Central Pattern Generator Control Research Articles

Stable Walking of a Biped Robot Controlled by Central Pattern Generator Using Multivariate Linear Mapping.

In order to improve the walking stability of a biped robot in multiple scenarios and reduce the complexity of the Central Pattern Generator (CPG) model, a new CPG walking controller based on multivariate linear mapping was proposed. At first, in order to establish a dynamics model, the lower limb mechanical structure of the biped robot was designed. According to the Lagrange and angular momentum conservation method, the hybrid dynamic model of the biped robot was established. The initial value of the robot's passive walking was found by means of Poincaré mapping and cell mapping methods. Then, a multivariate linear mapping model was established to form a new lightweight CPG model based on a Hopf oscillator. According to the parameter distribution of the new CPG model, a preliminary parameter-tuning idea was proposed. At last, the joint simulation of MATLAB and V-REP shows that the biped robot based on the new CPG control has a stable periodic gait in flat and uphill scenes. The proposed method could improve the stability and versatility of bipedal walking in various environments and can provide general CPG generation and a tuning method reference for robotics scholars.

Bionic Walking Control of a Biped Robot Based on CPG Using an Improved Particle Swarm Algorithm

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots.

Personalized assist-as-needed dressing assistance robot without human modeling using Rowat-Selverston CPG controller

Dressing is a critical component of Activity of Daily Living (ADLs). The development of assistive technology for dressing tasks is urgently needed from the viewpoint of privacy, for example, to prevent the wearer from being seen in the nude. Despite their importance, these technologies are underutilized in garment donning compared to other ADLs. The reasons for the lack of utilization of assistive technology are that the task is complex, as it involves handling individuals with large individual differences in height and symptoms and requires manipulation of flexible clothing. Addressing these variances presents a formidable academic challenge. Our previous study identified periodic patterns in human movements during robot-assisted dressing and noted individual variability in these patterns. Capitalizing on this finding, we propose a novel control strategy for a robot that adapts to individual needs during dressing, employing the ‘Assist-As-Needed’ (AAN) principle from physical therapy. The proposed control method uses Central Pattern Generators (CPG) that can be synchronously controlled in response to external forces, enabling model-free control without human modeling. We utilize the Rowat-Selverston CPG model, which is recognized for its adaptive response to human motions in human-robot interactions such as with handshaking robots. A simulator of the Rowat-Selverston CPG was created. The output of the CPG was confirmed by inputting the data set obtained in previous studies, and the parameters were adjusted. The CPG output was then applied to the recorded target joint trajectory for the dressing assistance. We prepared a replay of the default trajectory, a trajectory with simple harmonic motion applied, and a trajectory with CPG output applied, and confirmed whether individual adaptation according to the AAN was possible through subject experiments. The experimental results showed that the Rowat-Selverston CPG control method enables individual adaptive dressing assistance according to the AAN principle. This study summarizes these methods and results and contributes to the realization of an individually adaptive system that follows the AAN principle, especially in developing assistive technology.

Intelligent Control Strategy for Robotic Manta via CPG and Deep Reinforcement Learning

The robotic manta has attracted significant interest for its exceptional maneuverability, swimming efficiency, and stealthiness. However, achieving efficient autonomous swimming in complex underwater environments presents a significant challenge. To address this issue, this study integrates Deep Deterministic Policy Gradient (DDPG) with Central Pattern Generators (CPGs) and proposes a CPG-based DDPG control strategy. First, we designed a CPG control strategy that can more precisely mimic the swimming behavior of the manta. Then, we implemented the DDPG algorithm as a high-level controller that adaptively modifies the CPG’s control parameters based on the real-time state information of the robotic manta. This adjustment allows for the regulation of swimming modes to fulfill specific tasks. The proposed strategy underwent initial training and testing in a simulated environment before deployment on a robotic manta prototype for field trials. Both further simulation and experimental results validate the effectiveness and practicality of the proposed control strategy.

Structure and Gait Design of a Lunar Exploration Hexapod Robot Based on Central Pattern Generator Model

To address the challenges of sinking, imbalance, and complex control systems faced by hexapod robots walking on lunar soil, this study develops an umbrella-shaped foot lunar exploration hexapod robot. The overall structure of the robot is designed to mimic the body structure of insects. By incorporating a four-bar linkage mechanism to replace the commonly used naked joints in traditional hexapod robots, the robot reduces the number of degrees of freedom and simplifies control complexity. Additionally, an extension mechanism is added to the robot’s foot, unfolding into an umbrella shape to provide a larger support area, effectively addressing the issue of foot sinking instability during walking. This study adopts and simplifies the Central Pattern Generator (CPG) model to generate stable periodic control signals for the robot’s legs. Precise control of the extension mechanism’s unfolding period is achieved through mapping functions. A joint simulation platform using Solid Works and Matlab is established to analyze the stability of the robot’s walking. Finally, walking experiments are conducted on the prototype, confirming the smooth walking of the lunar exploration hexapod robot. The results indicate that the designed lunar exploration hexapod robot has a reasonable structure, excellent stability in motion, and the CPG control scheme is feasible.

CPG-Fuzzy Heading Control for a Hexapod Robot with Arc-Shaped Blade Legs

Based on the central pattern generator (CPG) and fuzzy controller, this paper proposes a heading control method for the directional motion for a new type of blade legged hexapod robot (BLHR). First, the modified Hopf oscillator is used to construct the CPG model of BLHR based on the limit cycle. Second, the fuzzy controller is applied to adjust the support angles of legs to change the heading of BLHR, thereby correcting the error between the actual and desired heading angle in real-time. Finally, the feasibility and effectiveness of the proposed CPG-Fuzzy control method is verified in Gazebo simulations and real-world experiments. This is the first attempt to combine CPG and fuzzy control in the context of hexapod robot. In comparison to existing control methods, the proposed CPG-Fuzzy controller can implement heading control of BLHR with better performance and value of further investigation.

Optimization of Biped Robot Walking Based on the Improved Particle Swarm Algorithm

The central pattern generator (CPG) is widely applied in biped gait generation, and the particle swarm optimization (PSO) algorithm is commonly used to solve optimization problems for CPG network controllers. However, the canonical PSO algorithms fail to balance exploration and exploitation, resulting in reduced optimization accuracy and stability, decreasing the control effectiveness of CPG controllers. In order to address this issue, a balanced PSO (BPSO) algorithm is proposed, which achieves better performance by balancing the algorithm’s exploration and exploitation capabilities. The BPSO algorithm’s solving process consists of two phases: the free exploration phase (FEP), which emphasizes exploration, and the attention exploration phase (AEP), which emphasizes exploitation. The proportion of each phase during optimization is controlled by an adjustable parameter. The BPSO algorithm is subjected to qualitative, numerical, convergence, and statistical analyses based on 13 benchmark functions. The experimental results from the benchmark functions demonstrate that the BPSO algorithm outperforms other comparison algorithms. Finally, a linear walking optimization method for humanoid robots based on the BPSO algorithm is established and tested in the Webots simulator. Comparative results with two other optimization methods show that the BPSO‐based optimization method enables the robot to achieve greater walking distance and smaller lateral deviation within a fixed number of iterations. Compared to the other two methods, walking distance increases by at least 60.98% and lateral deviation decreases by at least 1.96%. This research contributes to enhancing the locomotion capabilities of CPG‐controlled humanoid robots, enriching biped gait optimization theory and promoting the application of CPG gait control methods in humanoid robots.

CONCEPTUAL DESIGN AND BIO-INSPIRED CONTROL OF A NEW SURGICAL SOFT ROBOTIC GRIPPER

This paper describes the design and development of a novel soft robotic gripper for minimally invasive surgery (MIS) intended to remove foreign bodies from the patient's body by imitating human esophageal swallowing motions. The robotic gripper operates as follows: after locating and contacting the foreign body, the last segment of the gripper is expanding or contracting to match the size and catch the targeted object, then pushes forward, or bends it with its legs and starts to remove the object by a rhythmic peristaltic (periodically repeated) motion. This mode of the gripper’s operation allows removing bodies of different sizes from the human body without damaging the surrounding tissues, especially the blood vessels. Both the segments and legs of the gripper are made of dielectric elastomer actuators (DEAs) capable of large deformations under the influence of an external electric field (EEF). A central pattern generator (CPG)- based controller and a Rowat-Selverston type oscillator are selected to control both discrete and rhythmic motions based on electrical voltage – equivalent elastic strain relations of the orthotropic silicone elastomer obtained by the finite element analysis (FEA). The robotic gripper is modelled and studied by the ANSYS Workbench and Matlab/Simulink software. The performed computer modeling shows that due to the simple modular structure and CPG controller, the proposed robotic gripper is much faster, more adaptive and shows better response compared with its pneumatic actuated analogues.

Research on fusion control of sensor information and biological reflection based on CPG

To enhance the environmental adaptability of the quadruped robot and realize its stability in terrain with obstacles, this paper studies the fusion of sensor information feedback and bio-reflection control algorithm. First, a central pattern generator (CPG) that generates basal rhythmic movements is constructed. Secondly, a simplified foot end that can be used to identify the touchdown and obstacle information is designed. To enhance the stability of the quadruped robot during the flexor reflex process and avoid the problem of solidification in the reflex process, the foot touch signal was introduced into the flexor reflex algorithm, which enhanced the flexibility of the reflex process. To avoid secondary collisions with obstacles, the postswing reflection process is optimized, and active obstacle avoidance is realized after the obstacle is touched. Finally, the organic fusion of sensor information feedback, CPG control network, and bio-reflection control algorithm is realized. The simulation results show that the robot’s ability to overcome obstacles is improved, the number of reflections is less, and the reflection process is more stable.

Mathematical Modeling and Control Architecture of the Autonomous Lower Body of a Humanoid Robot

Joint controlling is n issues in humanoid robotics. Due to large number of joint present in the humanoid robot, nonlinearity causes the problem for a smooth walk. Processor has to do a lot of computation before the execution of the operation. Due to Serial and parallel linkages of Human manipulator structure, controlling is done with the mixed mode of the operation. The Central pattern generator (CPG) controller architecture were adopted for the such type of operation. CPG controller system interact with the environment and generates the task for the joint using the trajectory control. A simple master-slave control architecture was implemented for the controlling of the lower body of a humanoid robot trajectory. The nonlinearity was minimized by selecting the popper gear ratio. The stiffness and damping designed based on the natural frequency of the system. The controller design was optimized at damping factor 1. The structur

Trajectory tracking with external disturbance of bionic underwater robot based on CPG and robust model predictive control

Aiming at the 3-D trajectory tracking control problem of underwater bionic robots, a novel trajectory tracking control strategy based on robust model predictive control (RMPC) and central pattern generator (CPG) is designed for underwater bionic robots with external disturbances. Firstly, the overall mathematical model of the robot is established, and a discrete robust model predictive controller is designed for trajectory tracking control, and then its stability is proved. Secondly, according to the mechanical analysis of the pectoral fin and the caudal fin, the transformation functions of the pectoral fin and the caudal fin is designed, and the lower CPG motion controller and the upper RMPC trajectory tracking controller are combined to establish an RMPC-CPG controller. It not only improves the coordination and smoothness of the motion of the bionic robot during the trajectory tracking process, but also incorporates many constraints in the motion process into the rolling optimization, so that the control input meets the actual drive constraints and avoids the problem of drive oversaturation. Finally, the simulation experiment is designed to verify the tracking performance of the bionic underwater robot under external disturbance. Simulation results demonstrate the effectiveness of the controller and its robustness to external disturbances.

A CPG-based gait planning and motion performance analysis for quadruped robot

PurposeTo 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).Design/methodology/approachFirst, 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.FindingsThe 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.Originality/valueThe 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.

Motion coordination control of planar 5R parallel quadruped robot based on SCPL-CPG

The parallel leg of the quadruped robot has good structural stiffness, accurate movement, and strong bearing capacity, but it is complicated to control. To solve this problem, a series connection of parallel legs (SCPL) was proposed, as well as a control strategy combined with the central pattern generator (CPG). With the planar 5R parallel leg as the research object, the SCPL analysis method was used to analyze the leg structure. The topology of CPG network was built with the Hopf oscillator as the unit model, and the CPG was the core to model the robot control system. By continuously adjusting the parameters in the CPG control system and changing the connection weight, and the smooth transition between gaits was realized. The simulation results show that the SCPL analysis method can be effectively used in the analysis of parallel legs, and the control system can realize the smooth transition between gaits, which verifies the feasibility and effectiveness of the proposed control strategy.

Behavioral evidence for nested central pattern generator control of Drosophila grooming.

Central pattern generators (CPGs) are neurons or neural circuits that produce periodic output without requiring patterned input. More complex behaviors can be assembled from simpler subroutines, and nested CPGs have been proposed to coordinate their repetitive elements, organizing control over different time scales. Here, we use behavioral experiments to establish that Drosophila grooming may be controlled by nested CPGs. On a short time scale (5-7 Hz, ~ 200 ms/movement), flies clean with periodic leg sweeps and rubs. More surprisingly, transitions between bouts of head sweeping and leg rubbing are also periodic on a longer time scale (0.3-0.6 Hz, ~2 s/bout). We examine grooming at a range of temperatures to show that the frequencies of both oscillations increase-a hallmark of CPG control-and also that rhythms at the two time scales increase at the same rate, indicating that the nested CPGs may be linked. This relationship holds when sensory drive is held constant using optogenetic activation, but oscillations can decouple in spontaneously grooming flies, showing that alternative control modes are possible. Loss of sensory feedback does not disrupt periodicity but slow down the longer time scale alternation. Nested CPGs simplify the generation of complex but repetitive behaviors, and identifying them in Drosophila grooming presents an opportunity to map the neural circuits that constitute them.

CPG-Based Hierarchical Locomotion Control for Modular Quadrupedal Robots Using Deep Reinforcement Learning

Modular robots have the potential for an unmatched ability to perform versatile and robust locomotion. However, designing effective and adaptive locomotion controllers for modular robots is challenging, resulting in a number of model-based methods that typically require various forms of prior knowledge. Deep reinforcement learning (DRL) provides a promising model-free approach for locomotion control by trial-and-error. However, current DRL methods often require extensive interaction data, hindering many possible applications. In this letter, a novel two-level hierarchical locomotion framework for modular quadrupedal robots is proposed. The approach combines a low-level central pattern generator (CPG)-based controller with a high-level neural network to learn a variety of locomotion tasks using DRL. The low-level CPG controller is pre-optimized to generate stable rhythmic walking gaits, while the high-level network is trained to modulate the CPG parameters for achieving task goals based on high-dimensional inputs, including the robot states and user commands. The proposed approach is employed on a simulated modular quadruped. With a limited amount of prior knowledge, the proposed method is demonstrated to be capable of learning a variety of locomotion skills such as velocity tracking, path following, and navigating to a target. Simulation results show that the proposed method can achieve higher sample efficiency than the model-free DRL method and are substantially more robust than the baseline methods to external disturbances and irregular terrain.

Fish can save energy via proprioceptive sensing

Fish have evolved diverse and robust locomotive strategies to swim efficiently in complex fluid environments. However, we know little, if anything, about how these strategies can be achieved. Although most studies suggest that fish rely on the lateral line system to sense local flow and optimise body undulation, recent work has shown that fish are still able to gain benefits from the local flow even with the lateral line impaired. In this paper, we hypothesise that fish can save energy by extracting vortices shed from their neighbours using only simple proprioceptive sensing with the caudal fin. We tested this hypothesis on both computational and robotic fish by synthesising a central pattern generator (CPG) with feedback, proprioceptive sensing, and reinforcement learning. The CPG controller adjusts the body undulation after receiving feedback from the proprioceptive sensing signal, decoded via reinforcement learning. In our study, we consider potential proprioceptive sensing inputs to consist of low-dimensional signals (e.g. perceived forces) detected from the flow. With simulations on a computational robot and experiments on a robotic fish swimming in unknown dynamic flows, we show that the simple proprioceptive sensing is sufficient to optimise the body undulation to save energy, without any input from the lateral line. Our results reveal a new sensory-motor mechanism in schooling fish and shed new light on the strategy of control for robotic fish swimming in complex flows with high efficiency.

Research and experiments on electromagnetic-driven multi-joint bionic fish

AbstractBased on the characteristics of high-frequency swing during fast swimming of fish, this paper designs a bionic fish-driven joint based on electromagnetic drive to achieve high-frequency swing. Aiming at the characteristic parameters of high-frequency swing control, the Fourier transform is used to separate the characteristic parameters and then compared the driving accuracy of the joints in open-loop and closed-loop. The comparison results show that the closed-loop control is performed after Fourier transform. Under the same driving conditions, the closed-loop control method can improve the joint driving accuracy. Then a bionic fish robot composed of three joints is designed according to this method and Kane method is used to model it dynamically and combined with the central pattern generator control method to complete model simulation and related experiments. The experimental results show that the bionic fish prototype can swim faster under high-frequency swing under electromagnetically driven joints.

Effects of Elastic Joints on Performances of a Close-Chained Rod Rolling Robot

In rolling experiments, the performances of spider-like robot are limited greatly by its motors’ driving ability; meanwhile, the ground reaction forces are so great that they damaged the rods. In this paper, we solve above problems both mechanically and by control. Firstly, we design the parameters of the central pattern generator (CPG) network based on the kinematics of the robot to enable a smooth rolling trajectory. And we also analyze the kinematic rolling and dynamic rolling briefly. Secondly, we add torsion springs to the passive joints of the spider-like robot aiming to make use of its energy storage capacity to compensate the insufficient torque. The simulation results show that the optimized CPG control parameters can reduce the fluctuation of the mass center and the ground reaction forces. The torsion spring can reduce the peak torque requirements of the actuated joints by 50%.

Reproducing Five Motor Behaviors in a Salamander Robot With Virtual Muscles and a Distributed CPG Controller Regulated by Drive Signals and Proprioceptive Feedback.

Diverse locomotor behaviors emerge from the interactions between the spinal central pattern generator (CPG), descending brain signals and sensory feedback. Salamander motor behaviors include swimming, struggling, forward underwater stepping, and forward and backward terrestrial stepping. Electromyographic and kinematic recordings of the trunk show that each of these five behaviors is characterized by specific patterns of muscle activation and body curvature. Electrophysiological recordings in isolated spinal cords show even more diverse patterns of activity. Using numerical modeling and robotics, we explored the mechanisms through which descending brain signals and proprioceptive feedback could take advantage of the flexibility of the spinal CPG to generate different motor patterns. Adapting a previous CPG model based on abstract oscillators, we propose a model that reproduces the features of spinal cord recordings: the diversity of motor patterns, the correlation between phase lags and cycle frequencies, and the spontaneous switches between slow and fast rhythms. The five salamander behaviors were reproduced by connecting the CPG model to a mechanical simulation of the salamander with virtual muscles and local proprioceptive feedback. The main results were validated on a robot. A distributed controller was used to obtain the fast control loops necessary for implementing the virtual muscles. The distributed control is demonstrated in an experiment where the robot splits into multiple functional parts. The five salamander behaviors were emulated by regulating the CPG with two descending drives. Reproducing the kinematics of backward stepping and struggling however required stronger muscle contractions. The passive oscillations observed in the salamander's tail during forward underwater stepping could be reproduced using a third descending drive of zero to the tail oscillators. This reduced the drag on the body in our hydrodynamic simulation. We explored the effect of local proprioceptive feedback during swimming and forward terrestrial stepping. We found that feedback could replace or reduce the need for different drives in both cases. It also reduced the variability of intersegmental phase lags toward values appropriate for locomotion. Our work suggests that different motor behaviors do not require different CPG circuits: a single circuit can produce various behaviors when modulated by descending drive and sensory feedback.

Local CPG Self Growing Network Model with Multiple Physical Properties

Compared with traditional control methods, the advantage of CPG (Central Pattern Generator) network control is that it can significantly reduce the size of the control variable without losing the complexity of its motion mode output. Therefore, it has been widely used in the motion control of robots. To date, the research into CPG network has been polarized: one direction has focused on the function of CPG control rather than biological rationality, which leads to the poor functional adaptability of the control network and means that the control network can only be used under fixed conditions and cannot adapt to new control requirements. This is because, when there are new control requirements, it is difficult to develop a control network with poor biological rationality into a new, qualified network based on previous research; instead, it must be explored again from the basic link. The other direction has focused on the rationality of biology instead of the function of CPG control, which means that the form of the control network is only similar to a real neural network, without practical use. In this paper, we propose some physical characteristics (including axon resistance, capacitance, length and diameter, etc.) that can determine the corresponding parameters of the control model to combine the growth process and the function of the CPG control network. Universal gravitation is used to achieve the targeted guidance of axon growth, Brownian random motion is used to simulate the random turning of axon self-growth, and the signal of a single neuron is established by the Rall Cable Model that simplifies the axon membrane potential distribution. The transfer model, which makes the key parameters of the CPG control network—the delay time constant and the connection weight between the synapses—correspond to the axon length and axon diameter in the growth model and the growth and development of the neuron processes and control functions are combined. By coordinating the growth and development process and control function of neurons, we aim to realize the control function of the CPG network as much as possible under the conditions of biological reality. In this way, the complexity of the control model we develop will be close to that of a biological neural network, and the control network will have more control functions. Finally, the effectiveness of the established CPG self-growth control network is verified through the experiments of the simulation prototype and experimental prototype.