Autonomous navigation through unstructured terrains has been most effectively demonstrated by animals, who utilize a large set of locomotive styles to move through their native habitats. While legged robots have recently demonstrated several of these locomotion modalities (such as walking, running, jumping, and climbing vertical walls), motion planners have yet to be able to leverage these unique mobility characteristics. In this article, we outline some of the specific motion planning challenges faced when attempting to plan for legged systems with dynamic gaits, with specific instances of these demonstrated by the dynamic climbing platform TAILS. Using a unique implementation of sampling-based model predictive optimization, we demonstrate the ability to motion plan around obstacles on vertical walls and experimentally demonstrate this on TAILS by navigating through traditionally difficult narrow gap problems.

中文翻译：

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腿式机动性导航：动态攀爬

通过非结构化地形的自主导航已被动物最有效地展示，它们利用大量机车样式在其原生栖息地中移动。虽然腿式机器人最近展示了其中的几种运动方式（例如步行、跑步、跳跃和攀爬垂直墙壁），但运动规划人员尚未能够利用这些独特的移动特性。在本文中，我们概述了在尝试为具有动态步态的腿式系统进行规划时所面临的一些特定运动规划挑战，并通过动态攀爬平台 TAILS 展示了这些挑战的具体实例。使用基于采样的模型预测优化的独特实现，