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Abstract Accurate control of a humanoid robot's global position (i.e., its three-dimensional (3D) position in the world) is critical to the reliable execution of high-risk tasks such as avoiding collision with pedestrians in a crowded environment. This paper introduces a time-based nonlinear control approach that achieves accurate global-position tracking (GPT) for multi-domain bipedal walking. Deriving a tracking controller for bipedal robots is challenging due to the highly complex robot dynamics that are time-varying and hybrid, especially for multi-domain walking that involves multiple phases/domains of full actuation, over actuation, and underactuation. To tackle this challenge, we introduce a continuous-phase GPT control law for multi-domain walking, which provably ensures the exponential convergence of the entire error state within the full and over actuation domains and that of the directly regulated error state within the underactuation domain. We then construct sufficient multiple-Lyapunov stability conditions for the hybrid multi-domain tracking error system under the proposed GPT control law. We illustrate the proposed controller design through both three-domain walking with all motors activated and two-domain gait with inactive ankle motors. Simulations of a ROBOTIS OP3 bipedal humanoid robot demonstrate the satisfactory accuracy and convergence rate of the proposed control approach under two different cases of multi-domain walking as well as various walking speed and desired paths. 

Stereovision systems can extract full-field three-dimensional (3D) displacements of structures by processing the images collected with two synchronized cameras. To obtain accurate measurements, the cameras must be calibrated to account for lens distortion (i.e., intrinsic parameters) and compute the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking photos of a calibration object (e.g., a checkerboard) with the two cameras. Because the calibration object must be similar in size to the targeted structure, measurements on large-scale structures are highly impractical. This research proposes a multi-sensor board with three inertial measurement units and a laser distance meter to compute the extrinsic parameters of a stereovision system and streamline the calibration procedure. In this paper, the performances of the proposed sensor-based calibration are compared with the accuracy of the traditional image-based calibration procedure. Laboratory experiments show that cameras calibrated with the multi-sensor board measure displacements with 95% accuracy compared to displacements obtained from cameras calibrated with the traditional procedure. The results of this study indicate that the sensor-based approach can increase the applicability of 3D digital image correlation measurements to large-scale structures while reducing the time and complexity of the calibration. 

Three-dimensional digital image correlation (3D-DIC) has become a strong alternative to traditional contact-based techniques for structural health monitoring. 3D-DIC can extract the full-field displacement of a structure from a set of synchronized stereo images. Before performing 3D-DIC, a complex calibration process must be completed to obtain the stereovision system’s extrinsic parameters (i.e., cameras’ distance and orientation). The time required for the calibration depends on the dimensions of the targeted structure. For example, for large-scale structures, the calibration may take several hours. Furthermore, every time the cameras’ position changes, a new calibration is required to recalculate the extrinsic parameters. The approach proposed in this research allows determining the 3D-DIC extrinsic parameters using the data measured with commercially available sensors. The system utilizes three Inertial Measurement Units with a laser distance meter to compute the relative orientation and distance between the cameras. In this paper, an evaluation of the sensitivity of the newly developed sensor suite is provided by assessing the errors in the measurement of the extrinsic parameters. Analytical simulations performed on a 7.5 x 5.7 m field of view using the data retrieved from the sensors show that the proposed approach provides an accuracy of ~10-6 m and a promising way to reduce the complexity of 3D-DIC calibration. 

To meet 2050 decarbonization goals, Massachusetts will not be able to rely on carbon intensive energy sources (e.g. natural gas and gasoline) and hydrogen has been considered a replacement. To produce hydrogen without carbon emissions, renewable energy sources will be used to power electrolyzer stacks. However, renewable energy sources will also be in high demand for other energy sectors, such as automobiles and electrification. This paper estimates the amount of wind energy needed to replace natural gas with hydrogen and electrify automobiles. Comparisons are also made for a scenario in which heat pumps are used to replace natural gas. These energy sectors represent the bulk of energy consumed within Massachusetts and are of high interest to stakeholders globally. The analysis reveals the daunting amount of wind energy needed for replacement and that it is highly unlikely for hydrogen to replace natural gas in time to meet the state’s climate goals.