Robotic Gait Research Articles

Fault-tolerant gait design for quadruped robots with one locked leg using the GF set theory

The fault-tolerant gait plays an important role in improving the reliability and prolonging the service life of legged robots. However, few fault-tolerant gaits are available for quadruped robots and the static stability margin in some gaits cannot avoid being zero. This paper designs a novel fault-tolerant gait for quadruped robots with one locked leg using the GF set theory. First, a quadruped robot with serial-parallel leg mechanism and its typical static gait are introduced. Then, the mobility of the robot with one locked leg in different stages is addressed using the GF set theory. The fault-tolerant gait pattern is developed by taking full advantage of the mobility. Further, the performances of the fault-tolerant gaits are analyzed to demonstrate its capability. Finally, simulations are conducted to validate the fault-tolerant gait and its performance. The results show that the fault-tolerant gait has the capability of omnidirectional walking and adapting to rough terrains while maintaining a nonzero static stability margin.

Stroke survivor perceptions of using an exoskeleton during acute gait rehabilitation

Robotic-assisted gait training (RAGT) devices allow intensive high repetition of the gait cycle in individuals with locomotor disability, with reduced therapist effort. In addition to usual rehabilitation, RAGT post-stroke improves the likelihood of regaining independent walking, with maximum efficacy identified in the acute and subacute phases of stroke. This study explores the usability and acceptance of RAGT among persons with stroke in an acute hospital setting and examines users’ perceptions of two different modes of robotic assistance provided during rehabilitation. A mixed-methods approach comprised semi-structed interviews of end-user perspectives of RAGT in an acute hospital setting following stroke and two 10-point Likert scales rating how comfortable and how natural robotic gait felt using different assistance modes. Content analysis of qualitative data was undertaken with results synthesised by common meaning units. Quantitative data were reported using summary statistics, with Spearmann’s correlation co-efficient examining the relationship between Likert scale ratings and measures of participants’ stroke related disability. Ten individuals (6 men; 4 women; mean age of 64.5. ± 13 years) were recruited in an acute hospital setting following admission with a stroke diagnosis. Content analysis of interview transcripts identified discussion units centring around positive aspects of how helpful the device was, negative aspects related to set-up time, weight of the device and multiple instructions delivered during use. Initially participants identified that the device could look intimidating, and they feared falling in the device but they subsequently identified the correct mindset for using the device is to trust the technology and not be afraid. Mean ratings for device comfort (7.94 ± 1.4) and how natural walking felt (7.05 ± 1.9) were favourable. Interestingly, a strong relationship was identified, whereby the higher the level of disability, the more natural participants rated walking in the device during maximal assistance mode (rho = 0.62; p = 0.138). This study suggests individuals in the early phases of stroke perceive RAGT to be acceptable and helpful in the main, with some associated negative aspects. Walking in the device was rated as comfortable and natural. Those with greater disability rated the assisted walking as more natural.

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.

Improving Ankle Muscle Recruitment via Plantar Pressure Biofeedback during Robot Resisted Gait Training in Cerebral Palsy.

Neurological impairment from stroke or cerebral palsy often presents with diminished ankle plantar flexor function during the propulsive phase of gait. This deficit often results in slow, energy-expensive walking patterns that limit community mobility. Robotic gait training interventions may prove effective in improving functional outcomes, including exoskeleton resistance used to provide targeted neuromuscular recruitment. However, these interventions to date have required regular verbal cues and coaching for proper plantar flexor engagement with resistance, particularly for pediatric applications. In this validation study, we sought to address the need for automating and improving the effectiveness of facilitating user engagement with robotic resistance. Specifically, our main goal was to compare changes in plantar flexor activity between walking with plantar flexor resistance alone vs plantar flexor resistance combined with plantar pressure biofeedback in individuals with cerebral palsy. We recruited 8 ambulatory adolescents with cerebral palsy between the ages of 11-18 years old to participate in this cross-sectional feasibility study. Supporting our hypothesis, we observed a 36 ± 36% and 46 ± 39% increase in mean and peak soleus activity, respectively, between resistance plus biofeedback vs resistance alone (both p < 0.05). Compared to other biofeedback sensing modalities like assessment of muscle activity via surface electrodes, integrating the plantar pressure-based system within the wearable robotic devices minimizes barriers to clinical implementation by reducing cost, complexity, and setup time. With these positive feasibility results, our future work will explore longer-term training effects of ankle resistance combined with plantar pressure biofeedback.

Effects of spinal structure on quadruped bounding gait

AbstractThis paper proposes a robot model with multiple spines and investigates its effects on the bounding gait of quadruped robot. Contrastive tests, carried out by adding an active driving joint (ADJ) and changing the number of passive driving joints, were divided between simulation and physical. The results reveal that the spinal structure (one ADJ and several passive driving joints) is closely related to the performance parameters. Increasing the number of passive driving joints can improve the velocity and energy efficiency. This structure may have significant guidance for designing a quadruped robot.

Robust Walking for Humanoid Robot Based on Divergent Component of Motion.

In order to perform various complex tasks in place of humans, humanoid robots should walk robustly in the presence of interference. In the paper, an improved model predictive control (MPC) method based on the divergent components of motion (DCM) is proposed. Firstly, the humanoid robot model is simplified to a finite-sized foot-pendulum model. Then, the gait of the humanoid robot in the single-support phase (SSP) and double-support phase (DSP) is planned based on DCM. The center of mass (CoM) of the robot will converge to the DCM, which simplifies the feedback control process. Finally, an MPC controller incorporating an extended Kalman filter (EKF) is proposed to realize the tracking of the desired DCM trajectory. By adjusting the step duration, the controller can compensate for CoM trajectory errors caused by disturbances. Simulation results show that—compared with the traditional method—the method we propose achieves improvements in both disturbed walking and uneven-terrain walking.

Toward a Data-Driven Template Model for Quadrupedal Locomotion

This work investigates a data-driven template model for trajectory planning of dynamic quadrupedal robots. Many state-of-the-art approaches involve using a reduced-order model, primarily due to computational tractability. The spirit of the trajectory planning approach in this work draws on recent advancements in the area of behavioral systems theory. Here, we aim to capitalize on the knowledge of well-known template models to construct a data-driven model, enabling us to obtain an information rich reduced-order model. In particular, this work considers input-output states similar to that of the single rigid body model and proceeds to develop a data-driven representation of the system, which is then used in a predictive control framework to plan a trajectory for quadrupeds. The optimal trajectory is passed to a low-level and nonlinear model-based controller to be tracked. Preliminary experimental results are provided to establish the efficacy of this hierarchical control approach for trotting and walking gaits of a high-dimensional quadrupedal robot on unknown terrains and in the presence of disturbances.

Analysis of the Gait Characteristics and Usability after Wearable Exoskeleton Robot Gait Training in Incomplete Spinal Cord Injury Patients with Industrial Accidents: A Preliminary Study

Analysis of the Gait Characteristics and Usability after Wearable Exoskeleton Robot Gait Training in Incomplete Spinal Cord Injury Patients with Industrial Accidents: A Preliminary Study

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.

Co-Design of the Morphology and Actuation of Soft Robots for Locomotion

Abstract In recent years, the field of soft robotics has received considerable attention due to its potential in increasing the safety of human-robot interaction. The design of soft robots possesses great challenges. For example, the longstanding challenge of co-design morphology and actuation makes designing them by hand a trial-and-error process. Earlier work presented by the authors proposes a computational design synthesis (CDS) method for the automated design of virtual, soft locomotion robot morphologies. This work extends the CDS method for morphologies with the automated co-design of actuation. Two methods are considered. In the first method, the actuation of designs is described by parametric actuation curves (PACs) that model feedforward actuation patterns. For every morphology in the design process, a set of PACs is optimized that assumes symmetric and cyclic gaits. The second method, soft actor-critic (SAC) reinforcement learning, removes this assumption as well as models feedback control for comparison. Adding PAC optimization to the CDS method is shown to improve the performance of the resulting designs and to achieve better results within less design iterations. SAC is, however, deemed less effective, due to the need for design specific problem tuning for each new morphology. The SAC experiments also show that the best found soft robot gaits are symmetric and cyclic, although this is not a constraint in the SAC problem formulation, thus verifying the assumptions made in the PAC formulation. To validate the search space modeled in the co-design CDS method, a state-of-the-art soft robot is replicated and compared.

Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution.

Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy-evolution overarching development-is observed for most multicellular life forms. When designing robots, however, this tenet lifts: It becomes-however natural-a design choice. We propose to inverse this temporal hierarchy and design a developmental process happening at the phylogenetic timescale. Over a classic evolutionary search aimed at finding good gaits for tentacle 2D robots, we add a developmental process over the robots' morphologies. Within a generation, the morphology of the robots does not change. But from one generation to the next, the morphology develops. Much like we become bigger, stronger, and heavier as we age, our robots are bigger, stronger, and heavier with each passing generation. Our robots start with baby morphologies, and a few thousand generations later, end-up with adult ones. We show that this produces better and qualitatively different gaits than an evolutionary search with only adult robots, and that it prevents premature convergence by fostering exploration. In addition, we validate our method on voxel lattice 3D robots from the literature and compare it to a recent evolutionary developmental approach. Our method is conceptually simple, and it can be effective on small or large populations of robots, and intrinsic to the robot and its morphology, not the task or environment. Furthermore, by recasting the evolutionary search as a learning process, these results can be viewed in the context of developmental learning robotics.

Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not?

I was encouraged by the recent article by Kuo et al. entitled “Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders” to write an opinion piece on the possible further development of stationary robot-assisted gait training research. Randomized clinical trials investigating stationary gait robots have not shown the superiority of these devices over comparable interventions regarding clinical effectiveness, and there are clinical practice guidelines that even recommend against their use. Nevertheless, these devices are still widely used, and our field needs to find ways to apply these devices more effectively. The authors of the article mentioned above feed different machine learning algorithms with patients’ data from the beginning of a robot-assisted gait training intervention using the robot Lokomat. The output of these algorithms allows predictions of the clinical outcome (i.e., functional ambulation categories) while the patients are still participating in the intervention. Such an analysis based on the collection of the device’s data could optimize the application of these devices. The article provides an example of how our field of research could make progress as we advance, and in this opinion piece, I would like to present my view on the prioritization of upcoming research on robot-assisted gait training. Furthermore, I briefly speculate on some drawbacks of randomized clinical trials in the field of robot-assisted gait training and how the quality and thus the effectiveness of robot-assisted gait training could potentially be improved based on the collection and analysis of clinical training data, a better patient selection and by giving greater weight to the motivational aspects for the participants.

Soft robotic exosuit augmented high intensity gait training on stroke survivors: a pilot study

BackgroundStroke is a leading cause of serious gait impairments and restoring walking ability is a major goal of physical therapy interventions. Soft robotic exosuits are portable, lightweight, and unobtrusive assistive devices designed to improve the mobility of post-stroke individuals through facilitation of more natural paretic limb function during walking training. However, it is unknown whether long-term gait training using soft robotic exosuits will clinically impact gait function and quality of movement post-stroke.ObjectiveThe objective of this pilot study was to examine the therapeutic effects of soft robotic exosuit-augmented gait training on clinical and biomechanical gait outcomes in chronic post-stroke individuals.MethodsFive post-stroke individuals received high intensity gait training augmented with a soft robotic exosuit, delivered in 18 sessions over 6–8 weeks. Performance based clinical outcomes and biomechanical gait quality parameters were measured at baseline, midpoint, and completion.ResultsClinically meaningful improvements were observed in walking speed (p < 0.05) and endurance (p < 0.01) together with other traditional gait related outcomes. The gait quality measures including hip (p < 0.01) and knee (p < 0.05) flexion/extension exhibited an increase in range of motion in a symmetric manner (p < 0.05). We also observed an increase in bilateral ankle angular velocities (p < 0.05), suggesting biomechanical improvements in walking function.ConclusionsThe results in this study offer preliminary evidence that a soft robotic exosuit can be a useful tool to augment high intensity gait training in a clinical setting. This study justifies more expanded research on soft exosuit technology with a larger post-stroke population for more reliable generalization.Trial registration This study is registered with ClinicalTrials.gov (ID: NCT04251091)

Parallel Deep Reinforcement Learning Method for Gait Control of Biped Robot

In this brief, a parallel Deep Deterministic Policy Gradient (DDPG) algorithm is presented for biped robot gait control. Biped robot gait control is a high-dimensional continuous problem. It is challenging to obtain a fast and stable gait. Traditional methods cannot fully utilize autonomous exploration capability of a biped robot. A multiple Actor-Critic (AC) network is established to expand the scope of exploration and improve training efficiency. For optimizing experience replay mechanism, an experience filtering unit is introduced, and a cosine similarity method is used to classify experience. Then, a Markov Decision Process (MDP) model based on knowledge and experience is designed to solve the problem of sparse rewards. Finally, experimental results show that the parallel DDPG algorithm can make the biped robot walk more quickly and stably, and the speed reaches 0.62 m/s.

로봇보조 보행훈련이 뇌졸중환자의 보행에 미치는 영향

Objective: This study is a comparative study conducted retrospectively to investigate the effect of an exoskeleton-controlled gait-assisting robot gait machine on the improvement of balance and gait ability in subacute stroke patients. Method: 128 patients who were hospitalized in Seoul-based Hospital A from January 2020 to December 2020 agreed to participate in the study and use personal information. Patients with a factor of less than 6 months of onset and berg balance scale (BBS) score of greater than or equal to severity (21 points or more) were measured balance and gait analysis test. In the patients, 30 patients in the general physical therapy group and 30 patients in the general physical therapy with the additional exoskeleton-controlled gait-assisting robot gait training group were selected and their balance and gait abilities were retrospectively compared and analyzed. Results: The RAGTG significantly improved compared to the CG in the A-P position and lateral sysmmetry evaluation in the balance ability, and in the cadence and stride length evaluation in the gait ability. Conclusion: Both groups showed statistically significant improvement in balance and gait and in particular, the group that received additional RAGTG improved more in A-P position, lateral symmetry, and cadence, stride length compared to the CG. This result had a positive effect on balance ability and gait ability.

Efficacy of Overground Robotic Gait Training on Balance in Stroke Survivors: A Systematic Review and Meta-Analysis.

Strokes often lead to a deficit in motor control that contributes to a reduced balance function. Impairments in the balance function severely limit the activities of daily living (ADL) in stroke survivors. The present systematic review and meta-analysis primarily aims to explore the efficacy of overground robot-assisted gait training (o-RAGT) on balance recovery in individuals with stroke. In addition, the efficacy on ADL is also investigated. This systematic review identified nine articles investigating the effects of o-RAGT on balance, four of which also assessed ADL. The results of the meta-analysis suggest that o-RAGT does not increase balance and ADL outcomes more than conventional therapy in individuals after stroke. The data should not be overestimated due to the low number of studies included in the meta-analysis and the wide confidence intervals. Subgroup analyses to investigate the influence of participant’s characteristics and training dosage were not performed due to lack of data availability. Further well-designed randomized controlled trials are needed to investigate the efficacy of o-RAGT on balance in individuals with stroke.

Study of Joint Symmetry in Gait Evolution for Quadrupedal Robots Using a Neural Network

Bio-inspired legged robots have the potential to traverse uneven terrains in a very efficient way. The effectiveness of the robot gait depends on the joint symmetry of the robot; variations in joint symmetries can result in different types of gaits suitable for different scenarios. In the literature, symmetric and asymmetric gaits have been synthesized for legged robots; however, no relation between the gait effectiveness and joint symmetry has been studied. In this research work, the effect of joint symmetry on the robot gait is studied. To test the suggested algorithm, spider-like robot morphology was created in a simulator. The simulation environment was set to a flat surface where the robots could be tested. The simulations were performed on the PyroSim software platform, a physics engine built on top of the Open Dynamics Engine. The quadrupedal robot was created with eight joints, and it is controlled using an artificial neural network. The artificial neural network was optimized using a genetic algorithm. Different robot symmetries were tested, i.e., diagonal joint symmetry, diagonal joint reverse symmetry, adjacent joint symmetry, adjacent joint reverse symmetry and random joint symmetry or joint asymmetry. The robot controllers for each joint symmetry were evolved for a set number of generations and the robot controllers were evaluated using a fitness function that we designed. Our results showed that symmetry in joint movement could help in generating optimal gaits for our test terrain, and joint symmetry produced gaits that were already present in nature. Moreover, our results also showed that certain joint symmetries tended to perform better than others in terms of stability, speed, and distance traveled.

Soleus H and Lower Limb Posterior Root Muscle Reflexes During Stepping After Incomplete SCI.

The goal of this study was to examine and compare the step cycle related modulation of the soleus H and posterior root muscle (PRM) reflexes in subjects with and without spinal cord injury. Ten subjects without neurological injury and fifteen subjects with spinal cord injury (SCI) underwent soleus H reflex and lower limb PRM reflex testing while standing and stepping in a robotic gait orthosis. Reflex amplitudes were evaluated during standing, mid stance and mid swing to determine if speed and/or injury altered step cycle related neuromodulation. H and PRM reflexes in the soleus underwent step cycle related modulation in injured and uninjured subjects though the degree of modulation differed between the two reflexes with the H reflex showing more step cycle related modulation. We found in the SCI group that both the soleus H and soleus PRM reflex amplitudes were higher relative to the non-injured group and modulated less during the step cycle. We also found that modulation of the soleus H reflex, but not soleus PRM reflex, correlated to the lower extremity motor scores in individuals with SCI. Our evidence suggests that the inability to provide appropriate step cycle related reflex modulation may be due to decreased supra-spinal regulation of motoneuron and spinal excitability and could be an indicator of the severity of injury as it relates to clinically measured lower extremity motor scores.

Sideways crab-walking is faster and more efficient than forward walking for a hexapod robot

Articulated legs enable the selection of robot gaits, including walking in different directions such as forward or sideways. For longer distances, the best gaits might maximize velocity or minimize the cost of transport (COT). While animals often have morphology suited to walking either forward (like insects) or sideways (like crabs), hexapod robots often default to forward walking. In this paper, we compare forward walking with crab-like sideways walking. To do this, a simple gait design method is introduced for determining forward and sideways gaits with equivalent body heights and step heights. Specifically, the frequency and stride lengths are tuned within reasonable constraints to find gaits that represent a robot’s performance potential in terms of speed and energy cost. Experiments are performed in both dynamic simulation in Webots and a laboratory environment with our 18 degree-of-freedom hexapod robot, Sebastian. With the common three joint leg design, the results show that sideways walking is overall better (75% greater walking speed and 40% lower COT). The performance of sideways walking was better on both hard floors and granular media (dry play sand). This supports development of future crab-like walking robots for future applications. In future work, this approach may be used to develop nominal gaits without extensive optimization, and to explore whether the advantages of sideways walking persist for other hexapod designs.

Cadaveric Gait Simulation of the Effect of Subtalar Arthrodesis on Total Ankle Replacement Kinematics.

Patients undergoing total ankle replacement (TAR) often have symptomatic adjacent joint arthritis and deformity. Subtalar arthrodesis can effectively address a degenerative and/or malaligned hindfoot, but there is concern that it places abnormal stresses on the TAR and adjacent joints of the foot, potentially leading to early TAR failure. This study hypothesized that ankle and talonavicular joint kinematics would be altered after subtalar arthrodesis in the setting of TAR. Thirteen mid-tibia cadaveric specimens with neutral alignment were tested in a robotic gait simulator. To simulate gait, each specimen was secured to a static mounting fixture about a 6-degree of freedom robotic platform, and a force plate moves relative to the stationary specimen based on standardized gait parameters. Specimens were tested sequentially in TAR and TAR with subtalar arthrodesis (TAR-STfuse). Kinematics and range of motion of the ankle and talonavicular joint were compared between TAR and TAR-STfuse. There were significant differences in kinematics and range of motion between TAR and TAR-STfuse groups. At the ankle joint, TAR-STfuse had less internal rotation in early-mid stance (P < .05), with decreased range of motion in the sagittal (-2.7 degrees, P = .008) and axial (-1.8 degrees, P = .002) planes in early stance, and increased range of motion in the coronal plane in middle (+1.2 degrees, P < .001) and late (+2.5 degrees, P = .012) stance. At the talonavicular joint, there were significant differences in axial and coronal kinematics in early and late stance (P < .05). Subtalar arthrodesis resulted in significantly decreased talonavicular range of motion in all planes in early and late stance (P < .003). In ankles implanted with the TAR design used in this study, kinematics of the ankle and talonavicular joint were found to be altered after subtalar arthrodesis. Aberrant motion may reflect altered contact mechanics at the prosthesis and increased stress at the bone-implant interface, and affect the progression of adjacent joint arthritis in the talonavicular joint. These findings may provide a correlate to clinical studies that have cited hindfoot arthrodesis as a risk factor for TAR failure.