Active Sensing Strategy: Multi-Modal, Multi-Robot Source Localization and Mapping in Real-World Settings with Fixed One-Way Switching

Active Sensing Strategy: Multi-Modal, Multi-Robot Source Localization and Mapping in Real-World Settings with Fixed One-Way Switching

This paper introduces a state-machine model designed for a multi-modal, multi-robot environmental sensing algorithm tailored to dynamic real-world settings. The multi-modal algorithm uniquely combines two distinct exploration strategies for gas source localization and mapping tasks: (1) an initial exploration phase using multi-robot coverage path planning with variable formations, providing early gas field indication; and (2) a subsequent active sensing phase employing multi-robot swarms for precise field estimation. The state machine provides the logic for the transition between these two sensing algorithms. In the exploration phase, a coverage path is generated, maximizing the visited area while measuring gas concentration and estimating the initial gas field at pre-defined sample times. Subsequently, in the active sensing phase, mobile robots moving in a swarm collaborate to select the next measurement point by broadcasting potential positions and reward values, ensuring coordinated and efficient sensing for a multi-robot swarm system. System validation involves hardware-in-the-loop experiments and real-time experiments with a radio source emulating a gas field. The proposed approach is rigorously benchmarked against state-of-the-art single-mode active sensing and gas source localization techniques. The comprehensive evaluation highlights the multi-modal switching approach's capacity to expedite convergence, adeptly navigate obstacles in dynamic environments, and significantly enhance the accuracy of gas source location predictions. These findings highlight the effectiveness of our approach, showing significant improvements: a 43% reduction in turnaround time, a 50% increase in estimation accuracy, and enhanced robustness of multi-robot environmental sensing in cluttered scenarios without collisions. These advancements surpass the performance of conventional active sensing strategies, the partial differential equation model, and geometrical localization approaches, underscoring the efficacy of our method.