Self-coloring-Driven Plume Source Localization Strategy for Multiple Robots Using Dirichlet Process Gaussian Mixture Model and Mutation Random Salp Swarm Algorithm

Accidents of leaks and emissions of flammable, explosive, and toxic substances severely threaten people’s health and public safety. Traditional heuristic algorithms for source localization that utilize wind information to guide the robots to search for plumes in the airflow environment, substantial potential plume information is not fully considered, reducing the success rate and precision of source localization. To solve this problem, in this study, we propose a novel strategy for plume source localization using information gain. This strategy is inspired by the navigation and foraging behaviors of the salps in the ocean, and the robots use a mutation random salp swarm algorithm to track the plume. This strategy utilizes the Dirichlet process Gaussian mixture model to color the plume information and achieve a dynamic update of the plume information distribution. Therefore, this developed strategy provides potential source location clues for the robots, dynamically adjusting the robots’ search behaviors and enhancing the source-seeking success rate and efficiency. This strategy is verified by experimental validations. The test results show that with two methods that use wind information, namely, the improved whale optimization algorithm and wind utilization II particle swarm optimization, and two approaches independent of wind information, namely, the fixed-step fruit fly optimization algorithm and improved particle swarm optimization, the proposed strategy improves the source localization success rate by 5% to 33% and accelerates the efficiency by 0.1 to 2.6 times in the obstacle-free scenario, and improves the source localization success rate by 4% to 100% and accelerates the efficiency by 0.4 to 2.2 times in the obstacle environment.