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.

翻译：由于DARPA地下挑战赛中的环境高度复杂，所有六支受资助团队均采用腿式机器人作为其机器人团队的一部分。这些机器人具备跨越障碍物的独特运动能力，这对导航规划提出了特殊要求。本文介绍并分析了CERBERUS团队在决赛中使用的导航规划器ArtPlanner。该规划器基于采样方法，通过可达性抽象确定有效位姿，并利用学习到的足部接触评分来限制可作为安全步态区域的探索范围。由此生成的规划图通过仿真训练的神经网络赋予学习型运动成本，以最小化行进时间并降低失败风险。该方法在有限计算时间内实现了实时性能。我们展示了在DARPA地下挑战赛决赛期间收集的广泛实验结果，该规划器助力CERBERUS团队赢得比赛，驱动四台ANYmal四足机器人连续自主运行90分钟，未发生任何规划或运动失败。