Forward Locomotion Research Articles

Forward and backward control of an ultrafast millimeter-scale microrobot via vibration mode transition.

The ability to move backward is crucial for millimeter-scale microrobots to navigate dead-end tunnels that are too narrow to allow for turning maneuvers. In this study, we introduce a 15-mm-long legged microrobot, BHMbot-B, which is capable of rapid forward and backward locomotion through vibration mode transition control. By properly arranging the vibratory motions of the magnet, cantilever, and linkages, the pitching movement of the body and the vibration of the forelegs are in phase during the first-order vibration mode of the cantilever and in antiphase during the second-order mode, which induces the forward and backward movement of the microrobot. Owing to its outstanding load-bearing capacity, the BHMbot-B equipped with dual electromagnetic actuators, an onboard battery, and a control circuit, can execute complex running trajectories under wireless command. Its maximum untethered running speeds are evaluated as 18.0 BL/s (360 mm/s) in the forward direction and 16.9 BL/s (338 mm/s) in the backward direction.

Muscle Activity During Lateral And Forward Locomotion With Body Weight Support

Muscle Activity During Lateral And Forward Locomotion With Body Weight Support

Influence Of Body Weight Support On Physiological Responses During Lateral And Forward Locomotion

Influence Of Body Weight Support On Physiological Responses During Lateral And Forward Locomotion

Kinematically distinct saccades are used in a context-dependent manner by larval zebrafish

Saccades are rapid eye movements that are used by all species with good vision. In this study, we set out to characterize the complete repertoire of larval zebrafish horizontal saccades to gain insight into their contributions to visually guided behavior and underlying neural control. We identified five saccade types, defined by systematic differences in kinematics and binocular coordination, which were differentially expressed across a variety of behavioral contexts. Conjugate saccades formed a large group that serves at least four functions. These include fast phases of the optokinetic nystagmus, visual scanning in stationary animals, and shifting gaze in coordination with body turns. In addition, we discovered a previously undescribed pattern of eye-body coordination in which small conjugate saccades partially oppose head rotation to maintain gaze during forward locomotion. Convergent saccades were coordinated with body movements to foveate prey targets during hunting. Detailed kinematic analysis showed that conjugate and convergent saccades differed in the millisecond coordination of the eyes and body and followed distinct velocity main sequence relationships. This challenges the prevailing view that all horizontal saccades are controlled by a common brainstem circuit and instead indicates saccade-type-specific neural control.

A two-layer neural circuit controls fast forward locomotion in Drosophila

Fast forward locomotion is critical for animal hunting and escaping behaviors. However, how the underlying neural circuit is wired at synaptic resolution to decide locomotion direction and speed remains poorly understood. Here, we identified in the ventral nerve cord (VNC) a set of ascending cholinergic neurons (AcNs) to be command neurons capable of initiating fast forward peristaltic locomotion in Drosophila larvae. Targeted manipulations revealed that AcNs are necessary and sufficient for fast forward locomotion. AcNs can activate their postsynaptic partners, A01j and A02j; both are interneurons with locomotory rhythmicity. Activated A01j neurons form a posterior-anteriorly descendent gradient in output activity along the VNC to launch forward locomotion from the tail. Activated A02j neurons exhibit quicker intersegmental transmission in activity that enables fast propagation of motor waves. Our work revealed a global neural mechanism that coordinately controls the launch direction and propagation speed of Drosophila locomotion, furthering the understanding of the strategy for locomotion control.

STEERING FROM THE REAR: COORDINATION OF CENTRAL PATTERN GENERATORS UNDERLYING NAVIGATION BY ASCENDING INTERNEURONS.

Understanding how animals coordinate movements to achieve goals is a fundamental pursuit in neuroscience. Here we explore how neurons that reside in posterior lower-order regions of a locomotor system project to anterior higher-order regions to influence steering and navigation. We characterized the anatomy and functional role of a population of ascending interneurons in the ventral nerve cord of Drosophila larvae. Through electron microscopy reconstructions and light microscopy, we determined that the cholinergic 19f cells receive input primarily from premotor interneurons and synapse upon a diverse array of postsynaptic targets within the anterior segments including other 19f cells. Calcium imaging of 19f activity in isolated central nervous system (CNS) preparations in relation to motor neurons revealed that 19f neurons are recruited into most larval motor programmes. 19f activity lags behind motor neuron activity and as a population, the cells encode spatio-temporal patterns of locomotor activity in the larval CNS. Optogenetic manipulations of 19f cell activity in isolated CNS preparations revealed that they coordinate the activity of central pattern generators underlying exploratory headsweeps and forward locomotion in a context and location specific manner. In behaving animals, activating 19f cells suppressed exploratory headsweeps and slowed forward locomotion, while inhibition of 19f activity potentiated headsweeps, slowing forward movement. Inhibiting activity in 19f cells ultimately affected the ability of larvae to remain in the vicinity of an odor source during an olfactory navigation task. Overall, our findings provide insights into how ascending interneurons monitor motor activity and shape interactions amongst rhythm generators underlying complex navigational tasks.

A spinal circuit model with asymmetric cervical-lumbar layout controls backward locomotion and scratching in quadrupeds

Locomotion and scratching are basic motor functions which are critically important for animal survival. Although the spinal circuits governing forward locomotion have been extensively investigated, the organization of spinal circuits and neural mechanisms regulating backward locomotion and scratching remain unclear. Here, we extend a model by Danner et al. to propose a spinal circuit model with asymmetrical cervical-lumbar layout to investigate these issues. In the model, the left–right alternation within the cervical and lumbar circuits is mediated by V 0D and V 0V commissural interneurons (CINs), respectively. With different control strategies, the model closely reproduces multiple experimental data of quadrupeds in different motor behaviors. Specifically, under the supraspinal drive, walk and trot are expressed in control condition, half-bound is expressed after deletion of V 0V CINs, and bound is expressed after deletion of V0 (V 0D and V 0V) CINs; in addition, unilateral hindlimb scratching occurs in control condition and synchronous bilateral hindlimb scratching appears after deletion of V 0V CINs. Under the combined drive of afferent feedback and perineal stimulation, different coordination patterns between hindlimbs during BBS (backward-biped-spinal) locomotion are generated. The results suggest that (1) the cervical and lumbar circuits in the spinal network are asymmetrically recruited during particular rhythmic limb movements. (2) Multiple motor behaviors share a single spinal network under the reconfiguration of the spinal network by supraspinal inputs or somatosensory feedback. Our model provides new insights into the organization of motor circuits and neural control of rhythmic limb movements.

Referent Control of Side-to-Side Body-Weight Transfer During Standing and Stepping in Adults

Research suggests that locomotion may be primarily caused by shifting stable body balance from one location in the environment to another with subsequent rhythmical muscle activation by the central pattern generator (CPG), constituting a multi-level control system. All levels interact with environmental forces affected by proprioceptive and vestibular reflexes as well as vision. A similar multi-level control schema is likely used to shift body balance laterally when the body weight is rhythmically transferred from side-to-side. In order to do so, the system shifts a specific body posture in space. This body posture is referred to as the threshold or referent body posture, R, at which all muscles involved can be at rest but are activated depending on the deflection of the actual body posture, Q, from R. This concept has previously been investigated for forward and backward locomotion. The purpose of the present study was to verify if it was also applicable to locomotor tasks in other directions such as sidestepping. We predicted that during sidestepping, the actual and referent posture can transiently match each other bringing the activity of multiple muscles to a minimum. The existence of such minima was demonstrated in healthy adults performing three locomotor tasks involving shifts of the body weight from side-to-side thus further supporting the validity of the multi-level control scheme of locomotion.

Development of equation of motion deciphering locomotion including omega turns of Caenorhabditis elegans.

Locomotion is a fundamental behavior of Caenorhabditis elegans (C. elegans). Previous works on kinetic simulations of animals helped researchers understand the physical mechanisms of locomotion and the muscle-controlling principles of neuronal circuits as an actuator part. It has yet to be understood how C. elegans utilizes the frictional forces caused by the tension of its muscles to perform sequenced locomotive behaviors. Here, we present a two-dimensional rigid body chain model for the locomotion of C. elegans by developing Newtonian equations of motion for each body segment of C. elegans. Having accounted for friction-coefficients of the surrounding environment, elastic constants of C. elegans, and its kymogram from experiments, our kinetic model (ElegansBot) reproduced various locomotion of C. elegans such as, but not limited to, forward-backward-(omega turn)-forward locomotion constituting escaping behavior and delta-turn navigation. Additionally, ElegansBot precisely quantified the forces acting on each body segment of C. elegans to allow investigation of the force distribution. This model will facilitate our understanding of the detailed mechanism of various locomotive behaviors at any given friction-coefficients of the surrounding environment. Furthermore, as the model ensures the performance of realistic behavior, it can be used to research actuator-controller interaction between muscles and neuronal circuits.

Development of equation of motion deciphering locomotion including omega turns of Caenorhabditis elegans

Locomotion is a fundamental behavior of Caenorhabditis elegans (C. elegans). Previous works on kinetic simulations of animals helped researchers understand the physical mechanisms of locomotion and the muscle-controlling principles of neuronal circuits as an actuator part. It has yet to be understood how C. elegans utilizes the frictional forces caused by the tension of its muscles to perform sequenced locomotive behaviors. Here, we present a two-dimensional rigid body chain model for the locomotion of C. elegans by developing Newtonian equations of motion for each body segment of C. elegans. Having accounted for friction-coefficients of the surrounding environment, elastic constants of C. elegans, and its kymogram from experiments, our kinetic model (ElegansBot) reproduced various locomotion of C. elegans such as, but not limited to, forward-backward-(omega turn)-forward locomotion constituting escaping behavior and delta-turn navigation. Additionally, ElegansBot precisely quantified the forces acting on each body segment of C. elegans to allow investigation of the force distribution. This model will facilitate our understanding of the detailed mechanism of various locomotive behaviors at any given friction-coefficients of the surrounding environment. Furthermore, as the model ensures the performance of realistic behavior, it can be used to research actuator-controller interaction between muscles and neuronal circuits.

Actuation strategy characterizations of a vibro-impact crawling robot driven by dielectric elastomer actuators

Vibro-impact crawling robots driven by the emerging dielectric elastomer actuators (DEAs) feature the advantages of reduced system complexity and bidirectional locomotion capability. However, due to the lack of systematic dynamic models and in-depth investigations, the fundamental locomotion principles of the robots are still unclear and therefore their applications in real-world scenarios remain hindered. In this paper, a comprehensive dynamic model of this robot is developed by considering the complex interactions between the electro-mechanical coupling, impact mechanism, and multiple nonlinear friction characteristics. By incorporating extensive modeling and experimental studies, we explain the fundamental principles that lead to the bidirectional locomotion of the robot. The actuation strategies for both forward and backward locomotion are characterized in-depth, which include the actuation frequencies, relative phases, and waveforms. Three typical contact surface cases (i.e. rigid & dry, rigid & viscous, compliant & viscous) are considered in this paper, where we report the changes in the optimal actuation strategies for bidirectional locomotion with the contact surfaces and reveal the influences of key factors of friction coefficient, viscosity, and material compliance. The key findings reported in this work can build foundations for developing a highly robust and efficient DEA-driven vibro-impact crawling robot for broad applications.

Neuronal mechanisms regulating locomotion in adult Drosophila.

The coordinated action of multiple leg joints and muscles is required even for the simplest movements. Understanding the neuronal circuits and mechanisms that generate precise movements is essential for comprehending the neuronal basis of the locomotion and to infer the neuronal mechanisms underlying several locomotor-related diseases. Drosophila melanogaster provides an excellent model system for investigating the neuronal circuits underlying motor behaviors due to its simple nervous system and genetic accessibility. This review discusses current genetic methods for studying locomotor circuits and their function in adult Drosophila. We highlight recently identified neuronal pathways that modulate distinct forward and backward locomotion and describe the underlying neuronal control of leg swing and stance phases in freely moving flies. We also report various automated leg tracking methods to measure leg motion parameters and define inter-leg coordination, gait and locomotor speed of freely moving adult flies. Finally, we emphasize the role of leg proprioceptive signals to central motor circuits in leg coordination. Together, this review highlights the utility of adult Drosophila as a model to uncover underlying motor circuitry and the functional organization of the leg motor system that governs correct movement.

Power consumption during forward locomotion of C. elegans: an electrical circuit simulation

Biological neuronal networks are of great interest for emerging technological approaches such as neuromorphic engineering due to their capability to efficiently process information. To understand the principles governing this energy efficiency, it is useful to investigate model organisms with small and well-characterized neuronal networks. Caenorhabditis elegans (C. elegans) is such a model organism and perfectly suited for this purpose, because its neuronal network consists of only 302 neurons whose interconnections are known. In this work, we design an ideal electrical circuit modeling this neuronal network in combination with the muscles it controls. We simulate this circuit by a run-time efficient wave digital algorithm. This allows us to investigate the energy consumption of the network occurring during locomotion of C. elegans and hence deduce potential design principles from an energy efficiency point of view. Simulation results verify that a locomotion is indeed generated. We conclude from the corresponding energy consumption rates that a small number of neurons in contrast to a high number of interconnections is favorable for consuming only little energy. This underlines the importance of interneurons. Moreover, we find that gap junctions are a more energy-efficient connection type than synapses, and inhibitory synapses consume more energy than excitatory ones. However, the energetically cheapest connection types are not the most frequent ones in C. elegans’ neuronal network. Therefore, a potential design principle of the network could be a balance between low energy costs and a certain functionality.Graphical abstractEnergy consumption rates during forward locomotion of C. elegans. a Rates for the ion channels of all neurons, and b average rates for ion channels of a single, active neuron. c Comparison of average rates with respect to the number of active sensory, motor, and interneurons. d Rates for all gap junctions and synapses, and e rates for all synapses of a specific neurotransmitter type. f Average rates for a single synaptic or gap junctions connection vs the total number of connections present for the type of connection (i.e. ACh-synapse, GABA-synapse, Glu-synapse, gap junction).

Dynamic modeling for continuous locomotion of bionic soft worm-like robot

In the nature, worm-like robot can bend its slender body into an arch bridge when it moves, and it will move quickly in this deformation mode to nimbly escape from enemies once encountering danger. Inspired by this locomotion mechanism, a plenty of bionic soft worm-like robots have been devised and studied adopting quasi-static method. Nevertheless, how to describe the stick-slip transition and the continuity of the entire locomotion process of soft worm-like robots is still a challenging problem. To settle the above issue, a simplified curved beam model is developed to depict a type of soft worm-like robot move forward continuously by controlling its initial curvature in this paper. Based on the curved beam model, a novel dynamic modeling method is further proposed to solve stick-slip nonlinear switchover problem of the soft robot. Since the curved beam is divided into finite segments, an angular variable of an arbitrary point is introduced to elaborate the deformed shape of the curved beam, and then the dimensionless discretized equations of motion can be established via theoretical derivation and solved by implicit integration method. Through dynamic simulation and analysis, a continuous forward locomotion and net displacement of the robot can be easily observed, and several laws of the locomotion are uncovered, which reveals the stick-slip dynamic behaviors. Distinguished from the normal quasi-static modeling approach, the law of motion of the soft worm-like robot is clarified from the dynamic point of view, which will provide important reference for structural design and optimization control of similar soft robots.

Abstract TP72: Capturing the Effects of Non-Body Weight Supported Backward and Forward Treadmill Training on Chronic Post-Stroke Lower Extremity Spasticity and Walking Speed

Introduction: Stroke-related walking impairment significantly impacts ambulatory independence and is a major public health issue. While the factors influencing walking impairment after stroke are multifactorial, the role of lower extremity spasticity is one factor that remains unsettled. For example, while past studies have suggested that increased lower extremity tone led to maladaptive walking patterns, others have suggested that spasticity may improve walking ability by compensating for weakness. Hence, the objective of this exploratory study was to determine the effects of serial non-body weight backward (BLTT, n=19) and forward (FLTT, n=21) locomotion treadmill training on lower extremity spasticity (LES). We hypothesized that both BLTT and FLTT will improve LES, but to a greater extent with BLTT. Methods: Data from a recently completed RCT (NCT04553198) consisting of 40-community-dwelling stroke survivors with mild-moderate walking impairment, who completed nine 30-minute training sessions (3 per week x 3) were used for this secondary exploratory analysis. Spasticity was assessed (quadricep, hamstrings, gastrocnemius, soleus muscles) by a trained therapist blinded to study intervention using the modified Ashworth Scale (MAS) and 10mWT was used to assess the relationship to walking speed. Results: Training resulted in reduced spasticity in the quadriceps (p = 0.01), but not in the other three muscle groups. Contrary to our working hypothesis, FLTT drove the improvement in spasticity in both quadriceps and hamstrings (p <0.01 and p = 0.02, respectively). Both groups demonstrated improvement in walking speed (p< 0.0001), but no correlation between change MAS and walking speed was observed.

Simultaneous control of forward and backward locomotion by spinal sensorimotor circuits.

Mammals walk in different directions, such as forward and backward. In human infants/adults and decerebrate cats, one leg can walk forward and the other backward simultaneously on a split-belt treadmill, termed hybrid or bidirectional locomotion. The purpose of the present study was to determine if spinal sensorimotor circuits generate hybrid locomotion and if so, how the limbs remain coordinated. We tested hybrid locomotion in 11 intact cats and in five following complete spinal thoracic transection (spinal cats) at three treadmill speeds with the hindlimbs moving forward, backward or bidirectionally. All intact cats generated hybrid locomotion with the forelimbs on a stationary platform. Four of five spinal cats generated hybrid locomotion, also with the forelimbs on a stationary platform, but required perineal stimulation. During hybrid locomotion, intact and spinal cats positioned their forward and backward moving hindlimbs caudal and rostral to the hip, respectively. The hindlimbs maintained consistent left-right out-of-phase alternation in the different stepping directions. Our results suggest that spinal locomotor networks generate hybrid locomotion by following certain rules at phase transitions. We also found that stance duration determined cycle duration in the different locomotor directions/conditions, consistent with a common rhythm-generating mechanism for different locomotor directions. Our findings provide additional insight on how left-right spinal networks and sensory feedback from the limbs interact to coordinate the hindlimbs and provide stability during locomotion in different directions. KEY POINTS: Terrestrial mammals can walk forward and backward, which is controlled in part by spinal sensorimotor circuits. Humans and cats also perform bidirectional or hybrid locomotion on a split-belt treadmill with one leg going forward and the other going backward. We show that cats with a spinal transection can perform hybrid locomotion and maintain left-right out-of-phase coordination, indicating that spinal sensorimotor circuits can perform simultaneous forward and backward locomotion. We also show that the regulation of cycle duration and phase duration is conserved across stepping direction, consistent with a common rhythm-generating mechanism for different stepping directions. The results help us better understand how spinal networks controlling the left and right legs enable locomotion in different directions.

Exploring storm petrel pattering and sea-anchoring using deep reinforcement learning

Developing hybrid aerial-aquatic vehicles that can interact with water surfaces while remaining aloft is valuable for various tasks, including ecological monitoring, water quality sampling, and search and rescue operations. Storm petrels are a group of pelagic seabirds that exhibit a unique locomotion pattern known as ‘pattering’ or ‘sea-anchoring,’ which is hypothesized to support forward locomotion and/or stationary posture at the water surface. In this study, we use morphological measurements of three storm petrel species and aero/hydrodynamic models to develop a computational storm petrel model and interact it with a hybrid fluid environment. Using deep reinforcement learning algorithms, we find that the storm petrel model exhibits high maneuverability and stability under a wide range of constant wind velocities after training. We also verify in the simulation that the storm petrel can use its ‘pattering’ or ‘sea-anchoring’ behavior to achieve different biomechanical sub-tasks (e.g. weight support, forward locomotion, stabilization) and adapt it under different wind speeds and optimization objectives. Specifically, we observe an adjustment in storm petrel’s movement patterns as wind velocity increases and quantitively analyze its biomechanics underneath. Our results provide new insights into how storm petrels achieve efficient locomotion and dynamic stability at the air–water interface and adapt their behaviors to different wind velocities and tasks in open environments. Ultimately, our study will guide the design of next-generation biomimetic petrel-inspired robots for tasks requiring proximity to the water interface and efficiency.

The mesencephalic locomotor region recruits V2a reticulospinal neurons to drive forward locomotion in larval zebrafish

The mesencephalic locomotor region (MLR) is a brain stem area whose stimulation triggers graded forward locomotion. How MLR neurons recruit downstream vsx2+ (V2a) reticulospinal neurons (RSNs) is poorly understood. Here, to overcome this challenge, we uncovered the locus of MLR in transparent larval zebrafish and show that the MLR locus is distinct from the nucleus of the medial longitudinal fasciculus. MLR stimulations reliably elicit forward locomotion of controlled duration and frequency. MLR neurons recruit V2a RSNs via projections onto somata in pontine and retropontine areas, and onto dendrites in the medulla. High-speed volumetric imaging of neuronal activity reveals that strongly MLR-coupled RSNs are active for steering or forward swimming, whereas weakly MLR-coupled medullary RSNs encode the duration and frequency of the forward component. Our study demonstrates how MLR neurons recruit specific V2a RSNs to control the kinematics of forward locomotion and suggests conservation of the motor functions of V2a RSNs across vertebrates.

How Free Swimming Fosters the Locomotion of a Purely Oscillating Fish-like Body.

The recoil motions in free swimming, given by lateral and angular rigid motions due to the interaction with the surrounding water, are of great importance for a correct evaluation of both the forward locomotion speed and efficiency of a fish-like body. Their contribution is essential for calculating the actual movements of the body rear end whose prominent influence on the generation of the proper body deformation was established a long time ago. In particular, the recoil motions are found here to promote a dramatic improvement of the performance when damaged fishes, namely for a partial functionality of the tail or even for its complete loss, are considered. In fact, the body deformation, which turns out to become oscillating and symmetric in the extreme case, is shown to recover in the water frame a kind of undulation leading to a certain locomotion speed though at the expense of a large energy consumption. There has been a deep interest in the subject since the infancy of swimming studies, and a revival has recently arisen for biomimetic applications to robotic fish-like bodies. We intend here to apply a theoretical impulse model to the oscillating fish in free swimming as a suitable test case to strengthen our belief in the beneficial effects of the recoil motions. At the same time, we intend to exploit the linearity of the model to detect from the numerical simulations the intrinsic physical reasons related to added mass and vorticity release behind the experimental observations.

Closed‐Loop Soft Robot Control Frameworks with Coordinated Policies Based on Reinforcement Learning and Proprioceptive Self‐Sensing

AbstractRecent advances in soft robots have been achieved by using compliant materials and exploiting the advantages of the soft structural designs of living organisms. Living organisms (which have theoretically infinite degrees of freedom) are not only mechanically soft but are also capable of smooth harmonic motions, thanks to global coordination and the individual sensing and control of local tissues. Despite improvements in structural designs, few soft robot control frameworks for global object‐oriented behaviors are reported. Such a framework will require the use of multiple segments, with local sensing and independent control using coordinated policies. Here, a class of reinforcement learning based control frameworks for soft robots (with high degrees of freedom) is presented, and their ability to conduct global tasks is demonstrated. Coordinated control policies are formulated to control multiple segments with independently controllable embedded actuators, based on localized proprioceptive self‐sensing capabilities. The control frameworks are employed to develop soft physical robots. Demonstrations and experiments include the forward and backward locomotion of multichannel soft robotic flatworms. This approach is applicable to multifunctional, high degrees of freedom soft robots, as demonstrated by experiments with light‐sensitive locomotion.