Gait Pattern Generation Research Articles

Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation

Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches have been proposed to synthesize the gait patterns for these robots; each approach exhibits different restrictions, advantages, and priorities. For the purpose of this document, we have classified gait pattern generators for multi-legged robots into three categories: precomputed, heuristic, and bio-inspired approaches. Precomputed approaches rely on a set of precalculated motion patterns obtained from geometric and/or kinematic models that are performed repeatedly whenever necessary and that cannot be modified on-the-fly to adapt to the terrain changes. On the other hand, heuristic and bio-inspired approaches offer on-line adaptability, but parameter-tuning and heading control can be difficult. In this document, we present the K3P algorithm, a real-time kinematic gait pattern generator conceived to command a legged robot. In contrast to other approaches, K3P enables the robot to adapt its gait to follow an arbitrary trajectory, at an arbitrary speed, over uneven terrain. No precomputed motions for the legs are required; instead, K3P modifies the motion of all mechanical joints to propel the body of the robot in the desired direction, maintaining a tripod stability at all times. In this paper, all the specific details of the aforementioned algorithm are presented, as well as different simulation results that validate its characteristics.

Individualized Three-Dimensional Gait Pattern Generator for Lower Limbs Rehabilitation Robots.

In the field of robotic gait rehabilitation, controlling robotic devices to follow specific human-like trajectories is often required. In recent years, various gait generator models have been proposed, providing customized gait patterns adjustable to a range of heights and gait speeds. However, these models were developed with a focus on gait rehabilitation devices designed to control the angular trajectories of the subject's joints, e.g. exoskeletons. Similar devices, e.g. end-effector robots, control the orientation and also the 3D position of the subject's joints and cannot easily implement these models. In this study, it is proposed a new individualized three-dimensional gait pattern generator for gait rehabilitation robots. The generator employs multi-variable regression models to predict the joint angular trajectories of the pelvis, hip, and ankle along the gait cycle. The 3D joints positions are then reconstructed by applying the predicted angular trajectories over a human model inspired on the inverted pendulum analogy using inverse kinematics. The generator's performance was statistically evaluated against real gait patterns from 42 participants walking at 8 different velocities. The predicted trajectories matched the measured ones with an average Root Mean Squared Error of 25.73 mm for all joints at all Cartesian axes, with better results between 3.3 - 5.4 km/h. Suggesting to be a good solution to be applied in end-effector gait robotic rehabilitation devices.

Learning hybrid locomotion skills-Learn to exploit residual actions and modulate model-based gait control.

This work has developed a hybrid framework that combines machine learning and control approaches for legged robots to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a model-based, full parametric closed-loop and analytical controller as the gait pattern generator. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel, and also generate compensatory actions for all joints, thus significantly augmenting the stability under unexpected perturbations. Seven Neural Network policies with different configurations were optimized to validate the effectiveness and the combined use of the modulation of the kernel parameters and the compensation for the arms and legs using residual actions. The results validated that modulating kernel parameters alongside the residual actions have improved the stability significantly. Furthermore, The performance of the proposed framework was evaluated across a set of challenging simulated scenarios, and demonstrated considerable improvements compared to the baseline in recovering from large external forces (up to 118%). Besides, regarding measurement noise and model inaccuracies, the robustness of the proposed framework has been assessed through simulations, which demonstrated the robustness in the presence of these uncertainties. Furthermore, the trained policies were validated across a set of unseen scenarios and showed the generalization to dynamic walking.

Intelligent proximal-policy-optimization-based decision-making system for humanoid robots

With the advancements in technology, robots have gradually replaced humans in different aspects. Allowing robots to handle multiple situations simultaneously and perform different actions depending on the situation has since become a critical topic. Currently, training a robot to perform a designated action is considered an easy task. However, when a robot is required to perform actions in different environments, both resetting and retraining are required, which are time-consuming and inefficient. Therefore, allowing robots to autonomously identify their environment can significantly reduce the time consumed. How to employ machine learning algorithms to achieve autonomous robot learning has formed a research trend in current studies. In this study, to solve the aforementioned problem, a proximal policy optimization algorithm was used to allow a robot to conduct self-training and select an optimal gait pattern to reach its destination successfully. Multiple basic gait patterns were selected, and information-maximizing generative adversarial nets were used to generate gait patterns and allow the robot to choose from numerous gait patterns while walking. The experimental results indicated that, after self-learning, the robot successfully made different choices depending on the situation, verifying this approach’s feasibility.

Various gait pattern generation and analysis of semi-passive quadruped walker with telescopic knee based on phase oscillator

Various gait pattern generation and analysis of semi-passive quadruped walker with telescopic knee based on phase oscillator

The History and Future of the WalkON Suit: A Powered Exoskeleton for People With Disabilities

In this article, the mechanical design concept, control strategy, and gait-trajectory-generation method are introduced for a powered exoskeleton, called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WalkON Suit</i> , which won the gold and bronze medals at Cybathlon 2020. The WalkON Suit was developed based on several distinct technologies, such as high-power actuation modules, robotic legs with parallel-elastic-actuation modules, gait-pattern generation, and the iterative-learning algorithm of human behavior, which made it powerful and unique.

Design of a Bio-Inspired Gait Phase Decoder Based on Temporal Convolution Network Architecture With Contralateral Surface Electromyography Toward Hip Prosthesis Control.

Inter-leg coordination is of great importance to guarantee the safety of the prostheses wearers, especially for the subjects at high amputation levels. The mainstream of current controllers for lower-limb prostheses is based on the next motion state estimation by the past motion signals at the prosthetic side, which lacks immediate responses and increases falling risks. A bio-inspired gait pattern generation architecture was proposed to provide a possible solution to the bilateral coordination issue. The artificial movement pattern generator (MPG) based on the temporal convolution network, fusing with the motion intention decoded from the surface electromyography (sEMG) measured at the impaired leg and the motion status from the kinematic modality of the prosthetic leg, can predict four sub gait phases. Experiment results suggested that the gait phase decoder exhibited a relatively high intra-subject consistency in the gait phase inference, adapted to various walking speeds with mean decoding accuracy ranging from 89.27 to 91.16% across subjects, and achieved an accuracy of 90.30% in estimating the gait phase of the prosthetic leg in the hip disarticulation amputee at the self-selected pace. With the proof of concept and the offline experiment results, the proposed architecture improves the walking coordination with prostheses for the amputees at hip level amputation.

Study on Gait Pattern Generation With Improved Hip Vertical Motion for Biped Robots

Study on Gait Pattern Generation With Improved Hip Vertical Motion for Biped Robots

Nonlinear model predictive control for robust bipedal locomotion: exploring angular momentum and CoM height changes

Human beings can utilize multiple balance strategies, e.g. step location adjustment and angular momentum adaptation, to maintain balance when walking under dynamic disturbances. In this work, we propose a novel Nonlinear Model Predictive Control (NMPC) framework for robust gait pattern generation, with the capabilities of step location adjustment, Center of Mass (CoM) height variation, and angular momentum adaptation. These features are realized by constraining the Zero Moment Point within the support polygon. By employing the nonlinear inverted pendulum plus flywheel model, the effects of upper-body rotation and vertical height motion are considered. As a result, the NMPC is formulated as a quadratically constrained quadratic program problem, which is solved fast by sequential quadratic programming. Consequently, robust walking patterns that exploit reactive stepping, body inclination, and CoM height variation are generated. Adaptable walking in multiple scenarios has been demonstrated through simulation studies.

Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking

Gait pattern generation has an important influence on the walking quality of biped robots. In most gait pattern generation methods, it is usually assumed that the torso keeps vertical during walking. It is very intuitive and simple. However, it may not be the most efficient. In this paper, we propose a gait pattern with torso pitch motion (TPM) during walking. We also present a gait pattern with torso keeping vertical (TKV) to study the effects of TPM on energy efficiency of biped robots. We define the cyclic gait of a five-link biped robot with several gait parameters. The gait parameters are determined by optimization. The optimization criterion is chosen to minimize the energy consumption per unit distance of the biped robot. Under this criterion, the optimal gait performances of TPM and TKV are compared over different step lengths and different gait periods. It is observed that (1) TPM saves more than 12% energy on average compared with TKV, and the main factor of energy-saving in TPM is the reduction of energy consumption of the swing knee in the double support phase and (2) the overall trend of torso motion is leaning forward in double support phase and leaning backward in single support phase, and the amplitude of the torso pitch motion increases as gait period or step length increases.

Gait pattern generation algorithm for lower-extremity rehabilitation–exoskeleton robot considering wearer’s condition

Various exoskeleton robots are being developed according to users’ conditions. Lower limb exoskeleton robots have attracted much attention for performing walking motions, a basic exercise for paralyzed patients. In this study, the walking environment of an exoskeleton robot wearer who walks using a crutch is identified as the foot position of the crutch support point and the support leg; therefore, the upcoming foothold location is calculated and sets as a control target. Rather than using preset pedestrian patterns, pedestrian patterns that can match the environment are created using the dynamic movement primitives machine learning technology. We evaluated the exoskeleton robot through an experiment with a healthy 29-year-old male. The experimental results showed that the exoskeleton robot was able to demonstrate the adaptive strides according to the wearer’s intention.

Torso pitch motion effects on walking gait for biped robots

Gait pattern generation has an important influence on the walking quality of biped robots. In most gait pattern generation method, it is usually assumed that the torso remains vertial during walking. It is very intuitive and simple. However, is the gait pattern of keeping the torso vertical the most efficient? This paper presents a gait pattern in which the torso has pitch motion during walking. We define the cyclic gait of a seven-link biped robot with multiple gait parameters. The gait parameters are determined by optimization. The optimization criterion is choosen to minimize the energy consumption per unit distance of the biped robot. In order to compare the energy consumption of the proposed gait pattern with the one of torso vertical gait pattern, we generate two sets of optimal gait with various walking step lengths and walking periods. The results show that the proposed gait pattern is more energy-efficiency than the torso vertical gait pattern.

Gait Pattern Generation for Bipod Robots based on Force Equal Distribution

Gait Pattern Generation for Bipod Robots based on Force Equal Distribution

Individualized Gait Generation for Rehabilitation Robots Based on Recurrent Neural Networks.

Individualized reference gait patterns for lower limb rehabilitation robots can greatly improve the effectiveness of rehabilitation. However, previous methods can only generate customized gait patterns at several fixed discrete walking speeds and generating gaits at continuously varying speeds and stride lengths remains unsolved. This work proposes an individualized gait pattern generation method based on a recurrent neural network (RNN), which is proficient in series modeling. We collected the largest gait data set of this kind, which consists of 4,425 gait patterns from 137 subjects. Using this data set, we trained an RNN to create a function mapping from body parameters and gait parameters to a gait pattern. The experimental results indicate that our model is able to generate gait patterns at continuously varying walking speeds and stride lengths while also reducing the errors in the ankle, knee, and hip measurements by 12.83%, 20.95%, and 28.25%, respectively, compared to previous state-of-the-art method.

Generation of diverse insect-like gait patterns using networks of coupled Rössler systems.

The generation of walking patterns is central to bio-inspired robotics and has been attained using methods encompassing diverse numerical as well as analog implementations. Here, we demonstrate the possibility of synthesizing viable gaits using a paradigmatic low-dimensional non-linear entity, namely, the Rössler system, as a dynamical unit. Through a minimalistic network wherein each instance is univocally associated with one leg, it is possible to readily reproduce the canonical gaits as well as generate new ones via changing the coupling scheme and the associated delays. Varying levels of irregularity can be introduced by rendering individual systems or the entire network chaotic. Moreover, through tailored mapping of the state variables to physical angles, adequate leg trajectories can be accessed directly from the coupled systems. The functionality of the resulting generator was confirmed in laboratory experiments by means of an instrumented six-legged ant-like robot. Owing to their simple form, the 18 coupled equations could be rapidly integrated on a bare-metal microcontroller, leading to the demonstration of real-time robot control navigating an arena using a brain-machine interface.

A Gait Pattern Generator for Closed-Loop Position Control of a Soft Walking Robot

This paper presents an approach to control the position of a gecko-inspired soft robot in Cartesian space. By formulating constraints under the assumption of constant curvature, the joint space of the robot is reduced in its dimension from nine to two. The remaining two generalized coordinates describe respectively the walking speed and the rotational speed of the robot and define the so-called velocity space. By means of simulations and experimental validation, the direct kinematics of the entire velocity space (mapping in Cartesian task space) is approximated by a bivariate polynomial. Based on this, an optimization problem is formulated that recursively generates the optimal references to reach a given target position in task space. Finally, we show in simulation and experiment that the robot can master arbitrary obstacle courses by making use of this gait pattern generator.

GPR and SPSO-CG based gait pattern generation for subject-specific training

GPR and SPSO-CG based gait pattern generation for subject-specific training

An experimental energy consumption comparison between trajectories generated by using the cart-table model and an optimization approach for the Bioloid robot

The aim of this article is to present a statistical comparison of the electric energy expenditure between two techniques for the generation of gait patterns in biped robots. The first one is to minimize the sum of the squared torques applied to the joints of the robot, and the second one is based on the cart-table model. For the experiments, we measured the energy delivered by the battery of the robot to the servomotors. We applied the two aforementioned methods for three velocities (0.5, 1.0, and 1.3 m/min). Additionally, each combination of method and velocity was performed by the robot 10 times. The energy expenditure for each method was compared by applying the Wilcoxon test. In all comparisons, the value of p was lower than 0.004, indicating that the differences were statistically significant. The optimization approach leads to a reduction in energy expenditure that ranged from 9.16 % to 13.35 %. The conclusion is that all the effort required to implement an approach that requires a complete dynamic model of the robot allows a significant reduction in energy consumption.

Fuzzy Dynamic Gait Pattern Generation for Real-Time Push Recovery Control of a Teen-Sized Humanoid Robot

A fuzzy dynamic gait pattern generator, which allows a teen-sized humanoid robot to generate, in real-time, a suitable gait pattern when it is hit by an unexpected force, is proposed in this paper. Conventional gait pattern generators usually utilize the ideal Zero Moment Point (ZMP) to plan the trajectory of the Center of Mass (CoM), along with a cycloid to generate steps. However, pre-planned gait patterns cannot deal with unexpected situations, especially instances when the robot experiences an unknown force. Therefore, we propose a dynamic gait pattern generator that leverages the Virtual Force Linear Inverted Pendulum Model (VFLIPM) to adjust the trajectory of the CoM, and which detects balance status by estimating the trajectory of the ZMP using eight high-precision load cell pressure sensors mounted onto the robot's soles. We integrate an accelerometer and the pressure sensors through a fuzzy controller to instantly respond to external forces and generate a suitable gait pattern. When the robot is pushed suddenly, it first adopts a pre-planned gait pattern to replace the current gait. At the same time, the fuzzy controller calculates the recovery gait, with appropriate strides and lean angles to absorb the impact. The proposed method is implemented on the teen-sized humanoid robot, David Junior II, for the Push Recovery event at RoboCup. David Junior II endured a hit with potential energy during walking, which is 1.3 times more robust than when standing.

Assessment of Gait Symmetry in Elderly Women with Low Bone Mineral Density Using a Portable Trunk Accelerometer: A Pilot Study.

BackgroundConvincing evidence regarding gait and balance function in elderly women with low bone mineral density (BMD) and/or osteoporosis is limited. In the present study, we aimed to compare the gait characteristics in elderly women with and without low BMD and to analyze plausible parameter(s) for predicting low BMD.Material/MethodsWe retrospectively analyzed prospectively collected data of 26 consecutive postmenopausal women aged >65 years who were admitted to our geriatric outpatient service. They were assigned to 2 groups, with (n=14) and without (n=12) low BMD based on T-score (<−1 standard deviation of the mean for healthy adults) of dual-energy X-ray absorptiometry. We compared gait parameters derived from a portable Tri-Axial trunk accelerometer that included cadence, walking speed, average acceleration, horizontal/vertical displacement ratio, and symmetry index of the Lissajous figure (LI) during a 5-meter walk test at comfortably fast speed between groups.ResultsWomen with low BMD tended to show increased LI compared with healthy subjects (P=0.04). No statistically significant differences were noted in cadence, walking speed, average acceleration, and ratio of horizontal and vertical displacement (P>0.05). Receiver operating characteristic curve analysis demonstrated that LI can predict low BMD in the femur neck with moderate accuracy (area under the curve=0.75, 95% confidence interval 0.55–0.95; P=0.031). The optimal cut-off value was 17%, with 67% specificity and 86% sensitivity.ConclusionsThese results suggest that elderly women with low BMD may walk with asymmetrical trunk movement, but they are able to generate gait patterns similar to healthy peers. The LI may provide valuable quantitative information for preventing fractures in subjects with osteoporosis.