在粗糙地形上使用任意数量的腿行走
Walking on Rough Terrain with Any Number of Legs
AI总结：
本文提出了一种适用于多腿机器人在粗糙地形上行走的控制架构，结合了事件级联控制器与CPG控制器的特点，适用于多腿机器人。
摘要：
机器人通过复制节肢动物在复杂环境中导航的卓越敏捷性将获益。这里我们考虑了具有6或更多腿的多腿系统的控制。当前机器人中的多腿控制策略包括大型黑盒机器学习模型、中央模式发生器（CPG）网络和开环前馈控制，其稳定性来自机械结构。我们提出了一种用于粗糙地形的多腿控制系统，该系统使用每2条腿有3个执行器的分段机器人，在6到16条腿的机器人上进行了仿真验证。每个分段具有相同的状体机，且每个分段还接收前一个分段的输入。我们的设计在WalkNet-like事件级联控制器和CPG基于控制器之间架起了桥梁：当有接触时，它紧密耦合到地面，但当地面接触缺失时，它会产生虚构的运动。这种方法可能作为多腿机器人的自适应且计算轻量级控制器，以及作为机器学习控制器学习的基础能力。
英文摘要：
Robotics would gain by replicating the remarkable agility of arthropods in navigating complex environments. Here we consider the control of multi-legged systems which have 6 or more legs. Current multi-legged control strategies in robots include large black-box machine learning models, Central Pattern Generator (CPG) networks, and open-loop feed-forward control with stability arising from mechanics. Here we present a multi-legged control architecture for rough terrain using a segmental robot with 3 actuators for every 2 legs, which we validated in simulation for robots with 6 to 16 legs. Segments have identical state machines, and each segment also receives input from the segment in front of it. Our design bridges the gap between WalkNet-like event cascade controllers and CPG-based controllers: it tightly couples to the ground when contact is present, but produces fictive locomotion when ground contact is missing. The approach may be useful as an adaptive and computationally lightweight controller for multi-legged robots, and as a baseline capability for scaffolding the learning of machine learning controllers.
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。
