Locomotion Gaits Research Articles

A CPG-Based Versatile Control Framework for Metameric Earthworm-Like Robotic Locomotion.

Annelids such as earthworms are considered to have central pattern generators (CPGs) that generate rhythms in neural circuits to coordinate the deformation of body segments for effective locomotion. At present, the states of earthworm-like robot segments are often assigned holistically and artificially by mimicking the earthworms' retrograde peristalsis wave, which is unable to adapt their gaits for variable environments and tasks. This motivates theauthorsto extend the bioinspired research from morphology to neurobiology by mimicking the CPG to build a versatile framework for spontaneous motion control. Here, the spatiotemporal dynamics is exploited from the coupled Hopf oscillators to not only unify the two existing gait generators for restoring temporal-symmetric phase-coordinated gaits and discrete gaits but also generate novel temporal-asymmetric phase-coordinated gaits. Theoretical and experimental tests consistently confirm that the introduction of temporal asymmetry improves the robot's locomotion performance. The CPG-based controller also enables seamless online switching of locomotion gaits to avoid abrupt changes, sharp stops, and starts, thus improving the robot's adaptability in variable working scenarios.

Orientation Control System: Enhancing Aerial Maneuvers for Quadruped Robots.

For legged robots, aerial motions are the only option to overpass obstacles that cannot be circumvented with standard locomotion gaits. In these cases, the robot must perform a leap to either jump onto the obstacle or fly over it. However, these movements represent a challenge, because, during the flight phase, the Center of Mass (CoM) cannot be controlled, and there is limited controllability over the orientation of the robot. This paper focuses on the latter issue and proposes an Orientation Control System (OCS), consisting of two rotating and actuated masses (flywheels or reaction wheels), to gain control authority on the orientation of the robot. Due to the conservation of angular momentum, the rotational velocity if the robot can be adjusted to steer the robot's orientation, even when the robot has no contact with the ground. The axes of rotation of the flywheels are designed to be incident, leading to a compact orientation control system that is capable of controlling both roll and pitch angles, considering the different moments of inertia in the two directions. The concept was tested by means of simulations on the robot Solo12.

Performance enhancement of an integrated electro-hydraulic actuator using dynamic modeling and optimization

For robotic applications, hydraulic actuation is still an open research issue. A hydraulic actuation solution based on integrated hydraulics is presented in the form of an integrated electro-hydraulic Actuator (IEHA). This actuator tackles some issues related to compactness and self-contained autonomous robotic systems. One of its primary uses is the actuation of the hydraulic humanoid robot HYDROïD. Primary experimental validation of the IEHA actuator has suffered from several low-performance issues. The main reason for such performance is due to that inner friction forces and leakage losses were not addressed or modeled yet. In this paper, a detailed dynamic model of the IEHA actuator is needed to identify the mechanical components that affect the volumetric and mechanical efficiencies of the actuator. The dynamic model focuses on the dynamics of essential parameters and components like the micro-pump pistons, their internal leakage, and friction during actuation. The design parameters of these mechanical parts are optimally calculated to produce a new enhanced dynamic model. The new IEHA model is verified through simulation to actuate the ankle pitch rotation of the robot. A genetic algorithm is used to optimize the geometrical and design parameters while enhancing the overall mechanical and volumetric efficiencies of the IEHA. The IEHA model was able to generate a locomotion gait cycle successfully for a walking speed of 1.17 m/s. Simulation results have shown that the design enhancements led to 95% and 97% volumetric and mechanical efficiencies respectively. Finally, the new enhanced IEHA prototype is tested experimentally. The output flow of the new prototype has improved by approximately 58%.

A soft microrobot with highly deformable 3D actuators for climbing and transitioning complex surfaces.

The climbing microrobots have attracted growing attention due to their promising applications in exploration and monitoring of complex, unstructured environments. Soft climbing microrobots based on muscle-like actuators could offer excellent flexibility, adaptability, and mechanical robustness. Despite the remarkable progress in this area, the development of soft microrobots capable of climbing on flat/curved surfaces and transitioning between two different surfaces remains elusive, especially in open spaces. In this study, we address these challenges by developing voltage-driven soft small-scale actuators with customized 3D configurations and active stiffness adjusting. Combination of programmed strain distributions in liquid crystal elastomers (LCEs) and buckling-driven 3D assembly, guided by mechanics modeling, allows for voltage-driven, complex 3D-to-3D shape morphing (bending angle > 200°) at millimeter scales (from 1 to 10 mm), which is unachievable previously. These soft actuators enable development of morphable electroadhesive footpads that can conform to different curved surfaces and stiffness-variable smart joints that allow different locomotion gaits in a single microrobot. By integrating such morphable footpads and smart joints with a deformable body, we report a multigait, soft microrobot (length from 6 to 90 mm, and mass from 0.2 to 3 g) capable of climbing on surfaces with diverse shapes (e.g., flat plane, cylinder, wavy surface, wedge-shaped groove, and sphere) and transitioning between two distinct surfaces. We demonstrate that the microrobot could navigate from one surface to another, recording two corresponding ceilings when carrying an integrated microcamera. The developed soft microrobot can also flip over a barrier, survive extreme compression, and climb bamboo and leaf.

IMU-Based Classification of Locomotion Modes, Transitions, and Gait Phases with Convolutional Recurrent Neural Networks.

This paper focuses on the classification of seven locomotion modes (sitting, standing, level ground walking, ramp ascent and descent, stair ascent and descent), the transitions among these modes, and the gait phases within each mode, by only using data in the frequency domain from one or two inertial measurement units. Different deep neural network configurations are investigated and compared by combining convolutional and recurrent layers. The results show that a system composed of a convolutional neural network followed by a long short-term memory network is able to classify with a mean F1-score of 0.89 and 0.91 for ten healthy subjects, and of 0.92 and 0.95 for one osseointegrated transfemoral amputee subject (excluding the gait phases because they are not labeled in the data-set), using one and two inertial measurement units, respectively, with a 5-fold cross-validation. The promising results obtained in this study pave the way for using deep learning for the control of transfemoral prostheses with a minimum number of inertial measurement units.

An Energy-Saving Snake Locomotion Pattern Learned in a Physically Constrained Environment With Online Model-Based Policy Gradient Method

Abstract Snake robots, composed of sequentially connected joint actuators, have recently gained increasing attention in the industrial field, like life detection in narrow space. Such robots can navigate the complex environment via the cooperation of multiple motors located on the backbone. However, controlling the robots in a physically constrained environment is challenging, and conventional control strategies can be energy-inefficient or even fail to navigate to the destination. This work develops a snake locomotion gait policy for energy-efficient control via deep reinforcement learning (DRL). After establishing the environment model, we apply a physics constrained online policy gradient method based on the proximal policy optimization (PPO) objective function of each joint motor parameterized by angular velocity. The DRL agent learns the standard serpenoid curve at each timestep. The policy is updated based on the robot’s observations and estimation of the current states. The robot simulator and task environment are built upon PyBullet. Compared to conventional control strategies, the snake robots controlled by the trained PPO agent can achieve faster movement and a more energy-efficient locomotion gait. This work demonstrates that DRL provides an energy-efficient solution for robot control.

Analysis of Kinematic Characteristics of Saanen Goat Spine under Multi-Slope.

In order to improve the slope movement stability and flexibility of quadruped robot, a theoretical design method of a flexible spine of a robot that was based on bionics was proposed. The kinematic characteristics of the spine were analyzed under different slopes with a Saanen goat as the research object. A Qualisys track manager (QTM) gait analysis system was used to obtain the trunk movement of goats under multiple slopes, and linear time normalization (LTN) was used to calibrate and match typical gait cycles to characterize the goat locomotion gait under slopes. Firstly, the spatial angle changes of cervical thoracic vertebrae, thoracolumbar vertebrae, and lumbar vertebrae were compared and analyzed under 0°, 5°, 10°, and 15° slopes, and it was found that the rigid and flexible coupling structure between the thoraco-lumbar vertebrae played an obvious role when moving on the slope. Moreover, with the increase in slope, the movement of the spine changed to the coupling movement of thoraco-lumbar coordination movement and a flexible swing of lumbar vertebrae. Then, the Gaussian mixture model (GMM) clustering algorithm was used to analyze the changes of the thoraco-lumbar vertebrae and lumbar vertebrae in different directions. Combined with anatomical knowledge, it was found that the motion of the thoraco-lumbar vertebrae and lumbar vertebrae in the goat was mainly manifested as a left-right swing in the coronal plane. Finally, on the basis of the analysis of the maximin and variation range of the thoraco-lumbar vertebrae and lumbar vertebrae in the coronal plane, it was found that the coupling motion of the thoraco-lumbar cooperative motion and flexible swing of the lumbar vertebrae at the slope of 10° had the most significant effect on the motion stability. SSE, R2, adjusted-R2, and RMSE were used as evaluation indexes, and the general equations of the spatial fitting curve of the goat spine were obtained by curve fitting of Matlab software. Finally, Origin software was used to obtain the optimal fitting spatial equations under eight movements of the goat spine with SSE and adjusted-R2 as indexes. The research will provide an idea for the bionic spine design with variable stiffness and multi-direction flexible bending, as well as a theoretical reference for the torso design of a bionic quadruped robot.

Multi-environment robotic transitions through adaptive morphogenesis.

The current proliferation of mobile robots spans ecological monitoring, warehouse management and extreme environment exploration, to an individual consumer's home1-4. This expanding frontier of applications requires robots to transit multiple environments, a substantial challenge that traditional robot design strategies have not effectively addressed5,6. For example, biomimetic design-copying an animal's morphology, propulsion mechanism and gait-constitutes one approach, but it loses the benefits of engineered materials and mechanisms that can be exploited to surpass animal performance7,8. Other approaches add a unique propulsive mechanism for each environment to the same robot body, which can result in energy-inefficient designs9-11. Overall, predominant robot design strategies favour immutable structures and behaviours, resulting in systems incapable of specializing across environments12,13. Here, to achieve specialized multi-environment locomotion through terrestrial, aquatic and the in-between transition zones, we implemented 'adaptive morphogenesis', a design strategy in which adaptive robot morphology and behaviours are realized through unified structural and actuation systems. Taking inspiration from terrestrial and aquatic turtles, we built a robot that fuses traditional rigid components and soft materials to radically augment the shape of its limbs and shift its gaits for multi-environment locomotion. The interplay of gait, limb shape and the environmental medium revealed vital parameters that govern the robot's cost of transport. The results attest that adaptive morphogenesis is a powerful method to enhance the efficiency of mobile robots encountering unstructured, changing environments.

Autonomous robotic exploration with simultaneous environment and traversability models learning.

In this study, we address generalized autonomous mobile robot exploration of unknown environments where a robotic agent learns a traversability model and builds a spatial model of the environment. The agent can benefit from the model learned online in distinguishing what terrains are easy to traverse and which should be avoided. The proposed solution enables the learning of multiple traversability models, each associated with a particular locomotion gait, a walking pattern of a multi-legged walking robot. We propose to address the simultaneous learning of the environment and traversability models by a decoupled approach. Thus, navigation waypoints are generated using the current spatial and traversability models to gain the information necessary to improve the particular model during the robot’s motion in the environment. From the set of possible waypoints, the decision on where to navigate next is made based on the solution of the generalized traveling salesman problem that allows taking into account a planning horizon longer than a single myopic decision. The proposed approach has been verified in simulated scenarios and experimental deployments with a real hexapod walking robot with two locomotion gaits, suitable for different terrains. Based on the achieved results, the proposed method exploits the online learned traversability models and further supports the selection of the most appropriate locomotion gait for the particular terrain types.

Chemoreception and chemotaxis of a three-sphere swimmer

The coupled problem of hydrodynamics and solute transport for the Najafi–Golestanian three-sphere swimmer is studied, with the Reynolds number set to zero and Péclet numbers (Pe) ranging from 0.06 to 60. The adopted method is the numerical simulation of the problem with a finite element code based upon the FEniCS library.For the swimmer executing the optimal locomotion gait, we report the Sherwood number as a function of Pe in homogeneous fluids and show that it is quite consistent with that corresponding to the well-known spherical squirmer model. It is also confirmed that little gain in solute flux is achieved by swimming, unless Pe is significantly larger than 10.We also consider the swimmer as a learning agent moving inside a fluid that has a concentration gradient. The outcomes of Q-learning processes show that learning locomotion (with the displacement as reward) is significantly easier than learning chemotaxis (with the increase of solute flux as reward). The chemotaxis learning problem, even at low Pe, has a varying environment that renders learning more difficult. Further, the learning difficulty increases severely with the Péclet number. The results demonstrate the challenges that natural and artificial swimmers need to overcome to migrate efficiently when exposed to chemical inhomogeneities.

A Multigait Continuous Flexible Snake Robot for Locomotion in Complex Terrain

This article presents a novel multigait continuous flexible snake mobile robot based on rotating, twisting, and untwisting of a flexible spring. The proposed robot combines the advantages of a twisted flexible helical spring and a string of rigid planar articulated circular shells into a unique wheel-less structure with 100 degrees of freedom per meter length. By adjusting the rotation direction and speed of two motors set on both ends of the constrained spring, the robot performs different motions. A kinematics model indicates multiple motions, i.e., wave-like, straightening, and curling motions can be realized through rotating, twisting, and untwisting of the helical spring, respectively. Through different motions, the robot achieves sidewinding gait, concertina gait, and side-pushing locomotion gait in open space, while lateral and vertical undulations in confined space. Locomotion experiments on floor, on sands, under water, in tubes, and other complex terrain are conducted, which indicates its potential for rescue and exploration missions in complex environments.

Efficient learning of robust quadruped bounding using pretrained neural networks

AbstractBounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles. The authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body movements. The authors first pretrained the neural network (NN) based on data from a robot operated by conventional model‐based controllers, and then further optimised the pretrained NN via deep reinforcement learning (DRL). In particular, the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity, which improved the bounding performance. The NN‐based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully. A variety of environments are presented both indoors and outdoors with the authors’ approach. The authors’ approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven terrain.The cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al., https://doi.org/10.1049/csy2.12062.

Design, Analysis, and Real-Time Simulation of a 3D Soft Robotic Snake.

Snakes are a remarkable source of inspiration for mobile search-and-rescue robots. Their unique slender body structure and multiple modes of locomotion are well-suited to movement in narrow passages and other difficult terrain. The design, manufacturing, modeling, and control techniques of soft robotics make it possible to imitate the structure, mechanical properties, and locomotion gaits of snakes, opening up new possibilities in robotics research. Building on our track record of contributions in this area, this article presents a soft robotic snake made of modules that can actively deform in three-dimensional (3D) and rigorously studies its performance under a range of conditions, including gait parameters, number of modules, and differences in the environment. A soft 3D-printed wave spring sheath is developed to support the robot modules, increasing the snake's performance in climbing steps threefold. Finally, we introduce a simulator and a numerical model to provide a real-time simulation of the soft robotic snake. With the help of the real-time simulator, it is possible to develop and test new locomotion gaits for the soft robotic snake within a short period of time, compared with experimental trial and error. As a result, the soft robotic snake presented in this article is able to locomote on different surfaces, perform different bioinspired and custom gaits, and climb over steps.

Impact of digital dermatitis on locomotion and gait traits of beef cattle.

Digital dermatitis (DD) is an infectious skin disease and a major cause of lameness that significantly impacts cattle productivity and welfare. However, DD does not always result in lameness and lameness scoring systems are not specific to hoof pathologies. Digital dermatitis detection protocols could be improved by including gait traits most related to DD. The aims of this study were to 1) determine the association between DD M-stage (“M” for Mortellaro), locomotion, and gait traits: arched back (AB), asymmetric gait (AG), head bobbing (HB), tracking up (TU), and reluctance to bear weight (WB), and 2) determine which traits are most associated with DD. Cattle (n = 480) from three feedlots were enrolled. Locomotion score (LS) and gait traits were assessed as cattle walked four strides along a dirt alleyway. Next, cattle were restrained in a chute, each hind foot lifted, and DD M-stage (absent, active, or chronic) determined. The association between presence of DD, LS, and gait traits were scored independently (n = 291). For both LS and gait the lowest score represents normal and the highest score severely altered. Digital dermatitis presence was associated with higher LS (P < 0.001). Odds ratios (ORs) for cattle with DD being lame or moderately to severely lame were 8.0 (P < 0.001) and 10.1 (P < 0.001) times more than cattle without lesions. Cattle with active lesions had the greatest odds of being lame (OR = 9.4; P < 0.001). Digital dermatitis presence was associated with all gait traits (P < 0.001), where AG (OR = 5.5; P < 0.001) and WB (OR = 5.8; P < 0.001) had the greatest OR for classifying cattle with DD as having altered gait. The OR for cattle with active lesions having altered gait was greatest for WB which was 6.0 (P < 0.001) times greater than cattle without lesions. The OR for cattle with chronic lesions having altered gait was greatest for AG being 6.5 (P < 0.001) times more than cattle without lesions. All gait traits had low sensitivity (Se) for detecting cattle with DD and varied from 6.7% to 55.8%. Locomotion score (Se 55.8%) and AG (Se 44.2%) were most predictive with positive predictive values of 76.6% and 74.3%, respectively. Specificity for all traits ranged from 94.1% for LS to 98.4% for WB with negative predictive values of 72.1% and 68.9%, respectively. In conclusion, LS, WB, and AG had the strongest association with cattle that had DD. Locomotion scoring that includes a focus on WB and AG is the best tool to detect DD in beef cattle.

Leg Shaping and Event-Driven Control of a Small-Scale, Low-DoF, Two-Mode Robot

Among small-scale mobile robots, multi-modal locomotion can help compensate for limited actuator capabilities. However, supporting multiple locomotion modes or gaits in small terrestrial robots typically requires complex designs with low locomotion efficiency. In this work, legged and rolling gaits are achieved by a 10~cm robot having just two degrees of freedom (DoF). This is acheived by leg shaping that facilitates whole body rolling and event-driven control that maintains motion using simple inertial sensor measurements. Speeds of approximately 0.4 and 2.2 body lengths per second are achieved in legged and rolling modes, respectively, with low cost of transport. The proposed design approach and control techniques may aid in design of further miniaturized robots reliant on transducers with small range-of-motion.

Enhancing Inference on Physiological and Kinematic Periodic Signals via Phase-Based Interpretability and Multi-Task Learning

Physiological and kinematic signals from humans are often used for monitoring health. Several processes of interest (e.g., cardiac and respiratory processes, and locomotion) demonstrate periodicity. Training models for inference on these signals (e.g., detection of anomalies, and extraction of biomarkers) require large amounts of data to capture their variability, which are not readily available. This hinders the performance of complex inference models. In this work, we introduce a methodology for improving inference on such signals by incorporating phase-based interpretability and other inference tasks into a multi-task framework applied to a generative model. For this purpose, we utilize phase information as a regularization term and as an input to the model and introduce an interpretable unit in a neural network, which imposes an interpretable structure on the model. This imposition helps us in the smooth generation of periodic signals that can aid in data augmentation tasks. We demonstrate the impact of our framework on improving the overall inference performance on ECG signals and inertial signals from gait locomotion.

Does the Heel’s Dissipative Energetic Behavior Affect Its Thermodynamic Responses During Walking?

Most of the terrestrial legged locomotion gaits, like human walking, necessitate energy dissipation upon ground collision. In humans, the heel mostly performs net-negative work during collisions, and it is currently unclear how it dissipates that energy. Based on the laws of thermodynamics, one possibility is that the net-negative collision work may be dissipated as heat. If supported, such a finding would inform the thermoregulation capacity of human feet, which may have implications for understanding foot complications and tissue damage. Here, we examined the correlation between energy dissipation and thermal responses by experimentally increasing the heel’s collisional forces. Twenty healthy young adults walked overground on force plates and for 10 min on a treadmill (both at 1.25 ms−1) while wearing a vest with three different levels of added mass (+0%, +15%, & +30% of their body mass). We estimated the heel’s work using a unified deformable segment analysis during overground walking. We measured the heel’s temperature immediately before and after each treadmill trial. We hypothesized that the heel’s temperature and net-negative work would increase when walking with added mass, and the temperature change is correlated with the increased net-negative work. We found that walking with +30% added mass significantly increased the heel’s temperature change by 0.72 ± 1.91 (p = 0.009) and the magnitude of net-negative work (extrapolated to 10 min of walking) by 326.94 ± 379.92 J (p = 0.005). However, we found no correlation between the heel’s net-negative work and temperature changes (p = 0.277). While this result refuted our second hypothesis, our findings likely demonstrate the heel’s dynamic thermoregulatory capacity. If all the negative work were dissipated as heat, we would expect excessive skin temperature elevation during prolonged walking, which may cause skin complications. Therefore, our results likely indicate that various heat dissipation mechanisms control the heel’s thermodynamic responses, which may protect the health and integrity of the surrounding tissue. Also, our results indicate that additional mechanical factors, besides energy dissipation, explain the heel’s temperature rise. Therefore, future experiments may explore alternative factors affecting thermodynamic responses, including mechanical (e.g., sound & shear-stress) and physiological mechanisms (e.g., sweating, local metabolic rate, & blood flow).

Gait switching and targeted navigation of microswimmers via deep reinforcement learning

Swimming microorganisms switch between locomotory gaits to enable complex navigation strategies such as run-and-tumble to explore their environments and search for specific targets. This ability of targeted navigation via adaptive gait-switching is particularly desirable for the development of smart artificial microswimmers that can perform complex biomedical tasks such as targeted drug delivery and microsurgery in an autonomous manner. Here we use a deep reinforcement learning approach to enable a model microswimmer to self-learn effective locomotory gaits for translation, rotation and combined motions. The Artificial Intelligence (AI) powered swimmer can switch between various locomotory gaits adaptively to navigate towards target locations. The multimodal navigation strategy is reminiscent of gait-switching behaviors adopted by swimming microorganisms. We show that the strategy advised by AI is robust to flow perturbations and versatile in enabling the swimmer to perform complex tasks such as path tracing without being explicitly programmed. Taken together, our results demonstrate the vast potential of these AI-powered swimmers for applications in unpredictable, complex fluid environments.

Bidirectional Locomotion of Soft Inchworm Crawler Using Dynamic Gaits.

Inchworm-styled locomotion is one of the simplest gaits for mobile robots, which enables easy actuation, effective movement, and strong adaptation in nature. However, an agile inchworm-like robot that realizes versatile locomotion usually requires effective friction force manipulation with a complicated actuation structure and control algorithm. In this study, we embody a friction force controller based on the deformation of the robot body, to realize bidirectional locomotion. Two kinds of differential friction forces are integrated into a beam-like soft robot body, and along with the cyclical actuation of the robot body, two locomotion gaits with opposite locomotion directions can be generated and controlled by the deformation process of the robot body, that is, the dynamic gaits. Based on these dynamic gaits, two kinds of locomotion control schemes, the amplitude-based control and the frequency-based control, are proposed, analyzed, and validated with both theoretical simulations and prototype experiments. The soft inchworm crawler achieves the versatile locomotion result via a simple system configuration and minimalist actuation input. This work is an example of using soft structure vibrations for challenging robotic tasks.

NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster.

Animal behavior emerges from an interaction between neural network dynamics, musculoskeletal properties and the physical environment. Accessing and understanding the interplay between these elements requires the development of integrative and morphologically realistic neuromechanical simulations. Here we present NeuroMechFly, a data-driven model of the widely studied organism, Drosophila melanogaster. NeuroMechFly combines four independent computational modules: a physics-based simulation environment, a biomechanical exoskeleton, muscle models and neural network controllers. To enable use cases, we first define the minimum degrees of freedom of the leg from real three-dimensional kinematic measurements during walking and grooming. Then, we show how, by replaying these behaviors in the simulator, one can predict otherwise unmeasured torques and contact forces. Finally, we leverage NeuroMechFly's full neuromechanical capacity to discover neural networks and muscle parameters that drive locomotor gaits optimized for speed and stability. Thus, NeuroMechFly can increase our understanding of how behaviors emerge from interactions between complex neuromechanical systems and their physical surroundings.