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.

翻译：本文提出一种面向动态真实环境的多模态多机器人环境感知算法的状态机模型。该算法创新性地融合了两种用于气体源定位与建图的探索策略：(1) 采用可变编队多机器人覆盖路径规划的初始探索阶段，用于早期气体场指示；(2) 后续采用多机器人集群进行精确场估计的主动感知阶段。状态机控制这两个阶段间的切换。在探索阶段，覆盖路径通过预定义采样时间测量气体浓度并估计初始气体场，同时最大化探测区域。在主动感知阶段，集群中的移动机器人协同选择下一测量点，确保协调高效的环境感知。系统验证通过硬件在环实验和模拟气体场的射频源实时测试完成。该方法与最先进的单模态主动感知及气体源定位技术进行了基准比较。评估结果表明：多模态切换方法能加速收敛过程，在动态环境中实现避障，并显著提升气体源定位精度。研究数据显示，该方法使转向时间减少43%，估计精度提高50%，并在杂乱场景中实现无碰撞的多机器人环境感知鲁棒性增强，其性能超越传统主动感知策略。