  This paper proposes a state-machine model for a multi-modal, multi-robot
environmental sensing algorithm. This multi-modal algorithm integrates two
different exploration algorithms: (1) coverage path planning using variable
formations and (2) collaborative active sensing using multi-robot swarms. The
state machine provides the logic for when to switch between these different
sensing algorithms. We evaluate the performance of the proposed approach on a
gas source localisation and mapping task. We use hardware-in-the-loop
experiments and real-time experiments with a radio source simulating a real gas
field. We compare the proposed approach with a single-mode, state-of-the-art
collaborative active sensing approach. Our results indicate that our
multi-modal switching approach can converge more rapidly than single-mode
active sensing.
