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The mechanical impedance of the human lower-limb joints during locomotion encodes our understanding of how the neuromotor system regulates the behavior of these tasks. Impedance is also a key component of several strategies for translating this behavior to robots, powered prosthetic limbs, and people empowered by exoskeletons. However, due to difficulty in making accurate measurements, there is little empirical evidence for the impedance behaviors of joints other than the ankle during active walking tasks. In this letter we propose a measurement system based on a highly backdrivable quasi-direct-drive actuator and a carefully calibrated actuator torque model. Bench-top validation with known mechanical impedance human-substitutes, confirms the viability of this system as an impedance measurement tool. A pilot study with two subjects utilizing a custom knee-exoskeleton apparatus confirms the feasibility of this system for human walking experiments. 

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. 

Most impedance-based walking controllers for powered knee–ankle prostheses use a finite state machine with dozens of user-specific parameters that require manual tuning by technical experts. These parameters are only appropriate near the task (e.g., walking speed and incline) at which they were tuned, necessitating many different parameter sets for variable-task walking. In contrast, this article presents a data-driven, phase-based controller for variable-task walking that uses continuously variable impedance control during stance and kinematic control during swing to enable biomimetic locomotion. After generating a data-driven model of variable joint impedance with convex optimization, we implement a novel task-invariant phase variable and real-time estimates of speed and incline to enable autonomous task adaptation. Experiments with above-knee amputee participants (N = 2) show that our data-driven controller 1) features highly linear phase estimates and accurate task estimates, 2) produces biomimetic kinematic and kinetic trends as task varies, leading to low errors relative to able-bodied references, and 3) produces biomimetic joint work and cadence trends as task varies. We show that the presented controller meets and often exceeds the performance of a benchmark finite state machine controller for our two participants, without requiring manual impedance tuning. 

Abstract The mechanical impedance of the joints of the leg governs the body's response to external disturbances, and its regulation is essential for the completion of tasks of daily life. However, it is still unclear how this quantity is regulated at the knee during dynamic tasks. In this work, we introduce a method to estimate the mechanical impedance of spring-mass systems using a torque-controllable exoskeleton with the intention of extending these methods to characterize the mechanical impedance of the human knee during locomotion. We characterize system bandwidth and intrinsic impedance and present a perturbation-based methodology to identify the mechanical impedance of known spring-mass systems. Our approach was able to obtain accurate estimates of stiffness and inertia, with errors under 3% and ∼13–16%, respectively. This work provides a qualitative and quantitative foundation that will enable accurate estimates of knee joint impedance during locomotion in future works. 

Recently, brushless motors with especially high torque densities have been developed for applications in autonomous aerial vehicles (i.e. drones), which usually employ exterior rotortype geometries (ER-BLDC motors). These motors are promising for other applications, such as humanoids and wearable robots; however, the emerging companies that produce motors for drone applications do not typically provide adequate technical specifications that would permit their general use across robotics-for example, the specifications are often tested in unrealistic forced convection environments, or are drone-specific, such as thrust efficiency. Furthermore, the high magnetic pole count in many ER-BLDC motors restricts the brushless drives able to efficiently commutate these motors at speeds needed for lightly-geared operation. This paper provides an empirical characterization of a popular ER-BLDC motor and a new brushless drive, which includes efficiencies of the motor across different power regimes, identification of the motor transfer function coefficients, thermal response properties, and closed loop control performance in the time and frequency domains. The intent of this work is to serve as a benchmark and reference for other researchers seeking to utilize these exciting and emerging motor geometries. 

Challenges associated with current prosthetic technologies limit the quality of life of lower-limb amputees. Passive prostheses lead amputees to walk slower, use more energy, fall more often, and modify their gait patterns to compensate for the prosthesis’ lack of net-positive mechanical energy. Robotic prostheses can provide mechanical energy, but may also introduce challenges through controller design. Fortunately, talented researchers are studying how to best control robotic leg prostheses, but the time and resources required to develop prosthetic hardware has limited their potential impact. Even after research is completed, comparison of results is confounded by the use of different, researcher-specific hardware. To address these issues, we have developed the Open-source Leg (OSL): a scalable robotic knee/ankle prosthesis intended to foster investigations of control strategies. This paper introduces the design goals, transmission selection, hardware implementation, and initial control benchmarks for the OSL. The OSL provides a common hardware platform for comparison of control strategies, lowers the barrier to entry for prosthesis research, and enables testing within the lab, community, and at home. 

Challenges associated with current prosthetic technologies limit the quality of life of lower-limb amputees. Passive prostheses lead amputees to walk slower, use more energy, fall more often, and modify their gait patterns to compensate for the prosthesis' lack of net-positive mechanical energy. Robotic prostheses can provide mechanical energy, but may also introduce challenges through controller design. Fortunately, talented researchers are studying how to best control robotic leg prostheses, but the time and resources required to develop prosthetic hardware has limited their potential impact. Even after research is completed, comparison of results is confounded by the use of different, researcher-specific hardware. To address these issues, we have developed the Open-source Leg (OSL): a scalable robotic knee/ankle prosthesis intended to foster investigations of control strategies. This paper introduces the design goals, transmission selection, hardware implementation, and initial control benchmarks for the OSL. The OSL provides a common hardware platform for comparison of control strategies, lowers the barrier to entry for prosthesis research, and enables testing within the lab, community, and at home.