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Sensory feedback from sense organs during animal locomotion can be heavily influenced by an organism’s mechanical structure. In insects, the interplay between sensing and mechanics can be demonstrated in the campaniform sensilla (CS) strain sensors located across the exoskeleton. Leg CS are highly sensitive to the loading state of the limb. In walking, loading is primarily influenced by ground reaction forces (GRF) mediated by the foot, or tarsus. The complex morphology of the tarsus provides compliance, passive and active substrate grip, and an increased moment arm for the GRF, all of which impact leg loading and the resulting CS discharge. To increase the biomimicry of robots we use to study strain feedback during insect walking, we have developed a series of tarsi for our robotic model of a Carausius morosus middle leg. We seek the simplest design that mimics tarsus functionality. Tarsi were designed with varying degrees of compliance, passive grip, and biomimetic structure. We created elastic silicone tarsal joints for several of these models and found that they produced linear stiffness within joint limits across different joint morphologies. Strain gauges positioned in CS locations on the trochanterofemur and tibia recorded strain while the leg stepped on a treadmill. Most, but not all, designs increased axial strain magnitude compared to previous data with no tarsus. Every tarsus design produced positive transversal strain in the tibia, indicating axial torsion in addition to bending. Sudden increases in tibial strain reflected leg slipping during stance. This data show how different aspects of the tarsus may mediate leg loading, allowing us to improve the mechanical biomimicry of future robotic test platforms. 

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. 

This study introduces a novel neuromechanical model of rat hindlimbs with biarticular muscles producing walking movements without ground contact. The design of the control network is informed by the findings from our previous investigations into two-layer central pattern generators (CPGs). Specifically, we examined one plausible synthetic nervous system (SNS) designed to actuate 3 biarticular muscles, including the Biceps femoris posterior (BFP) and Rectus femoris (RF), both of which provide torque about the hip and knee joints. We conducted multiple perturbation tests on the simulation model to investigate the contribution of these two biarticular muscles in stabilizing perturbed hindlimb walking movements. We tested the BFP and RF muscles under three conditions: active, only passive tension, and fully disabled. Our results show that when these two biarticular muscles were active, they not only reduced the impact of external torques, but also facilitated rapid coordination of motion phases. As a result, the hindlimb model with biarticular muscles demonstrated faster recovery compared to our previous monoarticular muscle model. 

Insects use various sensory organs to monitor proprioceptive and exteroceptive information during walking. The measurement of forces in the exoskeleton is facilitated by campaniform sensilla (CS), which monitor resisted muscle forces through the detection of exoskeletal strains. CS are commonly found in leg segments arranged in fields, groups, or as single units. Most insects have the highest density of sensor locations on the trochanter, a proximal leg segment. CS are arranged homologously across species, suggesting comparable functions despite noted morphological differences. Furthermore, the trochanter–femur joint is mobile in some species and fused in others. To investigate how different morphological arrangements influence strain sensing in different insect species, we utilized two robotic models of the legs of the fruit fly Drosophila melanogaster and the stick insect Carausius morosus. Both insect species are past and present model organisms for unraveling aspects of motor control, thus providing extensive information on sensor morphology and, in-part, function. The robotic models were dynamically scaled to the legs of the insects, with strain gauges placed with correct orientations according to published data. Strains were detected during stepping on a treadmill, and the sensor locations and leg morphology played noticeable roles in the strains that were measured. Moreover, the sensor locations that were absent in one species relative to the other measured strains that were also being measured by the existing sensors. These findings contributed to our understanding of load sensing in animal locomotion and the relevance of sensory organ morphology in motor control. 

Central pattern generators (CPGs) are ubiquitous neural circuits that contribute to an eclectic collection of rhythmic behaviors across an equally diverse assortment of animal species. Due to their prominent role in many neuromechanical phenomena, numerous bioinspired robots have been designed to both investigate and exploit the operation of these neural oscillators. In order to serve as effective tools for these robotics applications, however, it is often necessary to be able to adjust the phase alignment of multiple CPGs during operation. To achieve this goal, we present the design of our phase difference control (PDC) network using a functional subnetwork approach (FSA) wherein subnetworks that perform basic mathematical operations are assembled such that they serve to control the relative phase lead/lag of target CPGs. Our PDC network operates by first estimating the phase difference between two CPGs, then comparing this phase difference to a reference signal that encodes the desired phase difference, and finally eliminating any error by emulating a proportional controller that adjusts the CPG oscillation frequencies. The architecture of our PDC network, as well as its various parameters, are all determined via analytical design rules that allow for direct interpretability of the network behavior. Simulation results for both the complete PDC network and a selection of its various functional subnetworks are provided to demonstrate the efficacy of our methodology.