Multi-Robot Plume Source Localization by Distributed Quantum-Inspired Guidance With Formation Behavior

In combination with optimization theory and swarm evolutionary mechanisms, based on multiple mobile robots equipped with sensors, this paper deals with a plume source localization issue with environmental obstacles and communication restrictions for robots. First of all, we offer the plume modeling process and analyze some internal characteristics about its source, such that the source localization issue can be addressed by solving a path planning one with constraints for cooperative swarm robots. Secondly, a quantum potential well, an average estimator and a minimum estimator are introduced into swarm evolutionary mechanisms with an oscillation weight, such that quantum-behaved cooperative navigation (QBCN) scheme is proposed as a guidance strategy, where the average estimator and the minimum estimator are designed depending on distributed consensus theory. Subsequently, we put forth a formation behavior consisting of leader-follower tactics, obstacle avoidance tactics and motion optimization tactics, which not only provides a practical collision/obstacle measure, but also increases the coverage area for the objective. Afterwards, a performance analysis for the proposed policy is executed on the convergence and computational complexity, which ensures the accuracy and timeliness of source localization in theory. Finally, simulation tests are performed in different scenarios, and the results validate the practicability and effectiveness of the developed method.