Biped Locomotion Control through a Biomimetic CPG-based Controller

Generic Mechanism for Waveform Regulation and Synchronization of Oscillators: An Application for Robot Behavior Diversity Generation

While nonlinear oscillators have been widely used for central pattern generators to produce basic rhythmic signals for robot locomotion control, methods to shape and regulate the signal waveform without changing the characteristics of the oscillators have not been fully investigated, especially during the network synchronization process. To illustrate the principle and process of waveform regulation of nonlinear oscillators in detail and ensure that the influence can be controlled, we present a method for waveform regulation and synchronization and analyze the relationship of different factors (e.g., initial conditions, network parameters, phase, and waveform regulation factors) in synchronization deviation. Then, the method is indicated to be effective in other commonly used nonlinear oscillators and neural oscillators. As an example application, a three-layer behavioral control architecture for a legged robot is constructed based on the proposed method. Modules for the body behavior, leg coordination, and single-leg adjustment are established to realize diverse robot behaviors. The effectiveness of the method is validated by a series of experiments. The results prove that the method performs well in terms of signal control accuracy, behavior pattern diversity, and smooth motion transition.

Adapting Highly-Dynamic Compliant Movements to Changing Environments: A Benchmark Comparison of Reflex- vs. CPG-Based Control Strategies

To control highly-dynamic compliant motions such as running or hopping, vertebrates rely on reflexes and Central Pattern Generators (CPGs) as core strategies. However, decoding how much each strategy contributes to the control and how they are adjusted under different conditions is still a major challenge. To help solve this question, the present paper provides a comprehensive comparison of reflexes, CPGs and a commonly used combination of the two applied to a biomimetic robot. It leverages recent findings indicating that in mammals both control principles act within a low-dimensional control submanifold. This substantially reduces the search space of parameters and enables the quantifiable comparison of the different control strategies. The chosen metrics are motion stability and energy efficiency, both key aspects for the evolution of the central nervous system. We find that neither for stability nor energy efficiency it is favorable to apply the state-of-the-art approach of a continuously feedback-adapted CPG. In both aspects, a pure reflex is more effective, but the pure CPG allows easy signal alteration when needed. Additionally, the hardware experiments clearly show that the shape of a control signal has a strong influence on energy efficiency, while previous research usually only focused on frequency alignment. Both findings suggest that currently used methods to combine the advantages of reflexes and CPGs can be improved. In future research, possible combinations of the control strategies should be reconsidered, specifically including the modulation of the control signal's shape. For this endeavor, the presented setup provides a valuable benchmark framework to enable the quantitative comparison of different bioinspired control principles.

Adaptive Walking Control for a Quadruped Robot on Irregular Terrain Using the Complex-Valued CPG Network

In this paper, we propose a CPG (central pattern generator) network control system using motor dynamics for the gait planning of a quadruped robot with a trot walking pattern to climb up and down a slope and turn back and follow the symmetry of route. The CPG unit model, which includes two DC motors model, has the ability to generate the periodic joint angle with complex-value parameters. Through plural feedback parameters, the CPG network can adjust the frequency and amplitude of an internal neuron model such as a robot meeting an irregular surface of a road. Using the stride length and frequency of robot joint angles, the distance of walking with a trot pattern can be calculated. In order to confirm the validity of the proposed control system, a quadruped robot is produced to implement the adaptive walking system.

A modular framework to generate robust biped locomotion: from planning to control

Biped robots are inherently unstable because of their complex kinematics as well as dynamics. Despite many research efforts in developing biped locomotion, the performance of biped locomotion is still far from the expectations. This paper proposes a model-based framework to generate stable biped locomotion. The core of this framework is an abstract dynamics model which is composed of three masses to consider the dynamics of stance leg, torso, and swing leg for minimizing the tracking problems. According to this dynamics model, we propose a modular walking reference trajectories planner which takes into account obstacles to plan all the references. Moreover, this dynamics model is used to formulate the controller as a Model Predictive Control (MPC) scheme which can consider some constraints in the states of the system, inputs, outputs, and also mixed input-output. The performance and the robustness of the proposed framework are validated by performing several numerical simulations using MATLAB. Moreover, the framework is deployed on a simulated torque-controlled humanoid to verify its performance and robustness. The simulation results show that the proposed framework is capable of generating biped locomotion robustly.

Error-Based Learning Mechanism for Fast Online Adaptation in Robot Motor Control

Existing state-of-the-art frequency adaptation mechanisms of central pattern generators (CPGs) for robot locomotion control typically rely on correlation-based learning. They do not account for the tracking error that may occur between the actual system motion and CPG output, leading to the loss of precision, unwanted movement, inefficient energy locomotion, and in the worst cases, motor collapse. To overcome this problem, we developed online error-based learning for frequency adaptation of CPGs. The learning mechanism used for error reduction is a novel modification of the dual learner (DL) called dual integral learner (DIL). Being able to reduce tracking and steady-state errors, it can also perform fast and stable learning, adapting the CPG frequency to match the performance of robotic systems. Control parameters of the DIL are more straightforward for complex systems (like walking robots), compared to traditional correlation-based learning, since they correspond to error reduction. Due to its embedded memory, the DIL can relearn quickly and recover spontaneously from the previously learned parameters. All these features are not covered by the existing frequency adaptation mechanisms. We integrated the DIL into a neural CPG-based motor control system for use on different legged robots with various morphologies for evaluation. The results show that: 1) the DIL does not require precise adjustment of its parameters to fit specific robots; and 2) the DIL can automatically and quickly adapt the CPG frequency to the robots such that the entire trajectory of the CPG can be precisely followed with very low tracking and steady-state errors. Consequently, the robots can perform the desired movements with more energy-efficient locomotion compared to the state-of-the-art correlation-based learning mechanism called frequency adaptation through fast dynamical coupling (AFDC). In the future, the proposed error-based learning mechanism for fast online adaptation in robot motor control can be used as a basis for trajectory optimization, universal controllers, and other studies concerning the change of intrinsic or extrinsic parameters.

Synchronization of Non-linear Oscillators for Neurobiologically Inspired Control on a Bionic Parallel Waist of Legged Robot

Synchronization of coupled non-linear oscillators inspired by a central pattern generator (CPG) can control the bionic robot and promote the coordination and diversity of locomotion. However, for a robot with a strong mutual coupled structure, such neurobiological control is still missing. In this contribution, we present a σ-Hopf harmonic oscillator with decoupled parameters to expand the solution space of the locomotion of the robot. Unlike the synchronization of original Hopf oscillators, which has been fully discussed, the asymmetric factor of σ-Hopf oscillator causes a deformation in oscillation waveform. Using the non-linear synchronization theory, we construct the transition state model of the synchronization process to analyze the asymmetrical distortion, period change and duty ratio inconsistency. Then a variable coupling strength is introduced to eliminate the waveform deformation and maintain the fast convergence rate. Finally, the approach is used for the locomotion control of a bionic parallel waist of legged robot, which is a highly coupled system. The effectiveness of the approach in both independent and synthesis behavior of four typical motion patterns are validated. The result proves the importance of controllability of the oscillation waveform and the instantaneous state of the synchronization, which benefits the transition and transformation of the locomotion and makes the coupling motion more flexible.

A Review on the Control of the Mechanical Properties of Ankle Foot Orthosis for Gait Assistance

In the past decade, advanced technologies in robotics have been explored to enhance the rehabilitation of post-stroke patients. Previous works have shown that gait assistance for post-stroke patients can be provided through the use of robotics technology in ancillary equipment, such as Ankle Foot Orthosis (AFO). An AFO is usually used to assist patients with spasticity or foot drop problems. There are several types of AFOs, depending on the flexibility of the joint, such as rigid, flexible rigid, and articulated AFOs. A rigid AFO has a fixed joint, and a flexible rigid AFO has a more flexible joint, while the articulated AFO has a freely rotating ankle joint, where the mechanical properties of the AFO are more controllable compared to the other two types of AFOs. This paper reviews the control of the mechanical properties of existing AFOs for gait assistance in post-stroke patients. Several aspects that affect the control of the mechanical properties of an AFO, such as the controller input, number of gait phases, controller output reference, and controller performance evaluation are discussed and compared. Thus, this paper will be of interest to AFO researchers or developers who would like to design their own AFOs with the most suitable mechanical properties based on their application. The controller input and the number of gait phases are discussed first. Then, the discussion moves forward to the methods of estimating the controller output reference, which is the main focus of this study. Based on the estimation method, the gait control strategies can be classified into subject-oriented estimations and phase-oriented estimations. Finally, suggestions for future studies are addressed, one of which is the application of the adaptive controller output reference to maximize the benefits of the AFO to users.

Effects of gait support in patients with spinocerebellar degeneration by a wearable robot based on synchronization control

BACKGROUND

Spinocerebellar degeneration (SCD) mainly manifests a cerebellar ataxic gait, leading to marked postural sway and the risk of falling down. Gait support using a wearable robot is expected to be an effective solution to maintaining the status quo and/or delaying symptom progression. The aim of this study was to evaluate the effects of gait support in patients with SCD by using a wearable robotic system called curara ^®; while undergoing walking tests.

METHODS

The curara system assists both the hip and knee joints and supports the wearer's rhythmic gait using a synchronization control based on a central pattern generator. The system reflects the wearer's intended motion in response to the gait support by detecting an interactive force that is generated from slight movements of the wearer. The degree of coordinated motion between the robot and the wearer can be adjusted by modifying the synchronization gain. In this study, we provided gait support using three high-gain conditions (A, B, C) to more easily follow the wearer's movement in each joint. The synchronization gains for both the hip and knee joints (i.e., C_h and C_k) were set at 0.5 for condition A and at 0.4 for condition B. Condition C had different gains for the hip and knee joints (i.e., C_h=0.4 and C_k=0.5). With the walking test, we assessed the effects of the gait support provided by the curara system on walking smoothness (measured using the harmonic ratio: HR) and spatiotemporal parameters (gait speed, stride length, cadence) in SCD patients (n=12). We compared the performance between the three high-gain conditions and without assistance from the robot.

RESULTS

Under condition C, the HRs in the anteroposterior, mediolateral, and vertical directions (HR-AP, HR-ML, and HR-V) were especially high compared with those under conditions A and B. The results of the statistical analyses using repeated measures analysis of variance followed by Tukey's test showed that gait support with condition C results in a statistically significant increase in the HR-AP (2.04 ±0.52; p=0.025) and HR-V (2.06 ±0.37; p=0.032) when compared with walking without assistance from the system. In contrast, the gait speed, stride length, and cadence under condition C were no major changes in most patients, compared with the patient's walking without assistance.

CONCLUSIONS

The significantly increased HR indicates that gait support under condition C achieved smoother walking than when not wearing the power unit of the system. Consequently, we suggest that gait support using the curara system has the potential to improve walking smoothness in patients with SCD.

Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control

Rhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and coordinate the produced rhythmic activity, CPGs require sensory feedback, i.e., external signals. Nonlinear oscillators are a standard model of CPGs and are used in various robotic applications. A special class of nonlinear oscillators are adaptive frequency oscillators (AFOs). AFOs are able to adapt their frequency toward the frequency of an external periodic signal and to keep this learned frequency once the external signal vanishes. AFOs have been successfully used, for instance, for resonant tuning of robotic locomotion control. However, the choice of parameters for a standard AFO is characterized by a trade-off between the speed of the adaptation and its precision and, additionally, is strongly dependent on the range of frequencies the AFO is confronted with. As a result, AFOs are typically tuned such that they require a comparably long time for their adaptation. To overcome the problem, here, we improve the standard AFO by introducing a novel adaptation mechanism based on dynamical coupling strengths. The dynamical adaptation mechanism enhances both the speed and precision of the frequency adaptation. In contrast to standard AFOs, in this system, the interplay of dynamics on short and long time scales enables fast as well as precise adaptation of the oscillator for a wide range of frequencies. Amongst others, a very natural implementation of this mechanism is in terms of neural networks. The proposed system enables robotic applications which require fast retuning of locomotion control in order to react to environmental changes or conditions.