用数据驱动方法实现外骨骼实时自适应行走
Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis
- 结合步态规划与轨迹生成，统一求解动态行走问题
- 在Atalante外骨骼上验证，提升环境适应能力
- 支持在线重规划，适合复杂动态场景
外骨骼稳健双足行走需实时响应环境变化。本文提出混合数据驱动预测控制（HDDPC）框架，扩展了数据启用的预测控制方法，通过同步规划足部接触时序与连续轨迹，实现动态适应。该框架基于汉克尔矩阵建模系统动力学，并引入步间（S2S）转移机制增强适应性。通过融合接触调度与轨迹规划，提供高效统一的运动合成方案，支持在线重规划，实现鲁棒且响应迅速的行走。我们在Atalante外骨骼上验证了该方法，结果表明其显著提升了鲁棒性与环境适应能力。
原文摘要 · Abstract (English)
Robust bipedal locomotion in exoskeletons requires the ability to dynamically react to changes in the environment in real time. This paper introduces the hybrid data-driven predictive control (HDDPC) framework, an extension of the data-enabled predictive control, that addresses these challenges by simultaneously planning foot contact schedules and continuous domain trajectories. The proposed framework utilizes a Hankel matrix-based representation to model system dynamics, incorporating step-to-step (S2S) transitions to enhance adaptability in dynamic environments. By integrating contact scheduling with trajectory planning, the framework offers an efficient, unified solution for locomotion motion synthesis that enables robust and reactive walking through online replanning. We validate the approach on the Atalante exoskeleton, demonstrating improved robustness and adaptability.
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