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日益普及，实现高可靠性的控制能力变得至关重要。为此，本文研究如何准确估计机器人当前与地面接触的足部状态，以避免不当的控制动作危及机器人稳定性。此外，现代用于估计机器人基座坐标系位置与方向的算法也高度依赖此类接触模式估计。虽然可在足部安装专用接触传感器来估计接触模式，但这些传感器易受噪声、时延、与地面反复冲击导致的损坏/失效影响，且并非所有机器人都配备此类传感器。为克服这些限制，我们提出一种基于动量观测器的接触模式估计方法，该方法不依赖此类接触传感器。传统的动量观测器通常假设机器人基座坐标系可被视为惯性系。然而，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，并通过给定的接触假设对完整动力学模型施加约束，从而避免假设基座为惯性系或要求已知完全精确的基座速度估计。各模型估计值与测量值的（不）一致性，通过马尔可夫式融合方法用于判定最可能的接触模式。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，在使用Sarcos Guardian XO机器人（一种混合人形/外骨骼系统）采集的数据上达到了77.12%的准确率。