Research on Improved ACO Algorithm-Based Multi-Robot Odor Source Localization

Abstract

To address the issue of multi-robot odor source localization, this paper proposes an improved Ants Colony Optimization (ACO) algorithm in conjunction with an upwind search strategy. In the actual multi-robot search process, a group of robots with a higher pheromone value are used for upwind search while a group of robots with a lower pheromone value are used for search by using the improved ACO algorithm. The improved ACO algorithm uses the odor concentration measured by the robots as the pheromone values at the relevant nodal points (grid), and the time-varying wind field impact on odor plume transmission is taken into account in the global pheromone update, and the robots share the global pheromone distribution map. It can be seen from the simulation experiment done in the environment model created by using fluent software that this algorithm gives an robustness.