具有感知能力的变步长足部规划用于人类oid在断开脚踏板上的运动
Perceptive Variable-Timing Footstep Planning for Humanoid Locomotion on Disconnected Footholds
AI总结：
本研究提出了一种基于感知的混合整数模型预测控制框架，用于人类oid在断开脚踏板上的运动规划，通过动态调整步长和时间，提高行走的稳定性和鲁棒性。
摘要：
许多现实世界的行走场景包含障碍物和不安全的地面斑块（例如，滑腻或杂乱的区域），留下了一组断开的可接受脚踏点，可以建模为类似石子路的区域。我们提出了一种 onboard 的混合整数模型预测控制框架，该框架联合规划足部放置和步长持续时间，使用步与步之间的发散运动成分（DCM）动力学。将自体中心深度图像融合到概率局部高度图中，从中提取一个凸的可踏区域的并集。区域成员资格通过二进制变量在混合整数二次规划（MIQP）中强制执行。为了在保持优化可行性的同时保证安全性，我们嵌入了DCM空间中的捕获性界限：一个横向的一步条件（防止腿部交叉）和一个纵向的无限步界限，该界限限制了不稳定增长。我们进一步在步内重新规划，通过回传测量的瞬时DCM来更新初始DCM，提高对模型不匹配和外部干扰的鲁棒性。我们在Digit上随机化的石子路场中进行模拟测试，包括外部推搡。规划器生成具有地形意识的动态一致的足部序列，具有自适应的时序和毫秒级的求解时间。
英文摘要：
Many real-world walking scenarios contain obstacles and unsafe ground patches (e.g., slippery or cluttered areas), leaving a disconnected set of admissible footholds that can be modeled as stepping-stone-like regions. We propose an onboard, perceptive mixed-integer model predictive control framework that jointly plans foot placement and step duration using step-to-step Divergent Component of Motion (DCM) dynamics. Ego-centric depth images are fused into a probabilistic local heightmap, from which we extract a union of convex steppable regions. Region membership is enforced with binary variables in a mixed-integer quadratic program (MIQP). To keep the optimization tractable while certifying safety, we embed capturability bounds in the DCM space: a lateral one-step condition (preventing leg crossing) and a sagittal infinite-step bound that limits unstable growth. We further re-plan within the step by back-propagating the measured instantaneous DCM to update the initial DCM, improving robustness to model mismatch and external disturbances. We evaluate the approach in simulation on Digit on randomized stepping-stone fields, including external pushes. The planner generates terrain-aware, dynamically consistent footstep sequences with adaptive timing and millisecond-level solve times.
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