The odor distribution Advection-Diffusion environments in obstacle environment have been developed. In real world the odor distribution are changing over time and multi peaks especially in obstacle environments. The purpose of developing this environment is to bridge the gap between the very complex hard to understand real-world problem (odor dispersion model) and all too simple toy problems (dynamic bit matching or moving parabola). Modified Particle Swarm Optimization is a well-known algorithm, which can continuously track a changing optimum over time. We will adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles to solve odor source localization in real world environment. Firstly, PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Secondly, Charged PSO, which is another extension of the PSO, has also been applied to solve dynamic problems. In order to control autonomous vehicles in more realistic condition from the viewpoint of robotic, where a speed limitation of the robot behavior and collision avoidance mechanism should be taken into consideration as well as the effect of noise and threshold value for the odor sensor response, also positioning error of GPS sensor of robot. Simulations illustrate that the new approach can solve such dynamic environment in Advection-Diffusion odor model problems even though in obstacle environments.