Multi-Robot Plume Source Localization Based on Swarm Evolution Mechanisms

Abstract

Based on swarm optimization theory and measurement techniques, this paper deals with a plume source localization problem by multiple mobile robots equipped with sensors. At first, the plume model is established and its inner property is explored on the diffusion source. Secondly, under some constraints from the environment and robotic mechanics, multiple robots are driven toward the source via swarm evolution mechanisms. Next, after incorporating an new adaptive weight and a leading strategy, quantum-leading-based optimization (QLBO) algorithm is developed as evolution mechanisms. Subsequently, combined with formation behavior, a collision/obstacle avoidance scheme is proposed for robots relying on dynamic theory. Finally, simulation tests are performed on a plane with obstacles through QLBO mechanism with collision/obstacle avoidance, and the results verify the effectiveness of the designed mobile policy.