This paper presents a problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Odor source localization is an interesting application in dynamic problems. Modified Particle Swarm Optimization?is a well-known algorithm, which can continuously track a changing optimum over time.?PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charged PSO which is another extension of the PSO has also been applied to solve dynamic problems. We will adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles in more realistic environment 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 Gaussian and Advection-Diffusion odor model problems.