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Many studies of the flow of energy between the body, muscles, and elastic elements highlight advantages of the storage and recovery of elastic energy. The spring-like action of structures associated with muscles allows for movements that are less costly, more powerful and safer than would be possible with contractile elements alone. But these actions also present challenges that might not be present if the pattern of energy flow were simpler, for example, if power were always applied directly from muscle to motions of the body. Muscle is under the direct control of the nervous system, and precise modulation of activity can allow for finely controlled displacement and force. Elastic structures deform under load in a predictable way, but are not under direct control, thus both displacement and the flow of energy act at the mercy of the mechanical interaction of muscle and forces associated with movement. Studies on isolated muscle-tendon units highlight the challenges of controlling such systems. A carefully tuned activation pattern is necessary for effective cycling of energy between tendon and the environment; most activation patterns lead to futile cycling of energy between tendon and muscle. In power-amplified systems, “elastic backfire” sometimes occurs, where energy loaded into tendon acts to lengthen active muscles, rather than accelerate the body. Classic models of proprioception that rely on muscle spindle organs for sensing muscle and joint displacement illustrate how elastic structures might influence sensory feedback by decoupling joint movement from muscle fiber displacements. The significance of the complex flow of energy between muscles, elastic elements and the body for neuromotor control is worth exploring.

 

Existing data suggest the extracellular matrix (ECM) of vertebrate skeletal muscle consists of several morphologically distinct layers: an endomysium, perimysium, and epimysium surrounding muscle fibers, fascicles, and whole muscles, respectively. These ECM layers are hypothesized to serve important functional roles within muscle, influencing passive mechanics, providing avenues for force transmission, and influencing dynamic shape changes during contraction. The morphology of the skeletal muscle ECM is well described in mammals and birds; however, ECM morphology in other vertebrate groups including amphibians, fish, and reptiles remains largely unexamined. It remains unclear whether a multilayered ECM is a common feature of vertebrate skeletal muscle, and whether functional roles attributed to the ECM should be considered in mechanical analyses of non‐mammalian and non‐avian muscle. To explore the prevalence of a multilayered ECM, we used a cell maceration and scanning electron microscopy technique to visualize the organization of ECM collagen in muscle from six vertebrates: bullfrogs (Lithobates catesbeianus), turkeys (Meleagris gallopavo), alligators (Alligator mississippiensis), cane toads (Rhinella marina), laboratory mice (Mus musculus), and carp (Cyprinus carpio). All muscles studied contained a collagen‐reinforced ECM with multiple morphologically distinct layers. An endomysium surrounding muscle fibers was apparent in all samples. A perimysium surrounding groups of muscle fibers was apparent in all but carp epaxial muscle; a muscle anatomically, functionally, and phylogenetically distinct from the others studied. An epimysium was apparent in all samples taken at the muscle periphery. These findings show that a multilayered ECM is a common feature of vertebrate muscle and suggest that a functionally relevant ECM should be considered in mechanical models of vertebrate muscle generally. It remains unclear whether cross‐species variations in ECM architecture are the result of phylogenetic, anatomical, or functional differences, but understanding the influence of such variation on muscle mechanics may prove a fruitful area for future research.

 

Abstract Modular active cell robots (MACROs) are a design paradigm for modular robotic hardware that uses only two components, namely actuators and passive compliant joints. Under the MACRO approach, a large number of actuators and joints are connected to create mesh-like cellular robotic structures that can be actuated to achieve large deformation and shape change. In this two-part paper, we study the importance of different possible mesh topologies within the MACRO framework. Regular and semi-regular tilings of the plane are used as the candidate mesh topologies and simulated using finite element analysis (FEA). In Part 1, we use FEA to evaluate their passive stiffness characteristics. Using a strain-energy method, the homogenized material properties (Young's modulus, shear modulus, and Poisson's ratio) of the different mesh topologies are computed and compared. The results show that the stiffnesses increase with increasing nodal connectivity and that stretching-dominated topologies have higher stiffness compared to bending-dominated ones. We also investigate the role of relative actuator-node stiffness on the overall mesh characteristics. This analysis shows that the stiffness of stretching-dominated topologies scales directly with their cross-section area whereas bending-dominated ones do not have such a direct relationship. 

Abstract Modular active cell robots (MACROs) is a design approach in which a large number of linear actuators and passive compliant joints are assembled to create an active structure with a repeating unit cell. Such a mesh-like robotic structure can be actuated to achieve large deformation and shape-change. In this two-part paper, we use finite element analysis (FEA) to model the deformation behavior of different MACRO mesh topologies and evaluate their passive and active mechanical characteristics. In Part I, we presented the passive stiffness characteristics of different MACRO meshes. In this Part II of the paper, we investigate the active strain characteristics of planar MACRO meshes. Using FEA, we quantify and compare the strains generated for the specific choice of MACRO mesh topology and further for the specific choice of actuators actuated in that particular mesh. We simulate a series of actuation modes that are based on the angular orientation of the actuators within the mesh and show that such actuation modes result in deformation that is independent of the size of the mesh. We also show that there exists a subset of such actuation modes that spans the range of deformation behavior. Finally, we compare the actuation effort required to actuate different MACRO meshes and show that the actuation effort is related to the nodal connectivity of the mesh. 

Human movement is accomplished through muscle contraction, yet there does not exist a portable system capable of monitoring muscle length changes in real time. To address this limitation, we previously introduced magnetomicrometry, a minimally-invasive tracking technique comprising two implanted magnetic beads in muscle and a magnetic field sensor array positioned on the body’s surface adjacent the implanted beads. The implant system comprises a pair of spherical magnetic beads, each with a first coating of nickel-copper-nickel and an outer coating of Parylene C. In parallel work, we demonstrate submillimeter accuracy of magnetic bead tracking for muscle contractions in an untethered freely-roaming avian model. Here, we address the clinical viability of magnetomicrometry. Using a specialized device to insert magnetic beads into muscle in avian and lagomorph models, we collect data to assess gait metrics, bead migration, and bead biocompatibility. For these animal models, we find no gait differences post-versus pre-implantation, and bead migration towards one another within muscle does not occur for initial bead separation distances greater than 3 cm. Further, using extensive biocompatibility testing, the implants are shown to be non-irritant, non-cytotoxic, non-allergenic, and non-irritating. Our cumulative results lend support for the viability of these magnetic bead implants for implantation in human muscle. We thus anticipate their imminent use in human-machine interfaces, such as in control of prostheses and exoskeletons and in closed-loop neuroprosthetics to aid recovery from neurological disorders. 

Muscle tissue drives nearly all movement in the animal kingdom, providing power, mobility, and dexterity. Technologies for measuring muscle tissue motion, such as sonomicrometry, fluoromicrometry, and ultrasound, have significantly advanced our understanding of biomechanics. Yet, the field lacks the ability to monitor muscle tissue motion for animal behavior outside the lab. Towards addressing this issue, we previously introduced magnetomicrometry, a method that uses magnetic beads to wirelessly monitor muscle tissue length changes, and we validated magnetomicrometry via tightly-controlled in situ testing. In this study we validate the accuracy of magnetomicrometry against fluoromicrometry during untethered running in an in vivo turkey model. We demonstrate real-time muscle tissue length tracking of the freely-moving turkeys executing various motor activities, including ramp ascent and descent, vertical ascent and descent, and free roaming movement. Given the demonstrated capacity of magnetomicrometry to track muscle movement in untethered animals, we feel that this technique will enable new scientific explorations and an improved understanding of muscle function. 

During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates.