As bipedal robots become more and more popular in commercial and industrial settings, the ability to control them with a high degree of reliability is critical. To that end, this paper considers how to accurately estimate which feet are currently in contact with the ground so as to avoid improper control actions that could jeopardize the stability of the robot. Additionally, modern algorithms for estimating the position and orientation of a robot's base frame rely heavily on such contact mode estimates. Dedicated contact sensors on the feet can be used to estimate this contact mode, but these sensors are prone to noise, time delays, damage/yielding from repeated impacts with the ground, and are not available on every robot. To overcome these limitations, we propose a momentum observer based method for contact mode estimation that does not rely on such contact sensors. Often, momentum observers assume that the robot's base frame can be treated as an inertial frame. However, since many humanoids' legs represent a significant portion of the overall mass, the proposed method instead utilizes multiple simultaneous dynamic models. Each of these models assumes a different contact condition. A given contact assumption is then used to constrain the full dynamics in order to avoid assuming that either the body is an inertial frame or that a fully accurate estimate of body velocity is known. The (dis)agreement between each model's estimates and measurements is used to determine which contact mode is most likely using a Markov-style fusion method. The proposed method produces contact detection accuracy of up to 98.44% with a low noise simulation and 77.12% when utilizing data collect on the Sarcos Guardian XO robot (a hybrid humanoid/exoskeleton).

翻译：随着双足机器人在商业和工业场景中的应用日益广泛，实现高可靠性的控制能力变得至关重要。为此，本文研究了如何准确估计机器人当前与地面接触的足部状态，以避免因控制不当而危及机器人的稳定性。此外，现代用于估计机器人基座坐标系位置和方向的算法也高度依赖于此类接触模式估计。虽然可通过安装在足部的专用接触传感器来估计接触模式，但这些传感器易受噪声干扰、存在时间延迟、会因与地面的反复冲击而损坏或失效，且并非所有机器人都配备此类传感器。为克服这些局限，本文提出一种基于动量观测器的接触模式估计方法，该方法无需依赖接触传感器。传统的动量观测器通常假设机器人基座坐标系可视为惯性系，但由于许多人形机器人的腿部质量占整体质量的很大比例，本文提出的方法转而采用多个同步动力学模型。每个模型对应不同的接触条件，通过给定的接触假设对完整动力学模型施加约束，从而避免将机身视为惯性系或假设已知完全精确的机身速度估计。各模型估计值与测量值之间的（不）一致性，通过马尔可夫式融合方法来判断最可能的接触模式。所提方法在低噪声仿真中实现了高达98.44%的接触检测准确率，而在使用Sarcos Guardian XO机器人（一种混合人形/外骨骼机器人）采集的数据时，准确率达到77.12%。