with robots as agents for solving odor source localization in dynamic advection-diffusion environment had been developed. Simulation and realworld implementation are used to verify the robustness of PSO for odor source localization. Result verifies that PSO is feasible to be implemented either simulation orreal-world implementation. To enhance the potency of PSO, a new algorithm based on PSO which can handle the environmental change is investigated. Modification involves the capability to follow a local gradient of the chemical concentration within a plume and follow direction of the wind velocity. Moreover, the simultaneous search done by many groups of robots is adopted on Modified PSO (MPSO) to solve the multi-peak and multisource problem. Some modification is needed on the parallel search to handle multiple groups which following the same sources. Then ODE (Open Dynamics Engine) library is used for physical modeling of the robot like friction, balancing moment and the other. Finally the statistical analysis shows that the MPSO is technically sounds.