Balancing and Step Recovery Capturability via Sums-of-Squares Optimization
Balancing and Step Recovery Capturability via Sums-of-Squares Optimization
复制标题
通过平方和优化实现平衡和阶跃恢复捕获
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Russ Tedrake
中科院分区：
文献类型：
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作者：
Michael Posa;T. Koolen;Russ Tedrake
对腿式机器人的一个基本要求是保持平衡并尽可能防止潜在的破坏性跌倒。作为对外界干扰的反应，可以通过结合主动平衡措施来实现跌倒预防，例如：通过脚踝扭矩和上半身运动，以及通过反应步的放置。虽然人们普遍认为需要迈步来应对大的干扰，但只有最简单的步行模型才能很好地理解主动运动对平衡和步恢复的限制。基于凸优化的验证和控制技术的最新进展使人们能够更全面地了解更复杂模型的局限性和功能。在这项工作中，我们提出了一种算法方法，用于对步行机器人的可行捕获盆地进行形式分析，计算内部和外部近似值以及相应的推动恢复控制策略。超越经典的线性倒立摆模型 (LIPM)，我们分析了一系列基于质心动量的平面行走模型，研究了质心高度、角动量和踩踏过程中的冲击动力学对捕获性的影响。这种形式分析可以明确计算这些模型之间的差异，并评估最简单的模型在设计推送恢复控制策略时是否最终会牺牲能力，从而牺牲稳定性。
A fundamental requirement for legged robots is to maintain balance and prevent potentially damaging falls whenever possible. As a response to outside disturbances, fall prevention can be achieved by a combination of active balancing actions, e.g. through ankle torques and upper-body motion, and through reactive step placement. While it is widely accepted that stepping is required to respond to large disturbances, the limits of active motions on balancing and step recovery are only well understood for the simplest of walking models. Recent advances in convex optimization-based verification and control techniques enable a more complete understanding of the limits and capabilities of more complex models. In this work, we present an algorithmic approach for formal analysis of the viable-capture basins of walking robots, calculating both inner and outer approximations and corresponding push recovery control strategies. Extending beyond the classic Linear Inverted Pendulum Model (LIPM), we analyze a series of centroidal momentum based planar walking models, examining the effects of center of mass height, angular momentum, and impact dynamics during stepping on capturability. This formal analysis enables an explicit calculation of the differences between these models, and assessment of whether the simplest models ultimately sacrifice capability, and thus stability, when designing push recovery control policies.