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.

翻译：本文提出了一种用于多模态多机器人环境感知算法的状态机模型。该多模态算法融合了两种不同的探索算法：（1）基于可变编队的覆盖路径规划，以及（2）利用多机器人集群的协同主动感知。状态机提供了在这些不同感知算法间切换的逻辑依据。我们在气体源定位与制图任务上评估了所提方法的性能，并通过硬件在环实验以及利用模拟真实气场的无线电信号源开展了实时实验。我们将所提方法与一种采用单模态的先进协同主动感知方法进行了对比。结果表明，我们的多模态切换方法能够比单模态主动感知更快地收敛。