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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. 

Abstract This article is a historical perspective on how the study of the neuromechanics of insects and other arthropods has inspired the construction, and especially the control, of hexapod robots. Many hexapod robots’ control systems share common features, including: 1. Direction of motor output of each joint (i.e. to flex or extend) in the leg is gated by an oscillatory or bistable gating mechanism; 2. The relative phasing between each joint is influenced by proprioceptive feedback from the periphery (e.g. joint angles, leg load) or central connections between joint controllers; and 3. Behavior can be directed (e.g. transition from walking along a straight path to walking along a curve) via low-dimensional, broadly-acting descending inputs to the network. These distributed control schemes are inspired by, and in some robots, closely mimic the organization of the nervous systems of insects, the natural hexapods, as well as crustaceans. Nearly a century of research has revealed organizational principles such as central pattern generators, the role of proprioceptive feedback in control, and command neurons. These concepts have inspired the control systems of hexapod robots in the past, in which these structures were applied to robot controllers with neuromorphic (i.e. distributed) organization, but not neuromorphic computational units (i.e. neurons) or computational hardware (i.e. hardware-accelerated neurons). Presently, several hexapod robots are controlled with neuromorphic computational units with or without neuromorphic organization, almost always without neuromorphic hardware. In the near future, we expect to see hexapod robots whose controllers include neuromorphic organization, computational units, and hardware. Such robots may exhibit the full mobility of their insect counterparts thanks to a ‘biology-first’ approach to controller design. This perspective article is not a comprehensive review of the neuroscientific literature but is meant to give those with engineering backgrounds a gentle introduction into the neuroscientific principles that underlie models and inspire neuromorphic robot controllers. A historical summary of hexapod robots whose control systems and behaviors use neuromorphic elements is provided. Robots whose controllers closely model animals and may be used to generate concrete hypotheses for future animal experiments are of particular interest to the authors. The authors hope that by highlighting the decades of experimental research that has led to today’s accepted organization principles of arthropod nervous systems, engineers may better understand these systems and more fully apply biological details in their robots. To assist the interested reader, deeper reviews of particular topics from biology are suggested throughout. 

Abstract Insects are highly capable walkers, but many questions remain regarding how the insect nervous system controls locomotion. One particular question is how information is communicated between the ‘lower level’ ventral nerve cord (VNC) and the ‘higher level’ head ganglia to facilitate control. In this work, we seek to explore this question by investigating how systems traditionally described as ‘positive feedback’ may initiate and maintain stepping in the VNC with limited information exchanged between lower and higher level centers. We focus on the ‘reflex reversal’ of the stick insect femur-tibia joint between a resistance reflex (RR) and an active reaction in response to joint flexion, as well as the activation of populations of descending dorsal median unpaired (desDUM) neurons from limb strain as our primary reflex loops. We present the development of a neuromechanical model of the stick insect ( Carausius morosus ) femur-tibia (FTi) and coxa-trochanter joint control networks ‘in-the-loop’ with a physical robotic limb. The control network generates motor commands for the robotic limb, whose motion and forces generate sensory feedback for the network. We based our network architecture on the anatomy of the non-spiking interneuron joint control network that controls the FTi joint, extrapolated network connectivity based on known muscle responses, and previously developed mechanisms to produce ‘sideways stepping’. Previous studies hypothesized that RR is enacted by selective inhibition of sensory afferents from the femoral chordotonal organ, but no study has tested this hypothesis with a model of an intact limb. We found that inhibiting the network’s flexion position and velocity afferents generated a reflex reversal in the robot limb’s FTi joint. We also explored the intact network’s ability to sustain steady locomotion on our test limb. Our results suggested that the reflex reversal and limb strain reinforcement mechanisms are both necessary but individually insufficient to produce and maintain rhythmic stepping in the limb, which can be initiated or halted by brief, transient descending signals. Removing portions of this feedback loop or creating a large enough disruption can halt stepping independent of the higher-level centers. We conclude by discussing why the nervous system might control motor output in this manner, as well as how to apply these findings to generalized nervous system understanding and improved robotic control. 

We introduce Drosophibot, a hexapod robot with legs designed based on the Common fruit fly, Drosophila melanogaster, built as a test platform for neural control development. The robot models anatomical aspects not present in other, similar bio-robots such as a retractable abdominal segment, insect-like dynamic scaling, and compliant feet segments in the hopes that more similar biomechanics will lead to more similar neural control and resulting behaviors. In increasing biomechanical modeling accuracy, we aim to gain further insight into the insect’s nervous system to inform the current model and subsequent neural controllers for legged robots. 

This work shows one way to tune a servomotor controller to make it perform in a similar way to a biomechanical model of an insect leg joint. Three key metrics were considered: the equilibrium angle of the joint as a function of antagonistic inputs; the dynamics of the free response when perturbed; and the dynamics of active motions. We model an insect leg joint as a hinge actuated by a pair of antagonistic linear Hill muscles that drive a rigid distal leg segment. Passive forces from the exoskeleton are also modeled as passive viscoelastic elements (PVE). We approximate parameter values for the model based on the biomechanics literature, and then dynamically scale them to the scale of our robot, Drosophibot. We show how to tune the servo’s control mapping and feedback gain to mimic the dynamically scaled model of the animal joint.