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Force sensing is advantageous in walking and can signal leg slipping that could destabilize support of body weight, prior to changes in body position. Recordings of strain-detecting campaniform sensilla in blow fly legs showed force encoding in ranges reflecting their minimal body weight but firing was also inhibited by very small transient force decreases. A mathematical model of the receptors reproduced these characteristics and could aid in control of walking machines, independent of size and mass. 

Campaniform sensilla (CS) are mechanosensors embedded in the cuticle of insects. They are often found at locations near the joints of leg segments. On legs, CS are generally considered to respond directionally to cuticle bending during legged locomotion. It is currently unclear how CS locations affect strain levels at the CS, but this information is crucial for understanding how CS respond to stimuli. Here we present a parametric finite element model of the femoral CS field forDrosophilahind legs with 12 general and seven CS-specific parameters each. This model allows testing how changes in CS location, orientation and material property affect strain levels at each CS. We used experimentally acquired kinematic data and computed ground reaction forces to simulatein vivo-like forward stepping. The displacements found in this study at the physiological CS field location near the trochanter–femur joint are smaller than those necessary for conformation changes of ion channels involved in signal elicitation. Also, variation of material properties of the CS had little influence on displacement magnitudes at the CS cap where the sensory neuron attaches. Thus, our results indicate that ground reaction forces alone are unlikely to serve CS field activation during forward walking. 

Abstract During walking, sensory information is measured and monitored by sensory organs that can be found on and within various limb segments. Strain can be monitored by insect load sensors, campaniform sensilla (CS), which have components embedded within the exoskeleton. CS vary in eccentricity, size, and orientation, which can affect their sensitivity to specific strains. Directly investigating the mechanical interfaces that these sensors utilize to encode changes in load bears various obstacles, such as modeling of viscoelastic properties. To circumvent the difficulties of modeling and performing biological experiments in small insects, we developed 3-dimensional printed resin models based on high-resolution imaging of CS. Through the utilization of strain gauges and a motorized tensile tester, physiologically plausible strain can be mimicked while investigating the compression and tension forces that CS experience; here, this was performed for a field of femoral CS inDrosophila melanogaster. Different loading scenarios differentially affected CS compression and the likely neuronal activity of these sensors and elucidate population coding of stresses acting on the cuticle. 

Discharges of sensory receptors (campaniform sensilla) in the hind legs of stick insects can differentially signal forces that occur in walking uphill versus walking downhill. Unexpectedly, sensory firing most closely reflects the rate of change of force (dF/d t) even when the force levels are high. These signals have been replicated in a mathematical model of the receptors and could be used to stabilize leg movements both in the animal and in a walking robot. 

A challenge in robotics is to control interactions with the environment by modulating the stiffness of a manipulator’s joints. Smart servos are controlled with proportional feedback gain that is analogous to torsional stiffness of an animal’s joint. In animals, antagonistic muscle groups can be temporarily coactivated to stiffen the joint to provide greater opposition to external forces. However, the joint properties for which coactivation increases the stiffness of the joint remain unknown. In this study, we explore possible mechanisms by building a mathematical model of the stick insect tibia actuated by two muscles, the extensor and flexor tibiae. Muscle geometry, passive properties, and active properties are extracted from the literature. Joint stiffness is calculated by tonically activating the antagonists, perturbing the joint from its equilibrium angle, and calculating the restoring moment generated by the muscles. No reflexes are modeled. We estimate how joint stiffness depends on parallel elastic element stiffness, the shape of the muscle activation curve, and properties of the force-length curve. We find that co-contracting antagonist muscles only stiffens the joint when the peak of the force-length curve occurs at a muscle length longer than that when the joint is at equilibrium and the muscle force versus activation curve is concave-up. We discuss how this information could be applied to the control of a smart servo actuator in a robot leg. 

A challenge in robotics is to control interactions with the environment by modulating the stiffness of a manipulator’s joints. Smart servos are controlled with proportional feedback gain that is analogous to torsional stiffness of an animal’s joint. In animals, antagonistic muscle groups can be temporarily coactivated to stiffen the joint to provide greater opposition to external forces. However, the joint properties for which coactivation increases the stiffness of the joint remain unknown. In this study, we explore possible mechanisms by building a mathematical model of the stick insect tibia actuated by two muscles, the extensor and flexor tibiae. Muscle geometry, passive properties, and active properties are extracted from the literature. Joint stiffness is calculated by tonically activating the antagonists, perturbing the joint from its equilibrium angle, and calculating the restoring moment generated by the muscles. No reflexes are modeled. We estimate how joint stiffness depends on parallel elastic element stiffness, the shape of the muscle activation curve, and properties of the force-length curve. We find that co-contracting antagonist muscles only stiffens the joint when the peak of the force-length curve occurs at a muscle length longer than that when the joint is at equilibrium and the muscle force versus activation curve is concave-up. We discuss how this information could be applied to the control of a smart servo actuator in a robot leg.