How to run 50% faster without external energy

Elastic ankle exoskeletons influence soleus fascicle dynamics during unexpected perturbations

Spring-based passive ankle exoskeletons have been designed to emulate the energy conservation and power amplification roles of biological muscle-tendon units during locomotion. Yet, it remains unknown if similar assistive devices can serve the other elastomechanical role of biological muscle-tendon units - power attenuation. Here we explored the effect of bilateral passive ankle exoskeletons on neuromuscular control and muscle fascicle dynamics in the ankle plantarflexors during rapid, unexpected vertical perturbations. We recorded muscle activation and soleus fascicle length changes during hopping with and without exoskeleton assistance (0 and 76 Nm rad-1) on elevated platforms (20 cm), which were removed at an unknown time. Our results demonstrate that exoskeleton assistance leads to a reduction in soleus muscle activation, increases in fascicle length change and decreases in muscle forces during perturbed hopping. These changes have competing effects on the mechanics and energetics of lower limb muscles, likely limiting the capacity for series elastic tissues to absorb energy. As we strive towards the design of wearable assistive devices for everyday locomotion, information regarding real-time muscle-tendon behavior may enable tunable assistance that adapts to both the user and the environment.

Dynamic Finite Element Model Based on Timoshenko Beam Theory for Simulating High-Speed Nonlinear Helical Springs

Helical springs with nonlinear geometric parameters nowadays have shown great advantages over classical linear springs, especially due to their superior performance in diminishing dynamic responses in high-speed situations. However, existing studies are mostly available for springs with linear properties, and the sole FE spring models using solid elements occupy significant computational resources. This study presents an FE spring model based on Timoshenko beam theory, which allows for high-speed dynamic simulations of nonlinear springs using a beehive valve spring sample. The dynamic results are also compared with the results of the FE model using solid elements and the results of the engine head test and indicate that the proposed FE model can accurately predict dynamic spring forces and the phenomenon of coil clash when simulating the beehive spring at engine speeds of both 5600 and 8000 RPM. The results also indicate that rapid coil impact brings significant spike forces. It should also be noted that the FE spring model using beam elements displays sufficient accuracy in predicting the dynamic responses of nonlinear springs while occupying much fewer computational resources than the FE model using solid elements.

A Tunable, Simplified Model for Biological Latch Mediated Spring Actuated Systems

We develop a model of latch-mediated spring actuated (LaMSA) systems relevant to comparative biomechanics and bioinspired design. The model contains five components: two motors (muscles), a spring, a latch, and a load mass. One motor loads the spring to store elastic energy and the second motor subsequently removes the latch, which releases the spring and causes movement of the load mass. We develop freely available software to accompany the model, which provides an extensible framework for simulating LaMSA systems. Output from the simulation includes information from the loading and release phases of motion, which can be used to calculate kinematic performance metrics that are important for biomechanical function. In parallel, we simulate a comparable, directly actuated system that uses the same motor and mass combinations as the LaMSA simulations. By rapidly iterating through biologically relevant input parameters to the model, simulated kinematic performance differences between LaMSA and directly actuated systems can be used to explore the evolutionary dynamics of biological LaMSA systems and uncover design principles for bioinspired LaMSA systems. As proof of principle of this concept, we compare a LaMSA simulation to a directly actuated simulation that includes either a Hill-type force-velocity trade-off or muscle activation dynamics, or both. For the biologically-relevant range of parameters explored, we find that the muscle force-velocity trade-off and muscle activation have similar effects on directly actuated performance. Including both of these dynamic muscle properties increases the accelerated mass range where a LaMSA system outperforms a directly actuated one.

Theory of Fast Walking With Human-Driven Load-Carrying Robot Exoskeletons

Reaching and maintaining high walking speeds is challenging for a human when carrying extra weight, such as walking with a heavy backpack. Robotic limbs can support a heavy backpack when standing still, but accelerating a backpack within a couple of steps to race-walking speeds requires limb force and energy beyond natural human ability. Here, we conceive a human-driven robot exoskeleton that could accelerate a heavy backpack faster and maintain top speeds higher than what the human alone can when not carrying a backpack. The key components of the exoskeleton are the mechanically adaptive but energetically passive spring limbs. We show that by optimally adapting the stiffness of the limbs, the robot can achieve near-horizontal center of mass motion to emulate the load-bearing mechanics of the bicycle. We find that such an exoskeleton could enable the human to accelerate one extra body weight up to top race-walking speeds in ten steps. Our finding predicts that human-driven mechanically adaptive robot exoskeletons could extend human weight-bearing and fast-walking ability without using external energy.

Deep Reinforcement Learning with Gait Mode Specification for Quadrupedal Trot-Gallop Energetic Analysis

Quadruped system is an animal-like model which has long been analyzed in terms of energy efficiency during its various gait locomotion. The generation of certain gait modes on these systems has been achieved by classical controllers which demand highly specific domain-knowledge and empirical parameter tuning. In this paper, we propose to use deep reinforcement learning (DRL) as an alternative approach to generate certain gait modes on quadrupeds, allowing potentially the same energetic analysis without the difficulty of designing an ad hoc controller. We show that by specifying a gait mode in the process of learning, it allows faster convergence of the learning process while at the same time imposing a certain gait type on the systems as opposed to the case without any gait specification. We demonstrate the advantages of using DRL as it can exploit automatically the physical condition of the robots such as the passive spring effect between the joints during the learning process, similar to the adaptation skills of an animal. The proposed system would provide a framework for quadrupedal trot-gallop energetic analysis for different body structures, body mass distributions and joint characteristics using DRL.