The stiffness of passive lower-limb exoskeletons and orthoses governs their assistance. A common practice in the design of these systems is to assume the stiffness of the device is determined only by the intended elastic element (e.g., spring), while the structural components, human attachments, and soft tissues are considered rigid. In practice, the mechanical behavior of orthoses is significantly affected by the compliance of these elements, which drastically impacts the assistance provided. In this work, we present a linkage model with compliant elements that can accurately predict the applied stiffness of ankle-foot orthoses, and retroactively estimate the stiffness of unintended spring elements from published data. The compliant model accurately predicted the torque trajectories of two published passive orthoses with modeled peak torques within 4 % to 7 % of measured values. In contrast, the rigid model greatly overestimated the peak torques, predicting 203 % to 376 % of the measured values. The compliant model also indicated that an onboard joint encoder could only measure 52 % to 69 % of the peak ankle angle recorded with motion capture. The compliant model was also used to reassess the stiffness range of a variable-stiffness orthosis, indicating that its adjustable range is likely 69 % of rigid model predictions. Overall, this work highlights the need to consider how unmodeled compliance affects the mechanical behavior of orthoses and provides a foundation for further exploration.

Exoskeleton controllers have recently employed machine learning (ML) techniques to provide appropriate assistance throughout the terrains of the real world. One successful approach has been to learn a mapping between an exoskeleton wearer's kinematic measurements and a gait state vector that encodes how the wearer is currently walking (i.e. gait phase, speed), and then dynamically update the assistance based on the gait state. However, these methods require paired datasets of input kinematics to output gait states, which usually involves manual, time-consuming labeling of data from participants wearing specific exoskeletons and thus limits the scalability of these ML methods. A prior solution to this challenge—leveraging large pre-labeled datasets of normative human walking—introduces another problem, in that networks trained on these datasets learn only normative locomotion patterns, and thus may deteriorate when the data are changed by wearing the exoskeleton itself. In this context, we present an unsupervised-learning-based approach to both bypass the requirement of labeled data for gait state prediction and address the difficulty of domain adaptation from normative to exoskeleton-assisted walking. We validate our method in a set of walking simulations that featured exoskeleton data from 14 participants. This model showed significant improvements in state estimation relative to a model trained solely on pre-labeled normative walking, while also not requiring ground truth labels. This work presents a foundation that demonstrates labeled, device-specific data may not be required for predicting walking behavior in real time.

Springs are essential mechanical elements that are used across a wide variety of industries and mechanisms. Common across many spring types and applications is the importance of compactness, low mass and customizability. In this paper, we present a novel rotary spring design that is lightweight, compact and customizable. In addition, we empirically validate the design by experimentally quantifying the performance of two test springs on a custom dynamometry testbed. Our two-part spring geometry is comprised of a central rotating gear-like cam shaft, and a disk that includes a circular array of radially-spaced tapered cantilevered beams. The two springs that we designed and tested matched desired performance specifications within 3–6%, confirming the efficacy of this unique design approach.

Exoskeletons have shown great potential to enhance locomotion by augmenting the lower limb. While most research has focused on steady-state ambulatory activities, the ability to assist transient, ballistic tasks is also important for understanding the potential of exoskeletons in mobility enhancement. In this preliminary study (N = 5), we developed an individually-customized control strategy to assist vertical jumping. The control strategy was deployed on bilateral ankle exoskeletons (ExoBoot, Dephy Inc.). We structured the control strategy as a work loop that parameterized the assistance provided during the jump. We show that configuring the controller based on individual biomechanics and user preferences facilitates increased vertical jump height when using exoskeleton assistance. In addition, we demonstrate that a user's squat depth can have a significant (p < 0.05) impact on height achieved, but that this depth does not need to be optimized; rather, the exoskeleton provides the maximum performance assistance from both preferred- and deep-squat conditions. Jump height increased by 7.2% with the exoskeleton at its maximum assistance setting, which is comparable to or greater than previous systems.

Modern exoskeletons are typically developed to optimize for a single, physiological objective, the “gold standard” of which is a reduction of the wearer's metabolic rate. However, recent research suggests that these changes in metabolic rate are not yet perceivable on average. To address this gap, this study explores a novel economic value metric to quantify the value of exoskeleton assistance. The overarching goal of this work is the development of a perceptible metric that leverages the user experience to quantify exoskeleton success. We use the Vickrey second-price auction to obtain the monetary compensation needed for participants to continue walking for consecutive two-minute bouts. Comparing the participant's bidding trends when wearing and not wearing an exoskeleton captures the economic value of the experience, termed Marginal Value (MV). To reduce the logistical burden of the auction, we simulated human participants (robo-bidders) to compete alongside real participants. This work presents a sensitivity analysis to understand how the number and bidding behavior of the robo-bidders affects our economic value metric, MV. We found that MV was not significantly affected by the number of robo-bidders or their bidding behavior (i.e. effort rate). The bidding behavior of the human participants was affected by the robo-bidder effort rate, indicating that there is interplay in the bidding dynamics among the auction participants, but these changes do not significantly affect the marginal value. This study tentatively validates the current approach in generating our proposed metric for exoskeleton success, paving the way for economic value to be further explored as a holistic, personalized metric for the development of lower-limb exoskeletons.

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.

Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 ± 2.4%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 ± 2.7%).

Impressive animal locomotion capabilities are mediated by the co-evolution of the skeletal morphology and muscular properties. Legged robot performance would also likely benefit from the co-optimization of actuators and leg morphology. However, development of custom actuators for legged robots is expensive and time consuming, discouraging application-specific actuator optimization. This paper presents open-source designs for two quasi-direct-drive actuators with performance regimes appropriate for an 8–15 kg robot, built from off the shelf and 3D-printed components for less than $200 USD each. The mechanical, electrical, and thermal properties of each actuator are characterized and compared to benchmark data. Actuators subjected to 420k strides of gait data experienced only a 2% reduction in efficiency and 26 mrad in backlash growth, demonstrating viability for rigorous and sustained research applications. We present a thermal solution that nearly doubles the thermally-driven torque limits of our plastic actuator design. The performance results are comparable to traditional metallic actuators for use in high-speed legged robots of the same scale. These 3D printed designs demonstrate an approach for designing and characterizing low-cost, highly customizable and reproducible actuators, democratizing the field of actuator design and enabling co-design and optimization of actuators and robot legs.

Most controllers for lower-limb robotic prostheses require individually tuned parameter sets for every combination of speed and incline that the device is designed for. Because ambulation occurs over a continuum of speeds and inclines, this design paradigm requires tuning of a potentially prohibitively large number of parameters. This limitation motivates an alternative control framework that enables walking over a range of speeds and inclines while requiring only a limited number of tunable parameters. In this work, we present the implementation of a continuously varying kinematic controller on a custom powered knee-ankle prosthesis. The controller uses a phase variable derived from the residual thigh angle, along with real-time estimates of ground inclination and walking speed, to compute the appropriate knee and ankle joint angles from a continuous model of able-bodied kinematic data. We modify an existing phase variable architecture to allow for changes in speeds and inclines, quantify the closed-loop accuracy of the speed and incline estimation algorithms for various references, and experimentally validate the controller by observing that it replicates kinematic trends seen in able-bodied gait as speed and incline vary.

Natural dynamics, nonlinear optimization, and, more recently, convex optimization are available methods for stiffness design of energy-efficient series elastic actuators. Natural dynamics and general nonlinear optimization only work for a limited set of load kinetics and kinematics, cannot guarantee convergence to a global optimum, or depend on initial conditions to the numerical solver. Convex programs alleviate these limitations and allow a global solution in polynomial time, which is useful when the space of optimization variables grows (e.g., when designing optimal nonlinear springs or co-designing spring, controller, and reference trajectories). Our previous work introduced the stiffness design of series elastic actuators via convex optimization when the transmission dynamics are negligible, which is an assumption that applies mostly in theory or when the actuator uses a direct or quasi-direct drive. In this work, we extend our analysis to include friction at the transmission. Coulomb friction at the transmission results in a non-convex expression for the energy dissipated as heat, but we illustrate a convex approximation for stiffness design. We experimentally validated our framework using a series elastic actuator with specifications similar to the knee joint of the Open Source Leg, an open-source robotic knee-ankle prosthesis.

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.

Modern passive ankle-foot prostheses do not exhibit appropriate biomechanics during walking, and are unable to adjust their mechanics for other mobility tasks, such as stair traversal or quiet standing. In this paper, we introduce a quasi-passive ankle-foot prosthesis that addresses these challenges; the ankle has a customizable, nonlinear torque-angle curve, and the overall stiffness can be varied continuously between mobility tasks. The variation in mechanics is accomplished by integrating two mechanisms: a cam-based transmission, in which rotation of the ankle joint causes deflection of a leaf spring, and an active sliding support beneath the leaf spring, which can modify the spring's effective stiffness. In addition to introducing the design, we present the mathematics to calculate the cam profile for any arbitrary torque-angle curve, and experimentally characterize the system for a desired curve based on human walking. Lastly, we demonstrate the full range of stiffness levels available and stiffness transition time.