A PSO-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: Theory, simulation and measurement

This paper provides a combination of chemotaxic and anemotaxic modeling, known as Odor-Gated Rheotaxis (OGR), to solve real-world odor source localization problems. Throughout the history of trying to mathematically localize an odor source, two common biometric approaches have been used. The first approach, chemotaxis, describes how particles flow according to local concentration gradients within an odor plume. Chemotaxis is the basis for many algorithms, such as Particle Swarm Optimization (PSO). The second approach is anemotaxis, which measures the direction and velocity of a fluid flow, thus navigating upstrearn within a plume to localize its source. Although both chemotaxic and anemotaxic based algorithms are capable of solving overly-simplified odor localization problems, such as dynamic-bit-matching or moving-parabola problems, neither method by itself is adequate to accurately address real life scenarios. In the real world, odor distribution is multi-peaked due to obstacles in the environment. However, by combining the two approaches within a modified PSO-based algorithm, odors within an obstacle-filled environment can be localized and dynamic Advection-Diffusion problems can be solved. Thus, robots containing this Modified Particle Swarm Optimization algorithm (MPSO) can accurately trace an odor to its source.