  This paper introduces a state-machine model for a multi-modal, multi-robot
environmental sensing algorithm tailored to dynamic real-world settings. The
algorithm uniquely combines two exploration strategies for gas source
localization and mapping: (1) an initial exploration phase using multi-robot
coverage path planning with variable formations for early gas field indication;
and (2) a subsequent active sensing phase employing multi-robot swarms for
precise field estimation. The state machine governs the transition between
these two phases. During exploration, a coverage path maximizes the visited
area while measuring gas concentration and estimating the initial gas field at
predefined sample times. In the active sensing phase, mobile robots in a swarm
collaborate to select the next measurement point, ensuring coordinated and
efficient sensing. System validation involves hardware-in-the-loop experiments
and real-time tests with a radio source emulating a gas field. The approach is
benchmarked against state-of-the-art single-mode active sensing and gas source
localization techniques. Evaluation highlights the multi-modal switching
approach's ability to expedite convergence, navigate obstacles in dynamic
environments, and significantly enhance gas source location accuracy. The
findings show a 43% reduction in turnaround time, a 50% increase in estimation
accuracy, and improved robustness of multi-robot environmental sensing in
cluttered scenarios without collisions, surpassing the performance of
conventional active sensing strategies.
