发表机构
Technical University of Munich; German Aerospace Center (DLR)（慕尼黑工业大学; 德国航空航天中心（DLR））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出概念验证框架，基于高柔顺性四足机器人eBert的非线性正常模式（NNMs），利用非线性共振实现多步态运动，为设计敏捷高效机器人提供了新方向。
AI 中文摘要
动物的身体形态决定了它们可实现的步态模式，其中机械共振可降低主动控制的需求。通过调整姿势和肌肉刚度，它们利用具身智能实现不同速度下的有效步态。相比之下，大多数四足机器人由于非线性动力学的复杂性，并未专门设计用于利用机械共振，且需要专用的运动控制器。为提供一种替代方案，我们提出了一个概念验证框架，使机器人的非线性动力学在设计过程中具备可预测性，并展示如何利用这一知识，使多步态运动可由重力、惯性和弹性塑造的非线性共振自然产生。我们展示了高柔顺性四足机器人eBert，利用我们的新理论工具在其上识别出6种非线性正常模式（NNMs），并在仿真和硬件中验证了它们的存在。通过黑盒优化确定步长，仿真显示每种NNM自然发展为不同的步态，表现出不同的速度，且该特性在很大程度上可迁移至机器人硬件。我们的实验表明，eBert可利用其机械结构生成特定任务的运动，这或可作为设计新一代利用具身智能的敏捷高效机器人的基础。
英文摘要
Animals' body morphology shapes the gait patterns they can perform, where mechanical resonance reduces the need for active control. By tuning posture and muscle stiffness, they leverage their embodied intelligence to achieve effective gaits for different speeds. In contrast, most quadruped robots are not specifically designed to exploit mechanical resonance due to the complexity of nonlinear dynamics and require dedicated locomotion controllers. To provide an alternative, we present a proof of concept framework making the nonlinear dynamics of a robot predictable in the design process and show how this knowledge can be leveraged such that multi-gait locomotion can emerge from nonlinear resonances, shaped by gravity, inertia, and elasticity. We present the highly compliant quadruped robot eBert, on which we identify six nonlinear normal modes (NNMs) using our new theoretical tools and validate their existence in simulation and hardware. With black-box optimization to determine step length, simulations show how each NNM naturally develops into a distinct gait, manifesting different speeds, which also largely transfers to the robotic hardware. Our experiments show that eBert can exploit its mechanics to generate task-specific movements which may serve as foundation for designing a new generation of agile and efficient robots leveraging embodied intelligence.
