通过调控身体波浪运动实现多足机器人转向，提升复杂地形适应性。
Steering Elongate Multi-legged Robots By Modulating Body Undulation Waves
- 借鉴细长体游泳机理，将机器人视为高摩擦环境中的‘陆地泳者’建模。
- 通过叠加两个横向波浪实现转向，实测轨迹与理论预测高度一致。
- 方法适用于多种地形，适合研发高机动性仿生机器人团队。
蜈蚣因其多足结构和体驱控制，在多样环境中表现出优异的机动性。受此启发，其类比机器人也展现出高效的陆地运动能力。然而，这类多足机器人的成功主要局限于前进运动，转向研究仍不充分，原因在于高自由度机器人难以协调以沿可预测的平面轨迹运动。为此，我们借鉴细长系统在高阻尼环境中的几何力学（GM）控制策略，将多足细长系统建模为高摩擦环境中的‘陆地泳者’，并采用低阶无肢系统模板推导出转向方案。通过叠加两个横向身体波浪，提出一种有效的转向策略，并进一步探索‘转向波’的变体，实现一系列弧线追踪的运动基元。我们在一个机器人物理模型上验证了该调制方案，实验轨迹与理论位移预测高度吻合。随后，我们将该控制框架应用于地面控制机器人公司（Ground Control Robotics）的细长多足机器人 Major Tom，利用这些运动基元构建平面运动，并在不同地形上实现闭环控制。本工作建立了一套基于体形变化序列的低阶模型系统化框架，实现了平面内高机动设备的有效控制。
原文摘要 · Abstract (English)
Centipedes exhibit great maneuverability in diverse environments due to their many legs and body-driven control. By leveraging similar morphologies and control strategies, their robotic counterparts also demonstrate effective terrestrial locomotion. However, the success of these multi-legged robots is largely limited to forward locomotion; steering is substantially less studied, in part because of the difficulty in coordinating a high degree-of-freedom robot to follow predictable, planar trajectories. To resolve these challenges, we take inspiration from control schemes based on geometric mechanics(GM) in elongate system's locomotion through highly damped environments. We model the elongate, multi-legged system as a ``terrestrial swimmer" in highly frictional environments and implement steering schemes derived from low-order templates of elongate, limbless systems. We identify an effective turning strategy by superimposing two traveling waves of lateral body undulation and further explore variations of the ``turning wave" to enable a spectrum of arc-following steering primitives. We test our hypothesized modulation scheme on a robophysical model and validate steering trajectories against theoretically predicted displacements. We then apply our control framework to Ground Control Robotics' elongate multi-legged robot, Major Tom, using these motion primitives to construct planar motion and in closed-loop control on different terrains. Our work creates a systematic framework for controlling these highly mobile devices in the plane using a low-order model based on sequences of body shape changes.
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