具有稳定性的反应式步态组合：在静态和移动障碍物中的动态行走
Reactive Gait Composition with Stability: Dynamic Walking amidst Static and Moving Obstacles
AI总结：
提出基于 SSME 与 DMP 切换约束的模块化反应式步态规划框架，可先验保证动态双足行走在静态和移动障碍物环境中的稳定执行。
摘要：
本文提出了一种模块化运动规划方法，为通过周期性行为在变化环境中移动的机器人提供可证明的稳定性保证。我们将动态步行者作为此类系统的范例加以关注，尽管本文开发的工具也可用于支持采用 Dynamic Movement Primitives（DMP，动态运动基元）的通用组合式机器人运动规划方法。我们的方法先验地确保所提出的计划能够被稳定执行。这是通过将规划过程表述为具有多个平衡点的切换系统（Switching System with Multiple Equilibria，SSME），并证明在对 DMP 之间切换频率施加适当约束的情况下，系统演化始终保持在状态空间中明确刻画的捕获区域内来实现的。这些条件有效地将底层稳定性限制封装为一种易于传递给规划器的形式，以保证所提出的计划与机器人动力学相容。此外，我们展示了如何以滚动时域方式在线安全组合可用基元，使机器人能够对移动障碍物作出反应。所提出的框架在常见建模假设下应用于三维双足行走模型，并提供了一种模块化方法，用于稳定集成现成的底层运动控制与高层规划方法。
英文摘要：
This paper presents a modular approach to motion planning with provable stability guarantees for robots that move through changing environments via periodic locomotion behaviors. We focus on dynamic walkers as a paradigm for such systems, although the tools developed in this paper can be used to support general compositional approaches to robot motion planning with Dynamic Movement Primitives (DMPs). Our approach ensures a priori that the suggested plan can be stably executed. This is achieved by formulating the planning process as a Switching System with Multiple Equilibria (SSME) and proving that the system's evolution remains within explicitly characterized trapping regions in the state space under suitable constraints on the frequency of switching among the DMPs. These conditions effectively encapsulate the low-level stability limitations in a form that can be easily communicated to the planner to guarantee that the suggested plan is compatible with the robot's dynamics. Furthermore, we show how the available primitives can be safely composed online in a receding horizon manner to enable the robot to react to moving obstacles. The proposed framework is applied on 3D bipedal walking models under common modeling assumptions, and offers a modular approach towards stably integrating readily available low-level locomotion control and high-level planning methods.
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。
