用数据驱动方法实时调整参考轨迹，实现机器人跳跃飞行的超精准控制。
Reference-Steering via Data-Driven Predictive Control for Hyper-Accurate Robotic Flying-Hopping Locomotion
- 基于模型控制上叠加数据驱动输入输出模型，实时修正轨迹偏差。
- 在仿真和真实机器人PogoX上实现跳跃与飞行的高精度轨迹跟踪。
- 方法通用性强，适合各类需要精准运动控制的机器人系统。
先进的基于模型的控制设计已在机器人动态运动中取得成功，但因模型与实际硬件之间的差异，导致飞行、跳跃或行走等运动行为的精度尚未得到充分研究。为解决这一偏差问题，本文提出一种参考轨迹引导方法，在现有模型基础上构建数据驱动输入-输出（DD-IO）模型。该模型以期望轨迹为输入，实际执行轨迹为输出，利用数据驱动预测控制在线调整参考输入，使实际输出匹配预期目标。我们在机器人PogoX上验证了该方法，在仿真与实物平台上均实现了高精度的跳跃与飞行行为。该数据驱动的参考轨迹引导方法简单易用，可广泛应用于各类机器人系统以提升轨迹跟踪精度。
原文摘要 · Abstract (English)
State-of-the-art model-based control designs have been shown to be successful in realizing dynamic locomotion behaviors for robotic systems. The precision of the realized behaviors in terms of locomotion performance via fly, hopping, or walking has not yet been well investigated, despite the fact that the difference between the robot model and physical hardware is doomed to produce inaccurate trajectory tracking. To address this inaccuracy, we propose a referencing-steering method to bridge the model-to-real gap by establishing a data-driven input-output (DD-IO) model on top of the existing model-based design. The DD-IO model takes the reference tracking trajectories as the input and the realized tracking trajectory as the output. By utilizing data-driven predictive control, we steer the reference input trajectories online so that the realized output ones match the actual desired ones. We demonstrate our method on the robot PogoX to realize hyper-accurate hopping and flying behaviors in both simulation and hardware. This data-driven reference-steering approach is straightforward to apply to general robotic systems for performance improvement via hyper-accurate trajectory tracking.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。