ViT-A*：基于视觉Transformer A*的四足机器人路径规划
ViT-A*: Legged Robot Path Planning using Vision Transformer A*
AI总结：
针对四足机器人在复杂环境中的导航问题，提出利用可微分规划器结合视觉Transformer（ViT）进行地图预处理，实现端到端全局路径规划，并在Spot和Go1机器人上验证了其有效性。
摘要：
腿式机器人，尤其是四足机器人，提供了有前景的导航能力，特别是在需要穿越多种地形和避开障碍物的场景中。本文通过集成数据驱动的路径规划方法，解决了使腿式机器人有效导航复杂环境的挑战。我们提出了一种利用可微分规划器的方法，允许通过神经网络学习端到端的全局规划，用于指挥四足机器人。该方法利用2D地图和障碍物规格作为输入来生成全局路径。为了增强所开发的基于神经网络的路径规划器的功能，我们使用视觉Transformer（ViT）进行地图预处理，以有效处理更大的地图。在两个真实四足机器人（Boston Dynamics Spot和Unitree Go1）上的实验评估证明了所提方法在生成可靠路径规划方面的有效性和多功能性。
英文摘要：
Legged robots, particularly quadrupeds, offer promising navigation capabilities, especially in scenarios requiring traversal over diverse terrains and obstacle avoidance. This paper addresses the challenge of enabling legged robots to navigate complex environments effectively through the integration of data-driven path-planning methods. We propose an approach that utilizes differentiable planners, allowing the learning of end-to-end global plans via a neural network for commanding quadruped robots. The approach leverages 2D maps and obstacle specifications as inputs to generate a global path. To enhance the functionality of the developed neural network-based path planner, we use Vision Transformers (ViT) for map pre-processing, to enable the effective handling of larger maps. Experimental evaluations on two real robotic quadrupeds (Boston Dynamics Spot and Unitree Go1) demonstrate the effectiveness and versatility of the proposed approach in generating reliable path plans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
