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Emerging wearable, assistive, and mobile robots seek to interact with the environment and/or humans in a compliant, dynamic, and adaptable way. Springs are critical to achieving this objective, but the associated increase in volume, mass, and complexity is limiting their application and impact in this rapidly developing field. This article presents a novel rotary spring architecture that is both lightweight and compact. Our two-part spring consists of radially-spaced cantilever beams that interface with an internal, gear-like camshaft. We present the concept and equations governing their mechanics and design. To facilitate broad adoption, we introduce an open-source design tool, which enables the design of custom springs in minutes instead of hours or days. We also empirically demonstrate our design with four test springs and validate the achievement of target spring rates and deflections. Finally, we present several redesigns of existing springs in the robotics literature to demonstrate the wide applicability of our spring architecture. 

Accurate impedance control is key for biomimetic mechanical behavior in lower-limb robotic prostheses. However, due to compliance, friction, and inertia in the drivetrain, the commonly used open-loop impedance control strategy can often produce inaccurate results without appropriate compensation. This article presents a controller that accounts for these dynamics to improve the impedance rendering accuracy of a robotic prosthesis research platform, the Open-Source Leg (OSL v2). We first develop a dynamic model of the OSL v2’s drivetrain and show that it accurately predicts the system's joint torque with 97% mean explained variance across a diverse array of experiments. We then present a controller that compensates for the OSL v2’s inherent dynamics using a combination of feedback linearization and actuator-state feedback control. We experimentally validate this controller on the OSL v2 with a rotary dynamometer and in treadmill walking experiments. We show that it can render various constant impedance behaviors with higher stiffness and damping accuracy than a baseline controller. We also show our controller's ability to replicate the variable impedance trajectories of the human ankle joint, suggesting that this control approach could enable robotic prostheses that are biomimetic in their mechanical impedance in addition to their kinematics and kinetics. 

Robotic knee-ankle prostheses have often fallen short relative to passive microprocessor prostheses in time-based clinical outcome tests. User ambulation endurance is an alternative clinical outcome metric that may better highlight the benefits of robotic prostheses. However, previous studies were unable to show endurance benefits due to inaccurate high-level classification, discretized mid-level control, and insufficiently difficult ambulation tasks. In this case study, we present a phase-based mid-level prosthesis controller which yields biomimetic joint kinematics and kinetics that adjust to suit a continuum of tasks. We enrolled an individual with an above-knee amputation and challenged him to perform repeated, rapid laps of a circuit comprising activities of daily living with both his passive prosthesis and a robotic prosthesis. The participant demonstrated improved endurance with the robotic prosthesis and our mid-level controller compared to his passive prosthesis, completing over twice as many total laps before fatigue and muscle discomfort required him to stop. We also show that time-based outcome metrics fail to capture this endurance improvement, suggesting that alternative metrics related to endurance and fatigue may better highlight the clinical benefits of robotic prostheses. 

Powered knee-ankle prostheses can offer benefits over conventional passive devices during stair locomotion by providing biomimetic net-positive work and active control of joint angles. However, many modern control approaches for stair ascent and descent are often limited by time-consuming hand-tuning of user/task-specific parameters, predefined trajectories that remove user volition, or heuristic approaches that cannot be applied to both stair ascent and descent. This work presents a phase-based hybrid kinematic and impedance controller (HKIC) that allows for semi-volitional, biomimetic stair ascent and descent at a variety of step heights. We define a unified phase variable for both stair ascent and descent that utilizes lower-limb geometry to adjust to different users and step heights. We extend our prior data-driven impedance model for variable-incline walking, modifying the cost function and constraints to create a continuously-varying impedance parameter model for stair ascent and descent over a continuum of step heights. Experiments with above-knee amputee participants (N=2) validate that our HKIC controller produces biomimetic ascent and descent joint kinematics, kinetics, and work across four step height configurations. We also show improved kinematic performance with our HKIC controller in comparison to a passive microprocessor-controlled device during stair locomotion. 

Although the average healthy adult transitions from sit to stand over 60 times per day, most research on powered prosthesis control has only focused on walking. In this paper, we present a data-driven controller that enables sitting, standing, and walking with minimal tuning. Our controller comprises two high level modes of sit/stand and walking, and we develop heuristic biomechanical rules to control transitions. We use a phase variable based on the user's thigh angle to parameterize both walking and sit/stand motions, and use variable impedance control during ground contact and position control during swing. We extend previous work on data-driven optimization of continuous impedance parameter functions to design the sit/stand control mode using able-bodied data. Experiments with a powered knee-ankle prosthesis used by a participant with above-knee amputation demonstrate promise in clinical outcomes, as well as trade-offs between our minimal-tuning approach and accommodation of user preferences. Specifically, our controller enabled the participant to complete the sit/stand task 20% faster and reduced average asymmetry by half compared to his everyday passive prosthesis. The controller also facilitated a timed up and go test involving sitting, standing, walking, and turning, with only a mild (10%) decrease in speed compared to the everyday prosthesis. Our sit/stand/walk controller enables multiple activities of daily life with minimal tuning and mode switching. 

Passive prostheses cannot provide the net positive work required at the knee and ankle for step-over stair ascent. Powered prostheses can provide this net positive work, but user synchronization of joint motion and power input are critical to enabling natural stair ascent gaits. In this work, we build on previous phase variable-based control methods for walking and propose a stair ascent controller driven by the motion of the user's residual thigh. We use reference kinematics from an able-bodied dataset to produce knee and ankle joint trajectories parameterized by gait phase. We redefine the gait cycle to begin at the point of maximum hip flexion instead of heel strike to improve the phase estimate. Able-bodied bypass adapter experiments demonstrate that the phase variable controller replicates normative able-bodied kinematic trajectories with a root mean squared error of 12.66 deg and 2.64 deg for the knee and ankle, respectively. The knee and ankle joints provided on average 0.387J/kg and 0.212J/kg per stride, compared to the normative averages of 0.335J/kg and 0.207J/kg, respectively. Thus, this controller allows powered knee-ankle prostheses to perform net positive mechanical work to assist stair ascent. 

This paper presents a new model and phase-variable controller for sit-to-stand motion in above-knee amputees. The model captures the effect of work done by the sound side and residual limb on the prosthesis, while modeling only the prosthetic knee and ankle with a healthy hip joint that connects the thigh to the torso. The controller is parametrized by a biomechanical phase variable rather than time and is analyzed in simulation using the model. We show that this controller performs well with minimal tuning, under a range of realistic initial conditions and biological parameters such as height and body mass. The controller generates kinematic trajectories that are comparable to experimentally observed trajectories in non-amputees. Furthermore, the torques commanded by the controller are consistent with torque profiles and peak values of normative human sit-to-stand motion. Rise times measured in simulation and in non-amputee experiments are also similar. Finally, we compare the presented controller with a baseline proportional-derivative controller demonstrating the advantages of the phase-based design over a set-point based design.