Controlling legs for locomotion-insights from robotics and neurobiology

On the analysis and control of a bipedal legged locomotion model via partial feedback linearization

In this study, we introduce a new model for bipedal locomotion that enhances the classical spring-loaded inverted pendulum (SLIP) model. Our proposed model incorporates a damping term in the leg spring, a linear actuator serially interconnected to the leg, and a rotary actuator affixed to the hip. The distinct feature of this new model is its ability to overcome the non-integrability challenge inherent in the conventional SLIP models through the application of partial feedback linearization. By leveraging these actuators, our model enhances the stability and robustness of the locomotion mechanism, particularly when navigating across varied terrain profiles. To validate the effectiveness and practicality of this model, we conducted detailed simulation studies, benchmarking its performance against other recent models outlined in the literature. Our findings suggest that the redundancy in actuation introduced by our model significantly facilitates both open-loop and closed-loop walking gait, showcasing promising potential for the future of bipedal locomotion, especially for bio-inspired robotics applications in outdoor and rough terrains.

Mechanical modeling of mechanosensitive insect strain sensors as a tool to investigate exoskeletal interfaces

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.

The processing of proprioceptive signals in distributed networks: insights from insect motor control

The integration of sensory information is required to maintain body posture and to generate robust yet flexible locomotion through unpredictable environments. To anticipate required adaptations in limb posture and enable compensation of sudden perturbations, an animal's nervous system assembles external (exteroception) and internal (proprioception) cues. Coherent neuronal representations of the proprioceptive context of the body and the appendages arise from the concerted action of multiple sense organs monitoring body kinetics and kinematics. This multimodal proprioceptive information, together with exteroceptive signals and brain-derived descending motor commands, converges onto premotor networks - i.e. the local neuronal circuitry controlling motor output and movements - within the ventral nerve cord (VNC), the insect equivalent of the vertebrate spinal cord. This Review summarizes existing knowledge and recent advances in understanding how local premotor networks in the VNC use convergent information to generate contextually appropriate activity, focusing on the example of posture control. We compare the role and advantages of distributed sensory processing over dedicated neuronal pathways, and the challenges of multimodal integration in distributed networks. We discuss how the gain of distributed networks may be tuned to enable the behavioral repertoire of these systems, and argue that insect premotor networks might compensate for their limited neuronal population size by, in comparison to vertebrate networks, relying more heavily on the specificity of their connections. At a time in which connectomics and physiological recording techniques enable anatomical and functional circuit dissection at an unprecedented resolution, insect motor systems offer unique opportunities to identify the mechanisms underlying multimodal integration for flexible motor control.

Mechanosensory Control of Locomotion in Animals and Robots: Moving Forward

While animals swim, crawl, walk, and fly with apparent ease, building robots capable of robust locomotion remains a significant challenge. In this review, we draw attention to mechanosensation-the sensing of mechanical forces generated within and outside the body-as a key sense that enables robust locomotion in animals. We discuss differences between mechanosensation in animals and current robots with respect to (1) the encoding properties and distribution of mechanosensors and (2) the integration and regulation of mechanosensory feedback. We argue that robotics would benefit greatly from a detailed understanding of these aspects in animals. To that end, we highlight promising experimental and engineering approaches to study mechanosensation, emphasizing the mutual benefits for biologists and engineers that emerge from moving forward together.

Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: a review of experimental, simulation and robotic studies

Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment. Decoding the underlying mechanisms requires an integrative approach. Comparative experimental biology has allowed researchers to study the underlying components and some of their interactions across diverse animals. These studies have shown that locomotor neural circuits are distributed in the spinal cord, the midbrain and higher brain regions in vertebrates. The spinal cord plays a key role in locomotor control because it contains central pattern generators (CPGs) - systems of coupled neuronal oscillators that provide coordinated rhythmic control of muscle activation that can be viewed as feedforward controllers - and multiple reflex loops that provide feedback mechanisms. These circuits are activated and modulated by descending pathways from the brain. The relative contributions of CPGs, feedback loops and descending modulation, and how these vary between species and locomotor conditions, remain poorly understood. Robots and neuromechanical simulations can complement experimental approaches by testing specific hypotheses and performing what-if scenarios. This Review will give an overview of key knowledge gained from comparative vertebrate experiments, and insights obtained from neuromechanical simulations and robotic approaches. We suggest that the roles of CPGs, feedback loops and descending modulation vary among animals depending on body size, intrinsic mechanical stability, time required to reach locomotor maturity and speed effects. We also hypothesize that distal joints rely more on feedback control compared with proximal joints. Finally, we highlight important opportunities to address fundamental biological questions through continued collaboration between experimentalists and engineers.

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.

Adaptive load feedback robustly signals force dynamics in robotic model of Carausius morosus stepping

Animals utilize a number of neuronal systems to produce locomotion. One type of sensory organ that contributes in insects is the campaniform sensillum (CS) that measures the load on their legs. Groups of the receptors are found on high stress regions of the leg exoskeleton and they have significant effects in adapting walking behavior. Recording from these sensors in freely moving animals is limited by technical constraints. To better understand the load feedback signaled by CS to the nervous system, we have constructed a dynamically scaled robotic model of the Carausius morosus stick insect middle leg. The leg steps on a treadmill and supports weight during stance to simulate body weight. Strain gauges were mounted in the same positions and orientations as four key CS groups (Groups 3, 4, 6B, and 6A). Continuous data from the strain gauges were processed through a previously published dynamic computational model of CS discharge. Our experiments suggest that under different stepping conditions (e.g., changing "body" weight, phasic load stimuli, slipping foot), the CS sensory discharge robustly signals increases in force, such as at the beginning of stance, and decreases in force, such as at the end of stance or when the foot slips. Such signals would be crucial for an insect or robot to maintain intra- and inter-leg coordination while walking over extreme terrain.

Skeletal muscle function underpins muscle spindle abundance

Muscle spindle abundance is highly variable within and across species, but we currently lack any clear picture of the mechanistic causes or consequences of this variation. Previous use of spindle abundance as a correlate for muscle function implies a mechanical underpinning to this variation, but these ideas have not been tested. Herein, we use integrated medical imaging and subject-specific musculoskeletal models to investigate the relationship between spindle abundance, muscle architecture and in vivo muscle behaviour in the human locomotor system. These analyses indicate that muscle spindle number is tightly correlated with muscle fascicle length, absolute fascicle length change, velocity of fibre lengthening and active muscle forces during walking. Novel correlations between functional indices and spindle abundance are also recovered, where muscles with a high abundance predominantly function as springs, compared to those with a lower abundance mostly functioning as brakes during walking. These data demonstrate that muscle fibre length, lengthening velocity and fibre force are key physiological signals to the central nervous system and its modulation of locomotion, and that muscle spindle abundance may be tightly correlated to how a muscle generates work. These insights may be combined with neuromechanics and robotic studies of motor control to help further tease apart the functional drivers of muscle spindle composition.

Resonance as a Design Strategy for AI and Social Robots

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human-robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of "sympathetic resonance" as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human-robot interactions.

Adaptive Centipede Walking via Synergetic Coupling Between Decentralized Control and Flexible Body Dynamics

Multi-legged animals such as myriapods can locomote on unstructured rough terrain using their flexible bodies and legs. This highly adaptive locomotion emerges through the dynamic interactions between an animal’s nervous system, its flexible body, and the environment. Previous studies have primarily focused on either adaptive leg control or the passive compliance of the body parts and have shown how each enhanced adaptability to complex terrains in multi-legged locomotion. However, the essential mechanism considering both the adaptive locomotor circuits and bodily flexibility remains unclear. In this study, we focused on centipedes and aimed to understand the well-balanced coupling between the two abovementioned mechanisms for rough terrain walking by building a neuromechanical model based on behavioral findings. In the behavioral experiment, we observed a centipede walking when part of the terrain was temporarily removed and thereafter restored. We found that the ground contact sense of each leg was essential for generating rhythmic leg motions and also for establishing adaptive footfall patterns between adjacent legs. Based on this finding, we proposed decentralized control mechanisms using ground contact sense and implemented them into a physical centipede model with flexible bodies and legs. In the simulations, our model self-organized the typical gait on flat terrain and adaptive walking during gap crossing, which were similar to centipedes. Furthermore, we demonstrated that the locomotor performance deteriorated on rough terrain when adaptive leg control was removed or when the body was rigid, which indicates that both the adaptive leg control and the flexible body are essential for adaptive locomotion. Thus, our model is expected to capture the possible essential mechanisms underlying adaptive centipede walking and pave the way for designing multi-legged robots with high adaptability to irregular terrain.

Drosophila: A Model to Study the Pathogenesis of Parkinson's Disease

Human Central Nervous System (CNS) is the complex part of the human body, which regulates multiple cellular and molecular events taking place simultaneously. Parkinsons Disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease (AD). The pathological hallmarks of PD are loss of dopaminergic neurons in the substantianigra (SN) pars compacta (SNpc) and accumulation of misfolded α-synuclein, in intra-cytoplasmic inclusions called Lewy bodies (LBs). So far, there is no cure for PD, due to the complexities of molecular mechanisms and events taking place during the pathogenesis of PD. Drosophila melanogaster is an appropriate model organism to unravel the pathogenicity not only behind PD but also other NDs. In this context as numerous biological functions are preserved between Drosophila and humans. Apart from sharing 75% of human disease-causing genes homolog in Drosophila, behavioral responses like memory-based tests, negative geotaxis, courtship and mating are also well studied. The genetic, as well as environmental factors, can be studied in Drosophila to understand the geneenvironment interactions behind the disease condition. Through genetic manipulation, mutant flies can be generated harboring human orthologs, which can prove to be an excellent model to understand the effect of the mutant protein on the pathogenicity of NDs.

Intersegmental Interactions Give Rise to a Global Network

Animal motor behaviors require the coordination of different body segments. Thus the activity of the networks that control each segment, which are distributed along the nerve cord, should be adequately matched in time. This temporal organization may depend on signals originated in the brain, the periphery or other segments. Here we evaluate the role of intersegmental interactions. Because of the relatively regular anatomy of leeches, the study of intersegmental coordination in these animals restricts the analysis to interactions among iterated units. We focused on crawling, a rhythmic locomotive behavior through which leeches move on solid ground. The motor pattern was studied ex vivo, in isolated ganglia and chains of three ganglia, and in vivo. Fictive crawling ex vivo (crawling) displayed rhythmic characteristics similar to those observed in vivo. Within the three-ganglion chains the motor output presented an anterior-posterior order, revealing the existence of a coordination mechanism that occurred in the absence of brain or peripheral signals. An experimental perturbation that reversibly abolished the motor pattern in isolated ganglia produced only a marginal effect on the motor activity recorded in three-ganglion chains. Therefore, the segmental central pattern generators present in each ganglion of the chain lost the autonomy observed in isolated ganglia, and constituted a global network that reduced the degrees of freedom of the system. However, the intersegmental phase lag in the three-ganglion chains was markedly longer than in vivo. This work suggests that intersegmental interactions operate as a backbone of correlated motor activity, but additional signals are required to enhance and speed coordination in the animal.

Insect-Inspired Robots: Bridging Biological and Artificial Systems

This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.

The Role of Muscle Spindle Feedback in the Guidance of Hindlimb Movement by the Ipsilateral Forelimb during Locomotion in Mice

Safe and efficient locomotion relies on placing the foot on a reliable surface at the end of each leg swing movement. Visual information has been shown to be important for determining the location of foot placement in humans during walking when precision is required. Yet in quadrupedal animals where the hindlimbs are outside of the visual field, such as in mice, the mechanisms by which precise foot placement is achieved remain unclear. Here we show that the placement of the hindlimb paw is determined by the position of the forelimb paw during normal locomotion and in the presence of perturbations. When a perturbation elicits a stumbling corrective reaction, we found that the forelimb paw shifts posteriorly relative to body at the end of stance, and this spatial shift is echoed in hindlimb paw placement at the end of the swing movement. Using a mutant mouse line in which muscle spindle feedback is selectively removed, we show that this posterior shift of paw placement is dependent on muscle spindle feedback in the hindlimb but not in the forelimb. These findings uncover a neuronal mechanism that is independent of vision to ensure safe locomotion during perturbation. This mechanism adds to our general knowledge of how the nervous system controls targeted limb movements and could inform the development of autonomous walking machines.

Investigating the role of low level reinforcement reflex loops in insect locomotion

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.

A computational model of insect campaniform sensilla predicts encoding of forces during walking

Control of forces is essential in both animals and walking machines. Insects measure forces as strains in their exoskeletons via campaniform sensilla (CS). Deformations of cuticular caps embedded in the exoskeleton excite afferents that project to the central nervous system. CS afferent firing frequency (i.e. 'discharge') is highly dynamic, correlating with the rate of change of the force. Discharges adapt over time to tonic forces and exhibit hysteresis during cyclic loading.In this study we characterized a phenomenological model that predicts CS discharge, in which discharge is proportional to the instantaneous stimulus force relative to an adaptive variable. In contrast to previous studies of sensory adaptation, our model (1) is nonlinear and (2) reproduces the characteristic power-law adaptation with first order dynamics only (i.e. no 'fractional derivatives' are required to explain dynamics). We solve the response of the system analytically in multiple cases and use these solutions to derive the dynamics of the adaptive variable. We show that the model can reproduce responses of insect CS to many different force stimuli after being tuned to reproduce only one response, suggesting that the model captures the underlying dynamics of the system. We show that adaptation to tonic forces, rate-sensitivity, and hysteresis are different manifestations of the same underlying mechanism: the adaptive variable. We tune the model to replicate the dynamics of three different CS groups from two insects (cockroach and stick insect), demonstrating that it is generalizable. We also invert the model to estimate the stimulus force given the discharge recording from the animal. We discuss the adaptive neural and mechanical processes that the model may mimic and the model's use for understanding the role of load feedback in insect motor control. A preliminary model and results were previously published in the proceedings of the Conference on Biohybrid and Biomimetic Systems.

Electronics-free pneumatic circuits for controlling soft-legged robots

Pneumatically actuated soft robots have recently shown promise for their ability to adapt to their environment. Previously, these robots have been controlled with electromechanical components, such as valves and pumps, that are typically bulky and expensive. Here, we present an approach for controlling the gaits of soft-legged robots using simple pneumatic circuits without any electronic components. This approach produces locomotive gaits using ring oscillators composed of soft valves that generate oscillating signals analogous to biological central pattern generator neural circuits, which are acted upon by pneumatic logic components in response to sensor inputs. Our robot requires only a constant source of pressurized air to power both control and actuation systems. We demonstrate this approach by designing pneumatic control circuits to generate walking gaits for a soft-legged quadruped with three degrees of freedom per leg and to switch between gaits to control the direction of locomotion. In experiments, we controlled a basic walking gait using only three pneumatic memory elements (valves). With two oscillator circuits (seven valves), we were able to improve locomotion speed by 270%. Furthermore, with a pneumatic memory element we designed to mimic a double-pole double-throw switch, we demonstrated a control circuit that allowed the robot to select between gaits for omnidirectional locomotion and to respond to sensor input. This work represents a step toward fully autonomous, electronics-free walking robots for applications including low-cost robotics for entertainment and systems for operation in environments where electronics may not be suitable.

Distributed control of motor circuits for backward walking in Drosophila

How do descending inputs from the brain control leg motor circuits to change how an animal walks? Conceptually, descending neurons are thought to function either as command-type neurons, in which a single type of descending neuron exerts a high-level control to elicit a coordinated change in motor output, or through a population coding mechanism, whereby a group of neurons, each with local effects, act in combination to elicit a global motor response. The Drosophila Moonwalker Descending Neurons (MDNs), which alter leg motor circuit dynamics so that the fly walks backwards, exemplify the command-type mechanism. Here, we identify several dozen MDN target neurons within the leg motor circuits, and show that two of them mediate distinct and highly-specific changes in leg muscle activity during backward walking: LBL40 neurons provide the hindleg power stroke during stance phase; LUL130 neurons lift the legs at the end of stance to initiate swing. Through these two effector neurons, MDN directly controls both the stance and swing phases of the backward stepping cycle. These findings suggest that command-type descending neurons can also operate through the distributed control of local motor circuits.

Smooth and slipless walking mechanism inspired by the open-close cycle of a beetle claw

This study investigated the function of the beetle's claw for its smooth and slipless walking and designed an artificial claw open-close cycle mechanism to mimic the beetle's walking. First, the effects of claw opening and closing on beetles' ability to attach to surfaces were examined. A beetle does not have an attachment pad, and only its claws work to grip the ground; its claw opens and closes and attaches with two sharp hooks. With their claws, beetles can smoothly walk, neither slipping on nor having their claws stuck in the surface. How do they perform smooth walking with sharp claws? In this study, we observed that beetles close their claws when they raise and swung their legs forward, while they open their claws when they lowered their legs to the ground. We then conducted non-destructive tests: their claws were forced open or closed. There was a significant difference in the trajectories of forced-closed claws compared to intact claws and forced-open claws. When their claws were forced-closed, this caused slippage in walking. On the other hand, when a claw was forced-open and its rotation was also inhibited, the claw stuck heavily in the surface, and the beetle could not walk. Based on these findings, we designed an artificial claw to open and close in the same cyclic manner as in the case of natural beetles. The performance of the artificial claw was consistent with the conclusions drawn from natural beetles: the locomotive robot with the artificial claw smoothly moved without slippage. Through these observations, non-destructive tests and performance of the bio-inspired artificial claws, this study confirmed the function of the open-close cycle of beetle claws and demonstrated and successfully adopted it for a locomotive robot.

Neurodynamic modeling of the fruit fly Drosophila melanogaster

This manuscript describes neuromechanical modeling of the fruit fly Drosophila melanogaster in the form of a hexapod robot, Drosophibot, and an accompanying dynamic simulation. Drosophibot is a testbed for real-time dynamical neural controllers modeled after the anatomy and function of the insect nervous system. As such, Drosophibot has been designed to capture features of the animal's biomechanics in order to better test the neural controllers. These features include: dynamically scaling the robot to match the fruit fly by designing its joint elasticity and movement speed; a biomimetic actuator control scheme that converts neural activity into motion in the same way as observed in insects; biomimetic sensing, including proprioception from all leg joints and strain sensing from all leg segments; and passively compliant tarsi that mimic the animal's passive compliance to the walking substrate. We incorporated these features into a dynamical simulation of Drosophibot, and demonstrate that its actuators and sensors perform in an animal-like way. We used this simulation to test a neural walking controller based on anatomical and behavioral data from insects. Finally, we describe Drosophibot's hardware and show that the animal-like features of the simulation transfer to the physical robot.

Policy gradient optimization of controllers for natural dynamic mono-pedal gait

We have previously suggested a biologically-inspired natural dynamic controller for biped locomotion, which applies torque pulses to the different joints at particular phases of an internal phase variable. The parameters of the controller, including the timing and magnitude of the torque pulses and the dynamics of the phase variable, can be kept constant in open loop or adapted to the environment in closed loop. Here we demonstrate the implementation of this approach to a mono-ped robot and the optimization of the controller parameters to enhance robustness via policy gradient. Policy gradient was applied in simulations rather than the actual robot due to safety and hardware considerations. A grounded action transformation (GAT) was learned and used to facilitate the transfer of the learned policy from simulation to hardware. We demonstrate how GAT improves the match between simulations and experiments and how learning enhances the performance and robustness of the mono-ped robot.

A neuromechanical model exploring the role of the common inhibitor motor neuron in insect locomotion

In this work, we analyze a simplified, dynamical, closed-loop, neuromechanical simulation of insect joint control. We are specifically interested in two elements: (1) how slow muscle fibers may serve as temporal integrators of sensory feedback and (2) the role of common inhibitory (CI) motor neurons in resetting this integration when the commanded position changes, particularly during steady-state walking. Despite the simplicity of the model, we show that slow muscle fibers increase the accuracy of limb positioning, even for motions much shorter than the relaxation time of the fiber; this increase in accuracy is due to the slow dynamics of the fibers; the CI motor neuron plays a critical role in accelerating muscle relaxation when the limb moves to a new position; as in the animal, this architecture enables the control of the stance phase speed, independent of swing phase amplitude or duration, by changing the gain of sensory feedback to the stance phase muscles. We discuss how this relates to other models, and how it could be applied to robotic control.

TwoLumps Ascending Neurons Mediate Touch-Evoked Reversal of Walking Direction in Drosophila

External cues, including touch, enable walking animals to flexibly maneuver around obstacles and extricate themselves from dead-ends (for reviews, see [1-3]). In a screen for neurons that enable Drosophila melanogaster to retreat when it encounters a dead-end, we identified a pair of ascending neurons, the TwoLumps Ascending (TLA) neurons. Silencing TLA activity impairs backward locomotion, whereas optogenetic activation triggers backward walking. TLA-induced reversal is mediated in part by the Moonwalker Descending Neurons (MDNs) [4], which receive excitatory input from the TLAs. Silencing the TLAs decreases the extent to which freely walking flies back up upon encountering a physical barrier in the dark, and TLAs show calcium responses to optogenetic activation of neurons expressing the mechanosensory channel NOMPC. We infer that TLAs convey feedforward mechanosensory stimuli to transiently activate MDNs in response to anterior body touch.

Body side-specific changes in sensorimotor processing of movement feedback in a walking insect

Feedback from load and movement sensors can modify timing and magnitude of the motor output in the stepping stick insect. One source of feedback is stretch reception by the femoral chordotonal organ (fCO), which encodes such parameters as the femorotibial (FTi) joint angle, the angular velocity, and its acceleration. Stimulation of the fCO causes a postural resistance reflex, during quiescence, and can elicit the opposite, so-called active reaction (AR), which assists ongoing flexion during active movements. In the present study, we investigated the role of fCO feedback for the difference in likelihood of generating ARs on the inside vs. the outside during curve stepping. We analyzed the effects of fCO stimulation on the motor output to the FTi and the neighboring coxa-trochanter and thorax-coxa joints of the middle leg. In inside and outside turns, the probability for ARs increases with increasing starting angle and decreasing stimulus velocity; furthermore, it is independent of the total angular excursion. However, the transition between stance and swing motor activity always occurs after a specific angular excursion, independent of the turning direction. Feedback from the fCO also has an excitatory influence on levator trochanteris motoneurons (MNs) during inside and outside turns, whereas the same feedback affects protractor coxae MNs only during outside steps. Our results suggest joint- and body side-dependent processing of fCO feedback. A shift in gain may be responsible for different AR probabilities between inside and outside turning, whereas the general control mechanism for ARs is unchanged.NEW & NOTEWORTHY We show that parameters of movement feedback from the tibia in an insect during curve walking are processed in a body side-specific manner, and how. From our results it is highly conceivable that the difference in motor response to the feedback supports the body side-specific leg kinematics during turning. Future studies will need to determine the source for the inputs that determine the local changes in sensory-motor processing.

Neuromechanical Model of Rat Hindlimb Walking with Two-Layer CPGs

This work demonstrates a neuromechanical model of rat hindlimb locomotion undergoing nominal walking with perturbations. In the animal, two types of responses to perturbations are observed: resetting and non-resetting deletions. This suggests that the animal locomotor system contains a memory-like organization. To model this phenomenon, we built a synthetic nervous system that uses separate rhythm generator and pattern formation layers to activate antagonistic muscle pairs about each joint in the sagittal plane. Our model replicates the resetting and non-resetting deletions observed in the animal. In addition, in the intact (i.e., fully afferented) rat walking simulation, we observe slower recovery after perturbation, which is different from the deafferented animal experiment. These results demonstrate that our model is a biologically feasible description of some of the neural circuits in the mammalian spinal cord that control locomotion, and the difference between our simulation and fictive motion shows the importance of sensory feedback on motor output. This model also demonstrates how the pattern formation network can activate muscle synergies in a coordinated way to produce stable walking, which motivates the use of more complex synergies activating more muscles in the legs for three-dimensional limb motion.

Swing Velocity Profiles of Small Limbs Can Arise from Transient Passive Torques of the Antagonist Muscle Alone

In large limbs, changing motor neuron activity typically controls within-movement velocity. For example, sequential agonist-antagonist-agonist motor neuron firing typically underlies the slowing often present at the end of human reaches. In physiological movements of large limbs, antagonistic muscle passive torque is generally negligible. In small limbs, alternatively, passive torques can determine limb rest position, generate restoring movements to it, and decrease agonist-generated movement amplitude and velocity maxima. These observations suggest that, in small limbs, passive forces might also control velocity changes within movements. We investigated this issue in stick insect middle leg femur-tibia (FT) joint. During swing, the FT joint extensor muscle actively shortens and the flexor muscle passively lengthens. As in human reaching, after its initial acceleration, FT joint velocity continuously decreases. We measured flexor passive forces during imposed stretches spanning the ranges of FT joint angles, angular velocities, and movement amplitudes present in leg swings. The viscoelastic "transient" passive force that occurs during and soon after stretch depended on all three variables and could be tens of times larger than the "steady-state" passive force commonly measured long after stretch end. We combined these data, the flexor and extensor moment arms, and an existing extensor model to simulate FT joint swing. To measure only passive (flexor) muscle-dependent effects, we used constant extensor activations in these simulations. In simulations using data from ten flexor muscles, flexor passive torque could always produce swings with, after swing initiation, continuously decreasing velocities. Antagonist muscle passive torques alone can thus control within-movement velocity.

Static stability predicts the continuum of interleg coordination patterns in Drosophila

During walking, insects must coordinate the movements of their six legs for efficient locomotion. This interleg coordination is speed dependent: fast walking in insects is associated with tripod coordination patterns, whereas slow walking is associated with more variable, tetrapod-like patterns. To date, however, there has been no comprehensive explanation as to why these speed-dependent shifts in interleg coordination should occur in insects. Tripod coordination would be sufficient at low walking speeds. The fact that insects use a different interleg coordination pattern at lower speeds suggests that it is more optimal or advantageous at these speeds. Furthermore, previous studies focused on discrete tripod and tetrapod coordination patterns. Experimental data, however, suggest that changes observed in interleg coordination are part of a speed-dependent spectrum. Here, we explore these issues in relation to static stability as an important aspect for interleg coordination in Drosophila We created a model that uses basic experimentally measured parameters in fruit flies to find the interleg phase relationships that maximize stability for a given walking speed. The model predicted a continuum of interleg coordination patterns spanning the complete range of walking speeds as well as an anteriorly directed swing phase progression. Furthermore, for low walking speeds, the model predicted tetrapod-like patterns to be most stable, whereas at high walking speeds, tripod coordination emerged as most optimal. Finally, we validated the basic assumption of a continuum of interleg coordination patterns in a large set of experimental data from walking fruit flies and compared these data with the model-based predictions.

Walking with perturbations: a guide for biped humans and robots

This paper provides an update on the neural control of bipedal walking in relation to bioinspired models and robots. It is argued that most current models or robots are based on the construct of a symmetrical central pattern generator (CPG). However, new evidence suggests that CPG functioning is basically asymmetrical with its flexor half linked more tightly to the rhythm generator. The stability of bipedal gait, which is an important problem for robots and biological systems, is also addressed. While it is not possible to determine how biological biped systems guarantee stability, robot solutions can be useful to propose new hypotheses for biology. In the second part of this review, the focus is on gait perturbations, which is an important topic in robotics in view of the frequent falls of robots when faced with perturbations. From the human physiology it is known that the initial reaction often consists of a brief interruption followed by an adequate response. For instance, the successful recovery from a trip is achieved using some basic reactions (termed elevating and lowering strategies), that depend on the phase of the step cycle of the trip occurrence. Reactions to stepping unexpectedly in a hole depend on comparing expected and real feedback. Implementation of these ideas in models and robotics starts to emerge, with the most advanced robots being able to learn how to fall safely and how to deal with complicated disturbances such as provided by walking on a split-belt.

Leg force interference in polypedal locomotion

The examination of gaits and gait changes has been the focus of movement physiology and legged robot engineering since the first emergence of the fields. While most examinations have focused on bipedal and quadrupedal designs, many robotic implementations rely on the higher static stability of three or more pairs of legs. Thus far, however, the effect of number of pairs of legs on locomotion dynamics has not been examined. Accordingly, the present approach aims to extend available theory to polypedal designs and examines how the number of active walking legs affects body dynamics when combined with changing duty factors and phase relations. The model shows that ground force interference of higher numbers of active pairs of walking legs can prevent effective use of bouncing gaits, such as trot, and their associated advantages, such as energy efficiency, because significantly higher degrees of leg synchronization are required. It also shows that small changes in the leg coordination pattern have a much higher impact on the center-of-mass dynamics in locomotor systems with many legs than in those with fewer legs. In this way, the model reveals coordinative constraints for specific gaits facilitating the assessment of animal locomotion and economization of robotic locomotion.

It's Not a Bug, It's a Feature: Functional Materials in Insects

Over the course of their wildly successful proliferation across the earth, the insects as a taxon have evolved enviable adaptations to their diverse habitats, which include adhesives, locomotor systems, hydrophobic surfaces, and sensors and actuators that transduce mechanical, acoustic, optical, thermal, and chemical signals. Insect-inspired designs currently appear in a range of contexts, including antireflective coatings, optical displays, and computing algorithms. However, as over one million distinct and highly specialized species of insects have colonized nearly all habitable regions on the planet, they still provide a largely untapped pool of unique problem-solving strategies. With the intent of providing materials scientists and engineers with a muse for the next generation of bioinspired materials, here, a selection of some of the most spectacular adaptations that insects have evolved is assembled and organized by function. The insects presented display dazzling optical properties as a result of natural photonic crystals, precise hierarchical patterns that span length scales from nanometers to millimeters, and formidable defense mechanisms that deploy an arsenal of chemical weaponry. Successful mimicry of these adaptations may facilitate technological solutions to as wide a range of problems as they solve in the insects that originated them.

Leg-local neural mechanisms for searching and learning enhance robotic locomotion

Adapting motor output based on environmental forces is critical for successful locomotion in the real world. Arthropods use at least two neural mechanisms to adjust muscle activation while walking based on detected forces. Mechanism 1 uses negative feedback of leg depressor force to ensure that each stance leg supports an appropriate amount of the body's weight. Mechanism 2 encourages searching for ground contact if the leg supports no body weight. We expand the neural controller for MantisBot, a robot based upon a praying mantis, to include these mechanisms by incorporating leg-local memory and command neurons, as observed in arthropods. We present results from MantisBot transitioning between searching and stepping, mimicking data from animals as reported in the literature.

Topological and modality-specific representation of somatosensory information in the fly brain

Insects and mammals share similarities of neural organization underlying the perception of odors, taste, vision, sound, and gravity. We observed that insect somatosensation also corresponds to that of mammals. In Drosophila, the projections of all the somatosensory neuron types to the insect's equivalent of the spinal cord segregated into modality-specific layers comparable to those in mammals. Some sensory neurons innervate the ventral brain directly to form modality-specific and topological somatosensory maps. Ascending interneurons with dendrites in matching layers of the nerve cord send axons that converge to respective brain regions. Pathways arising from leg somatosensory neurons encode distinct qualities of leg movement information and play different roles in ground detection. Establishment of the ground pattern and genetic tools for neuronal manipulation should provide the basis for elucidating the mechanisms underlying somatosensation.

Simple analytical model reveals the functional role of embodied sensorimotor interaction in hexapod gaits

Insects have various gaits with specific characteristics and can change their gaits smoothly in accordance with their speed. These gaits emerge from the embodied sensorimotor interactions that occur between the insect’s neural control and body dynamic systems through sensory feedback. Sensory feedback plays a critical role in coordinated movements such as locomotion, particularly in stick insects. While many previously developed insect models can generate different insect gaits, the functional role of embodied sensorimotor interactions in the interlimb coordination of insects remains unclear because of their complexity. In this study, we propose a simple physical model that is amenable to mathematical analysis to explain the functional role of these interactions clearly. We focus on a foot contact sensory feedback called phase resetting, which regulates leg retraction timing based on touchdown information. First, we used a hexapod robot to determine whether the distributed decoupled oscillators used for legs with the sensory feedback generate insect-like gaits through embodied sensorimotor interactions. The robot generated two different gaits and one had similar characteristics to insect gaits. Next, we proposed the simple model as a minimal model that allowed us to analyze and explain the gait mechanism through the embodied sensorimotor interactions. The simple model consists of a rigid body with massless springs acting as legs, where the legs are controlled using oscillator phases with phase resetting, and the governed equations are reduced such that they can be explained using only the oscillator phases with some approximations. This simplicity leads to analytical solutions for the hexapod gaits via perturbation analysis, despite the complexity of the embodied sensorimotor interactions. This is the first study to provide an analytical model for insect gaits under these interaction conditions. Our results clarified how this specific foot contact sensory feedback contributes to generation of insect-like ipsilateral interlimb coordination during hexapod locomotion.

A Synthetic Nervous System Controls a Simulated Cockroach

The purpose of this work is to better understand how animals control locomotion. This knowledge can then be applied to neuromechanical design to produce more capable and adaptable robot locomotion. To test hypotheses about animal motor control, we model animals and their nervous systems with dynamical simulations, which we call synthetic nervous systems (SNS). However, one major challenge is picking parameter values that produce the intended dynamics. This paper presents a design process that solves this problem without the need for global optimization. We test this method by selecting parameter values for SimRoach2, a dynamical model of a cockroach. Each leg joint is actuated by an antagonistic pair of Hill muscles. A distributed SNS was designed based on pathways known to exist in insects, as well as hypothetical pathways that produced insect-like motion. Each joint’s controller was designed to function as a proportional-integral (PI) feedback loop and tuned with numerical optimization. Once tuned, SimRoach2 walks through a simulated environment, with several cockroach-like features. A model with such reliable low-level performance is necessary to investigate more sophisticated locomotion patterns in the future.

Biarticular elements as a contributor to energy efficiency: biomechanical review and application in bio-inspired robotics

Despite the increased interest in exoskeleton research in the last decades, not much progress has been made on the successful reduction of user effort. In humans, biarticular elements have been identified as one of the reasons for the energy economy of locomotion. This document gives an extensive literature overview concerning the function of biarticular muscles in human beings. The exact role of these muscles in the efficiency of human locomotion is reduced to three elementary functions: energy transfer towards distal joints, efficient control of output force direction and double joint actuation. This information is used to give an insight in the application of biarticular elements in bio-inspired robotics, i.e. bipedal robots, exoskeletons, robotic manipulators and prostheses. Additionally, an attempt is made to find an answer on the question whether the biarticular property leads to a unique contribution to energy efficiency of locomotion, unachievable by mono-articular alternatives. This knowledge is then further utilised to indicate how biarticular actuation of exoskeletons can contribute to an increased performance in reducing user effort.

Six-legged walking in insects: how CPGs, peripheral feedback, and descending signals generate coordinated and adaptive motor rhythms

Walking is a rhythmic locomotor behavior of legged animals, and its underlying mechanisms have been the subject of neurobiological research for more than 100 years. In this article, we review relevant historical aspects and contemporary studies in this field of research with a particular focus on the role of central pattern generating networks (CPGs) and their contribution to the generation of six-legged walking in insects. Aspects of importance are the generation of single-leg stepping, the generation of interleg coordination, and how descending signals influence walking. We first review how CPGs interact with sensory signals from the leg in the generation of leg stepping. Next, we summarize how these interactions are modified in the generation of motor flexibility for forward and backward walking, curve walking, and speed changes. We then review the present state of knowledge with regard to the role of CPGs in intersegmental coordination and how CPGs might be involved in mediating descending influences from the brain for the initiation, maintenance, modification, and cessation of the motor output for walking. Throughout, we aim to specifically address gaps in knowledge, and we describe potential future avenues and approaches, conceptual and methodological, with the latter emphasizing in particular options arising from the advent of neurogenetic approaches to this field of research and its combination with traditional approaches.

Mantisbot is a robotic model of visually guided motion in the praying mantis

Insects use highly distributed nervous systems to process exteroception from head sensors, compare that information with state-based goals, and direct posture or locomotion toward those goals. To study how descending commands from brain centers produce coordinated, goal-directed motion in distributed nervous systems, we have constructed a conductance-based neural system for our robot MantisBot, a 29 degree-of-freedom, 13.3:1 scale praying mantis robot. Using the literature on mantis prey tracking and insect locomotion, we designed a hierarchical, distributed neural controller that establishes the goal, coordinates different joints, and executes prey-tracking motion. In our controller, brain networks perceive the location of prey and predict its future location, store this location in memory, and formulate descending commands for ballistic saccades like those seen in the animal. The descending commands are simple, indicating only 1) whether the robot should walk or stand still, and 2) the intended direction of motion. Each joint's controller uses the descending commands differently to alter sensory-motor interactions, changing the sensory pathways that coordinate the joints' central pattern generators into one cohesive motion. Experiments with one leg of MantisBot show that visual input produces simple descending commands that alter walking kinematics, change the walking direction in a predictable manner, enact reflex reversals when necessary, and can control both static posture and locomotion with the same network.

A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion

A dynamical model of an animal's nervous system, or synthetic nervous system (SNS), is a potentially transformational control method. Due to increasingly detailed data on the connectivity and dynamics of both mammalian and insect nervous systems, controlling a legged robot with an SNS is largely a problem of parameter tuning. Our approach to this problem is to design functional subnetworks that perform specific operations, and then assemble them into larger models of the nervous system. In this paper, we present networks that perform addition, subtraction, multiplication, division, differentiation, and integration of incoming signals. Parameters are set within each subnetwork to produce the desired output by utilizing the operating range of neural activity, R, the gain of the operation, k, and bounds based on biological values. The assembly of large networks from functional subnetworks underpins our recent results with MantisBot.

Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review

Walking animals produce adaptive interlimb coordination during locomotion in accordance with their situation. Interlimb coordination is generated through the dynamic interactions of the neural system, the musculoskeletal system, and the environment, although the underlying mechanisms remain unclear. Recently, investigations of the adaptation mechanisms of living beings have attracted attention, and bio-inspired control systems based on neurophysiological findings regarding sensorimotor interactions are being developed for legged robots. In this review, we introduce adaptive interlimb coordination for legged robots induced by various factors (locomotion speed, environmental situation, body properties, and task). In addition, we show characteristic properties of adaptive interlimb coordination, such as gait hysteresis and different time-scale adaptations. We also discuss the underlying mechanisms and control strategies to achieve adaptive interlimb coordination and the design principle for the control system of legged robots.

Pre-processing and transfer entropy measures in motor neurons controlling limb movements

Directed information transfer measures are increasingly being employed in modeling neural system behavior due to their model-free approach, applicability to nonlinear and stochastic signals, and the potential to integrate repetitions of an experiment. Intracellular physiological recordings of graded synaptic potentials provide a number of additional challenges compared to spike signals due to non-stationary behaviour generated through extrinsic processes. We therefore propose a method to overcome this difficulty by using a preprocessing step based on Singular Spectrum Analysis (SSA) to remove nonlinear trends and discontinuities. We apply the method to intracellular recordings of synaptic responses of identified motor neurons evoked by stimulation of a proprioceptor that monitors limb position in leg of the desert locust. We then apply normalized delayed transfer entropy measures to neural responses evoked by displacements of the proprioceptor, the femoral chordotonal organ, that contains sensory neurones that monitor movements about the femoral-tibial joint. We then determine the consistency of responses within an individual recording of an identified motor neuron in a single animal, between repetitions of the same experiment in an identified motor neurons in the same animal and in repetitions of the same experiment from the same identified motor neuron in different animals. We found that delayed transfer entropy measures were consistent for a given identified neuron within and between animals and that they predict neural connectivity for the fast extensor tibiae motor neuron.

Template for the neural control of directed stepping generalized to all legs of MantisBot

We previously developed a neural controller for one leg of our six-legged robot, MantisBot, that could direct locomotion toward a goal by modulating leg-local reflexes with simple descending commands from a head sensor. In this work, we successfully apply an automated method to tune the control network for all three pairs of legs of our hexapod robot MantisBot in only 90 s with a desktop computer. Each foot's motion changes appropriately as the body's intended direction of travel changes. In addition, several results from studies of walking insects are captured by this model. This paper both demonstrates the broad applicability of this control method for robots, and suggests neural mechanisms underlying observations from walking insects.

Design process and tools for dynamic neuromechanical models and robot controllers

We present a serial design process with associated tools to select parameter values for a posture and locomotion controller for simulation of a robot. The controller is constructed from dynamic neuron and synapse models and simulated with the open-source neuromechanical simulator AnimatLab 2. Each joint has a central pattern generator (CPG), whose neurons possess persistent sodium channels. The CPG rhythmically inhibits motor neurons that control the servomotor's velocity. Sensory information coordinates the joints in the leg into a cohesive stepping motion. The parameter value design process is intended to run on a desktop computer, and has three steps. First, our tool FEEDBACKDESIGN uses classical control methods to find neural and synaptic parameter values that stably and robustly control servomotor output. This method is fast, testing over 100 parameter value variations per minute. Next, our tool CPGDESIGN generates bifurcation diagrams and phase response curves for the CPG model. This reveals neural and synaptic parameter values that produce robust oscillation cycles, whose phase can be rapidly entrained to sensory feedback. It also designs the synaptic conductance of inter-joint pathways. Finally, to understand sensitivity to parameters and how descending commands affect a leg's stepping motion, our tool SIMSCAN runs batches of neuromechanical simulations with specified parameter values, which is useful for searching the parameter space of a complicated simulation. These design tools are demonstrated on a simulation of a robot, but may be applied to neuromechanical animal models or physical robots as well.

Mechanosensation and Adaptive Motor Control in Insects

The ability of animals to flexibly navigate through complex environments depends on the integration of sensory information with motor commands. The sensory modality most tightly linked to motor control is mechanosensation. Adaptive motor control depends critically on an animal's ability to respond to mechanical forces generated both within and outside the body. The compact neural circuits of insects provide appealing systems to investigate how mechanical cues guide locomotion in rugged environments. Here, we review our current understanding of mechanosensation in insects and its role in adaptive motor control. We first examine the detection and encoding of mechanical forces by primary mechanoreceptor neurons. We then discuss how central circuits integrate and transform mechanosensory information to guide locomotion. Because most studies in this field have been performed in locusts, cockroaches, crickets, and stick insects, the examples we cite here are drawn mainly from these 'big insects'. However, we also pay particular attention to the tiny fruit fly, Drosophila, where new tools are creating new opportunities, particularly for understanding central circuits. Our aim is to show how studies of big insects have yielded fundamental insights relevant to mechanosensation in all animals, and also to point out how the Drosophila toolkit can contribute to future progress in understanding mechanosensory processing.

Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution

This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented.

A Review of Natural Joint Systems and Numerical Investigation of Bio-Inspired GFRP-to-Steel Joints

There are a great variety of joint types used in nature which can inspire engineering joints. In order to design such biomimetic joints, it is at first important to understand how biological joints work. A comprehensive literature review, considering natural joints from a mechanical point of view, was undertaken. This was used to develop a taxonomy based on the different methods/functions that nature successfully uses to attach dissimilar tissues. One of the key methods that nature uses to join dissimilar materials is a transitional zone of stiffness at the insertion site. This method was used to propose bio-inspired solutions with a transitional zone of stiffness at the joint site for several glass fibre reinforced plastic (GFRP) to steel adhesively bonded joint configurations. The transition zone was used to reduce the material stiffness mismatch of the joint parts. A numerical finite element model was used to identify the optimum variation in material stiffness that minimises potential failure of the joint. The best bio-inspired joints showed a 118% increase of joint strength compared to the standard joints.

Neuromodulation and Synaptic Plasticity for the Control of Fast Periodic Movement: Energy Efficiency in Coupled Compliant Joints via PCA

There are multiple indications that the nervous system of animals tunes muscle output to exploit natural dynamics of the elastic locomotor system and the environment. This is an advantageous strategy especially in fast periodic movements, since the elastic elements store energy and increase energy efficiency and movement speed. Experimental evidence suggests that coordination among joints involves proprioceptive input and neuromodulatory influence originating in the brain stem. However, the neural strategies underlying the coordination of fast periodic movements remain poorly understood. Based on robotics control theory, we suggest that the nervous system implements a mechanism to accomplish coordination between joints by a linear coordinate transformation from the multi-dimensional space representing proprioceptive input at the joint level into a one-dimensional controller space. In this one-dimensional subspace, the movements of a whole limb can be driven by a single oscillating unit as simple as a reflex interneuron. The output of the oscillating unit is transformed back to joint space via the same transformation. The transformation weights correspond to the dominant principal component of the movement. In this study, we propose a biologically plausible neural network to exemplify that the central nervous system (CNS) may encode our controller design. Using theoretical considerations and computer simulations, we demonstrate that spike-timing-dependent plasticity (STDP) for the input mapping and serotonergic neuromodulation for the output mapping can extract the dominant principal component of sensory signals. Our simulations show that our network can reliably control mechanical systems of different complexity and increase the energy efficiency of ongoing cyclic movements. The proposed network is simple and consistent with previous biologic experiments. Thus, our controller could serve as a candidate to describe the neural control of fast, energy-efficient, periodic movements involving multiple coupled joints.