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ABSTRACTDrilling vibrations can cause inefficient drilling and accelerated damage to system components. Therefore, reducing or eliminating such vibrations is a major focus area for natural gas and geothermal drilling applications. One particularly important vibration mode is stick-slip. Stick-slip occurs when the bottom-hole angular velocity starts oscillating while the top hole angular velocity remains relatively constant. This not only causes poor drilling, it is also difficult to detect using surface sensors. In this work, we describe the development and testing of a benchtop drilling system for studying stick-slip dynamics and mitigation. We show how this system can produce stick-slip oscillations. Next, we use this data to formulate a data-driven rock-bit interaction model. This model can be combined with linear systems analysis to predict stick-slip and understand mitigation methods. We describe out instrumentation that enables closed-loop control under simulated communications constraints. We conclude by providing preliminary experimental data on bench-level stick-slip. INTRODUCTIONExploration via autonomous drilling processes for geothermal resources is an important focus area for drilling research. However, to fully realize the clean-energy promise of geothermal energy, key challenges still need to be resolved.Issues arising in the drilling process often originate from a drillstring's increased susceptibility to vibrational oscillations as depths increase. Some examples of drilling vibrations include stick-slip (Navarro-Lopez and Suarez, 2004), bit-bounce (Spanos et al., 1995), and whirl (Jansen, 1991). Torsional oscillations are the focus of this work.Torsional vibrations result in a destructive phenomenon known as stick-slip. Initiated at the bit-rock surface, the drillstring bit experiences large angular velocity oscillations not seen at the surface (Pavone and Desplans, 1994; Besselink et al., 2011; Kessai et al., 2020). Stick-slip results in premature bit wear and drillstring fracture.Stick-slip is a fundamentally nonlinear and unpredictable phenomena. Stick-slip results from the combination of bit-rock interactions and drillstring compliance. As a result, there is a key need for experimental studies of stick-slip dynamics and mitigation. 

Estimating human joint moments using wearable sensors has utility for personalized health monitoring and generalized exoskeleton control. Data-driven models have potential to map wearable sensor data to human joint moments, even with a reduced sensor suite and without subject-specific calibration. In this study, we quantified the RMSE and R 2 of a temporal convolutional network (TCN), trained to estimate human hip moments in the sagittal plane using exoskeleton sensor data (i.e., a hip encoder and thigh- and pelvis-mounted inertial measurement units). We conducted three analyses in which we iteratively retrained the network while: 1) varying the input sequence length of the model, 2) incorporating noncausal data into the input sequence, thus delaying the network estimates, and 3) time shifting the labels to train the model to anticipate (i.e., predict) human hip moments. We found that 930 ms of causal input data maintained model performance while minimizing input sequence length (validation RMSE and R 2 of 0.141±0.014 Nm/kg and 0.883±0.025, respectively). Further, delaying the model estimate by up to 200 ms significantly improved model performance compared to the best causal estimators (p<0.05), improving estimator fidelity in use cases where delayed estimates are acceptable (e.g., in personalized health monitoring or diagnoses). Finally, we found that anticipating hip moments further in time linearly increased model RMSE and decreased R 2 (p<0.05); however, performance remained strong (R 2 >0.85) when predicting up to 200 ms ahead. 

The fit of a wearable device, such as a prosthesis, can be quantitatively characterized by the mechanical coupling at the user-device interface. It is thought that the mechanical impedance, specifically the stiffness and damping, of wearable device interfaces can significantly impact human performance while using them. To test this theory, we develop a forearm-mounted testbed with a motorized, two degree of freedom (2-DOF) gimbal to simulate variations in the mechanical fit of an upper-extremity wearable device during pointing and target tracking tasks. The two gimbal motors are impedance-controlled to vary the mechanical stiffness and damping between the user and the device's laser pointer end-effector. In this paper, experiments are conducted to determine the torque constants of the motors before implementation in the testbed, and to validate the accuracy of the joint impedance controller. The completed impedance-controlled wearable interface testbed is validated further by comparing the gimbal joint displacements and torques, recorded during 2-DOF base excitation experiments, to MATLAB Simulink simulation data. 

This paper presents a novel physical gripping framework intended for controlled, high force density attachment on a range of surfaces. Our framework utilizes a light-activated chemical adhesive to attach to surfaces. The cured adhesive is part of a "sacrificial layer," which is shed when the gripper separates from the surface. In order to control adhesive behavior we utilize ultraviolet (UV) light sensitive acrylics which are capable of rapid curing when activated with 380nm light. Once cured, zero input power is needed to hold load. Thin plastic parts can be used as the sacrificial layers, and these can be released using an electric motor. This new gripping framework including the curing load capacity, adhesive deposition, and sacrificial methods are described in detail. Two proof-of concept prototypes are designed, built, and tested. The experimental results illustrate the response time (15-75s depending on load), high holding force-to-weight ratio (10-30), and robustness to material type. Additionally, two drawbacks of this design are discussed: corruption of the gripped surface and a limited number of layers. 

Soft materials and compliant actuation concepts have generated new design and control approaches in areas from robotics to wearable devices. Despite the potential of soft robotic systems, most designs currently use hard pumps, valves, and electromagnetic actuators. In this work, we take a step towards fully soft robots by developing a new compliant electromagnetic actuator architecture using gallium-indium liquid metal conductors, as well as compliant permanent magnetic and compliant iron composites. Properties of the new materials are first characterized and then co-fabricated to create an exemplary biologically-inspired soft actuator with pulsing or grasping motions, similar to Xenia soft corals. As current is applied to the liquid metal coil, the compliant permanent magnetic tips on passive silicone arms are attracted or repelled. The dynamics of the robotic actuator are characterized using stochastic system identification techniques and then operated at the resonant frequency of 7 Hz to generate high-stroke (>6 mm) motions. 

Robotic grasping can enable mobile vehicles to physically interact with the environment for delivery, repositioning, or landing. However, the requirements for grippers on mobile vehicles differ substantially from those used for conventional manipulation. Specifically, grippers for dynamic mobile robots should be capable of rapid activation, high force density, low power consumption, and minimal computation. In this work, we present a biologically-inspired robotic gripper designed specifically for mobile platforms. This design exploits a bistable shell to achieve “reflexive” activation based on contact with the environment. The mechanism can close its grasp within 0. 12s without any sensing or control. Electrical input power is not required for grasping or holding load. The reflexive gripper utilizes a novel pneumatic design to open its grasp with low power, and the gripper can carry slung loads up to 28 times its weight. This new mechanism, including the kinematics, static behavior, control structure, and fabrication, is described in detail. A proof of concept prototype is designed, built, and tested. Experimental results are used to characterize performance and demonstrate the potential of these methods.