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In response, this paper contributes a novel graph\u2010based subterranean exploration path planning method that is attuned to key topological properties of subterranean settings, such as large\u2010scale tunnel\u2010like networks and complex multibranched topologies. Designed both for aerial and legged robots, the proposed method is structured around a bifurcated local\u2010 and global\u2010planner architecture. The local planner utilizes a rapidly exploring random graph to reliably and efficiently identify paths that optimize an exploration gain within a local subspace, while simultaneously avoiding obstacles, respecting applicable traversability constraints and honoring dynamic limitations of the robots. Reflecting the fact that multibranched and tunnel\u2010like networks of underground environments can often lead to dead\u2010ends and accounting for the robot endurance, the global planning layer works in conjunction with the local planner to incrementally build a sparse global graph and is engaged when the system must be repositioned to a previously identified frontier of the exploration space, or commanded to return\u2010to\u2010home. The designed planner is detailed with respect to its computational complexity and compared against state\u2010of\u2010the\u2010art approaches. Emphasizing field experimentation, the method is evaluated within multiple real\u2010life deployments using aerial robots and the ANYmal legged system inside both long\u2010wall and room\u2010and\u2010pillar underground mines in the United States\u00a0and in Switzerland, as well as inside an underground bunker. 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