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 Subterranean Challenge期间存在高度复杂的环境,所有六个受资助的团队都依赖脚踏脚踏两用机器人作为其机器人团队的一部分,他们独特的能够跨越障碍的移动技能需要特殊的导航规划考虑。在这项工作中,我们介绍并检查CERBERUS 团队在决赛期间使用的导航规划员Art Planner。它基于一种基于取样的方法,该方法确定具有可达性的有效配置,并使用可达性抽象学分数限制被视为安全的踏脚区域。由此得出的规划图由经过模拟训练的神经网络分配了学习运动费用,以最大限度地缩短跨度和限制失败风险。我们的方法在受约束的计算时间内实现了实时性能。我们介绍了在DARPA Subterrane Challenge的决赛中收集的广泛实验结果,该方法有助于CERBERBURUS团队赢得竞争。它为4个安马利四分的自动运行90分钟,没有单一规划或移动故障。</s>