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This paper aims to use the Matsuoka’s neural oscillators as the basic units of central pattern generator (CPG), and to offer a new CPG architecture consisting of a dual neural CPG of circular three links responsible for oscillator phase adjustment, to which an external neural oscillator is added, which is responsible for oscillator amplitude adjustment, to control foot depth to balance itself when treading on an obstacle.

It is equipped with a triaxial accelerometer and a triaxial gyroscope to obtain a real-time robot attitude, and to disintegrate the foot tilt in each direction as feedback signals to CPG to restore the robot’ horizontal attitude on an uneven terrain. The CPG controller is a distributed control method, with each foot controller consisting of a group of reciprocally coupling neural oscillators and sensors to generate different locomotion by different coupling patterns.

The experiment results indicated that the gait design method succeeded in enabling a steady hexapod walking on a rugged terrain, the mode of response is such that adjustments can only be made when the tilt occurs.

The overall control mechanism uses individual foot tilts as the feedback signal input to the neural oscillators to change the amplitude and compare against the reference oscillators of fixed amplitude to generate the foot height reference signals that can balance the body, and then convert the control signals, through a trajectory generator, to foot trajectories from which the actual rotation angle of servo motors can be obtained through inverse kinematics to achieve the effect of restoring the balance when traveling.

The controller design based on the bionic CPG model has the ability to restore its balance when its body tilts. In addition to the model’s ability to control locomotion, from the response waveforms of this experiment, it can also be noticed that it can control the foot depth to balance itself when treading on an obstacle, and it can adapt to a changing environment. When the obstacle is removed, the robot can quickly regain its balance.

This paper aims to construct a central pattern generator (CPG) network that comprises coupled nonlinear oscillators to implement diversified locomotion gaits of robot AmphiHex-I. With the gaits, AmphiHex-I will have a strong locomotion ability in an amphibious environment, which is motivated by a novel public health application to detect the amphibious snail, Oncomelania hupensis, the snail intermediate host of Schistosoma japonicum, as an amphibious robot-based tool for schistosomiasis surveillance and response in the future.

First, the basis neural network was built by adopting six Hopf nonlinear oscillators which corresponded to six legs. Then, the correlation between the self-excited harmonic output signals generated from CPGs and various gaits was established. In view of requirements on its field application, the authors added a telecontrol system and an on-board battery to support the real-life remote control and a high-definition camera and a global positioning system module to acquire images and position information. Finally, the authors conducted the testing experiments on several tasks, e.g. detecting the distribution of Oncomelania hupensis snails.

The results demonstrate that the CPG is effective in controlling the robot’s diversified locomotion gaits. In addition, the robot is capable of fulfilling several testing tasks in the experiments.

The research provides a method based on CPG to control a hexapod robot with multiple motion patterns, which can effectively overcome the difficulty of motion control simply by changing certain mathematical parameters of a nonlinear equation, such as frequency, phase difference and offset angle, so as to realize the gait transitions. Also, using such a robot to probe the distribution of snails offers another way to tackle this laborious job, especially in some odious terrains, which will hence broaden the application of AmphiHex-I to vector surveillance in the fields of public health.

The purpose of this paper is to generate a virtual knee angle reference to be followed by a knee prosthesis control, using an adaptive central pattern generator (CPG). Also, to study the feasibility of this approach to implement a continuous control strategy on the prosthesis.

A CPG based on amplitude controlled phase oscillators (ACPOs) to track the current percentage of gait cycle on the prosthesis is proposed. Then, the virtual knee angle reference is generated along gait cycle, by interpolation with the corresponding angle of a sound knee. The structure and coupling of the CPG, as well as the control strategy are presented.

The coupling of the CPG with real gait on the prosthesis was proven, regardless of gait speed. Also, it was found that the maximum knee angle reached during walking is proportional to gait speed. Finally, generation of virtual knee angle reference to be followed by a prosthesis is demonstrated.

As only one event detected along gait cycle was used to update the CPG phase, the response to gait speed changes might be slow. Updating the CPG with more events remains for a future work.

The coupling of the CPG with real gait on the prosthesis results in a continuous gait cycle tracker, useful for any control strategy to be applied.

It is the first time a bio‐inspired concept as CPGs is applied to the prosthetic field. This could mean the beginning of a new era of cybernetic prostheses, which reproduce the lost limb and also the control functions of it.

This paper aims to develop a robofish with oscillating pectoral fins, and control it to mimic the bionic prototype by central pattern generators (CPGs).

First, the oscillation characteristics of the cownose ray were analyzed quantitatively. Second, a robofish with multi-joint pectoral fins was developed according to the bionic morphology and kinematics. Third, the improved phase oscillator was established, which contains a spatial asymmetric coefficient and a temporal asymmetric coefficient. Moreover, the CPG network is created to mimic the cownose ray and accomplish three-dimensional (3D) motions. Finally, the experiments were done to test the authors ' works.

The results demonstrate that the CPGs is effective to control the robofish to imitate the cownose ray realistically. In addition, the robofish is able to accomplish 3D motions of high maneuverability, and change among different swimming modes quickly and smoothly.

The research provides the method to develop a robofish from both 3D morphology and kinematics. The motion analysis and CPG control make sure that the robofish has the features of high maneuverability and camouflage. It is useful for military underwater applications and underwater detections in narrow environments. Second, this work lays the foundation for the autonomous 3D control. Moreover, the robotic fish can be taken as a scientific tool for the fluid bionics research.

The purpose of this paper is to model a motor region named the mesencephalic locomotors region (MLR) which is located in the end part of the brain and first part of the spinal cord. This model will be used for a Nao soccer player humanoid robot. It consists of three main parts: High Level Decision Unit (HLDU), MLR‐Learner and the CPG layer. The authors focus on a special type of decision making named curvilinear walking.

The authors' model is based on stimulation of some programmable central pattern generators (PCPGs) to generate curvilinear bipedal walking patterns. PCPGs are made from adaptive Hopfs oscillators. High level decision, i.e. curvilinear bipedal walking, will be formulated as a policy gradient learning problem over some free parameters of the robot CPG controller.

The paper provides a basic model for generating different types of motions in humanoid robots using only simple stimulation of a CPG layer. A suitable and fast curvilinear walk has been achieved on a Nao humanoid robot, which is similar to human ordinary walking. This model can be extended and used in other types of humanoid.

The authors' work is limited to a special type of biped locomotion. Different types of other motions are encouraged to be tested and evaluated by this model.

The paper introduces a bio‐inspired model of skill learning for humanoid robots. It is used for curvilinear bipedal walking pattern, which is a beneficial movement in soccer‐playing Nao robots in Robocup competitions.

The paper uses a new biological motor concept in artificial humanoid robots, which is the mesencephalic locomotor region.

This paper aims to imitate a cownose ray to develop a fish robot with paired flexible multi-fin-ray oscillating pectoral fins (OPFs) and control it to accomplish vivid stable 3-D motions using central pattern generators (CPGs) and fuzzy algorithm.

The cownose ray’s asymmetric sine-like oscillations were analyzed. Then a cownose-ray-like fish robot named Robo-ray was developed, which has paired flexible multi-fin-ray OPFs to actively control the fin shape and two tail fins to control the depth. To solve the problem of coordinated control for multi-degree-of-freedom Robo-ray, CPGs were adopted. An improved phase oscillator as a CPG unit with controlled amplitude, phase lag, smooth frequency transition and asymmetric oscillation characteristic was established. Furthermore, the CPG-fuzzy algorithm was developed for vivid stable 3-D motions. The open-loop speed control, the closed-loop control of depth and yaw were established.

The kinematic comparisons indicate that Robo-ray imitates the cownose ray realistically. The experimental results of closed-loop are obtained that the depth error of Robo-ray is less than ±100 mm and the course error is less than ±3°. Furthermore, the comprehensive experiments demonstrate that Robo-ray has high mobility, stability and robustness.

This research makes the fish robot with OPF propulsion closer to practical applications in complex underwater environment, for instance, ocean explorations, water quality monitoring and stealth military reconnaissance. In addition, Robo-ray can be taken as a scientific tool for better understanding of the hydrodynamics of OPF batoid.

The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters.

The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities.

The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot.

Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

The purpose of this paper is to introduce a motion control method for WFF-AmphiRobot, which can effectively realize the flexible motion of the robot on land, underwater and in the transition zone between land and water.

Based on the dynamics model, the authors selected the appropriate state variables to construct the state space model of the robot and estimated the feedback state of the robot through the maximum a posteriori probability estimation. The nonlinear predictive model controller of the robot is constructed by local linearization of the model to perform closed-loop control on the overall motion of the robot. For the control problem of the terminal trajectory, using the neural rhythmic movement theory in bionics to construct a robot central pattern generator (CPG) for real-time generation of terminal trajectory.

In this paper, the motion state of WFF-AmphiRobot is estimated, and a model-based overall motion controller for the robot and an end-effector controller based on neural rhythm control are constructed. The effectiveness of the controller and motion control algorithm is verified by simulation and physical prototype motion experiments on land and underwater, and the robot can ideally complete the desired behavior.

The paper designed a controller for WFF-AmphiRobot. First, when constructing the robot state estimator in this paper, the robot dynamics model is introduced as the a priori estimation model, and the error compensation of the a priori model is performed by the method of maximum a posteriori probability estimation, which improves the accuracy of the state estimator. Second, for the underwater oscillation motion characteristics of the flipper, the Hopf oscillator is used as the basis, and the flipper fluctuation equation is modified and improved by the CPG signal is adapted to the flipper oscillation demand. The controller effectively controls the position error and heading angle error within the desired range during the movement of the WFF-AmphiRobot.

This study aims to establish a bio-inspired controller for realizing the bounding gait of a quadruped robot system presented in this paper.

The bio-inspired controller is divided into three levels to mimic the biological patterns of animals. First, the high-level sub-controller is equivalent to the cerebellum, which could plan and control the motion of animals. Second, the effect of the middle-level sub-controller corresponds to the central nervous system. The central pattern generators in the spine generate the stable and cyclic signals as the fundamental rhythm for periodic motion of the leg and spine joints. Third, the low-level sub-controller is equal to the end effector, which adopts the simple proportional-derivative (PD) control to realize the specific motion trajectory of the legs and spine.

Combined with the stability criterion presented previously and the delayed feedback control method, the bounding gait of the cheetah virtual prototype could be actuated and stabilized by the bio-inspired controller. Moreover, the bio-inspired controller is applied to realize the bounding gait of an SQBot, which is a quadruped robot with a spine joint. Meanwhile, the validity and practicability of the bio-inspired controller for the control of quadruped robot have been verified against different forward velocities.

The bio-inspired controller and bionic quadruped robot system are instructive for the designing and actuating of the real quadruped robot.

To achieve stable gait planning and enhance the motion performance of quadruped robot, this paper aims to propose a motion control strategy based on central pattern generator (CPG) and back-propagation neural network (BPNN).

First, the Kuramoto phase oscillator is used to construct the CPG network model, and a piecewise continuous phase difference matrix is designed to optimize the duty cycle of walk gait, so as to realize the gait planning and smooth switching. Second, the mapper between CPG output and joint drive is established based on BP neural network, so that the quadruped robot based on CPG control has better foot trajectory to enhance the motion performance. Finally, to obtain better mapping effect, an evaluation function is resigned to evaluate the proximity between the actual foot trajectory and the ideal foot trajectory. Genetic algorithm and particle swarm optimization are used to optimize the initial weights and thresholds of BPNN to obtain more accurate foot trajectory.

The method provides a solution for the smooth gait switching and foot trajectory of the robot. The quintic polynomial trajectory is selected to testify the validity and practicability of the method through simulation and prototype experiment.

The paper solved the incorrect duty cycle under the walk gait of CPG network constructed by Kuramoto phase oscillator, and made the robot have a better foot trajectory by mapper to enhance its motion performance.