Due to the highly complex environment present during the DARPA Subterranean
Challenge, all six funded teams relied on legged robots as part of their
robotic team. Their unique locomotion skills of being able to step over
obstacles require special considerations for navigation planning. In this work,
we present and examine ArtPlanner, the navigation planner used by team CERBERUS
during the Finals. It is based on a sampling-based method that determines valid
poses with a reachability abstraction and uses learned foothold scores to
restrict areas considered safe for stepping. The resulting planning graph is
assigned learned motion costs by a neural network trained in simulation to
minimize traversal time and limit the risk of failure. Our method achieves
real-time performance with a bounded computation time. We present extensive
experimental results gathered during the Finals event of the DARPA Subterranean
Challenge, where this method contributed to team CERBERUS winning the
competition. It powered navigation of four ANYmal quadrupeds for 90 minutes of
autonomous operation without a single planning or locomotion failure