A Cyclic Inhibitory Central Pattern Generator Control Method Integrated with Mechanical Oscillators for Snake Robots

Synchronous control is a fundamental and significant problem for controlling a multi-joint robot. In this article, by applying two coupled Rayleigh oscillators as the referred central pattern generator models for the two joints of a two-degrees-of-freedom robot, the central pattern generator–based coupling control method is proposed for controlling the two-degrees-of-freedom robot’s joints. On these bases, the co-simulation model of the two-degrees-of-freedom robot with the proposed central pattern generator–based coupling control method is established via ADAMS and MATLAB/Simulink, and the performance of the central pattern generator–based coupling control method on synchronizing two motions of two-degrees-of-freedom robot’s joints is numerically simulated. Furthermore, the experimental setup of a two-degrees-of-freedom robot is established based on the real-time simulations system via the proposed central pattern generator–based coupling control method. And experiments are carried out on the established setup. Simulations and experimental results show that the phase of the controlled two-degrees-of-freedom robot’s joints is mutual locked to other, and their motion pattern can be adjusted through the coupling parameter in the central pattern generator–based coupling control method. In conclusion, the proposed central pattern generator–based coupling control method can control the two-degrees-of-freedom robot’s joints to produce the coordinated motions and adjust the rhythmic pattern of the two-degrees-of-freedom robot.

Download Full-text

Gait Control of Hexapod Robot Based on Field-Programmable Gate Array and Central Pattern Generator

Journal Européen des Systèmes Automatisés ◽

10.18280/jesa.530619 ◽

2020 ◽

Vol 53 (6) ◽

pp. 931-937

Author(s):

Tianbo Qiao

Keyword(s):

Central Pattern Generator ◽

Field Programmable Gate Array ◽

Control Method ◽

Pattern Generator ◽

Hexapod Robot ◽

Gait Control ◽

Field Programmable ◽

Physical Prototype ◽

Hardware Description ◽

Gate Array

This paper attempts to improve the terrain adaptability of hexapod robot through gait control. Firstly, the multi-leg coupling in the tripodal gait of the hexapod robot was modeled by Hopf oscillator. Then, annular central pattern generator (CPG) was adopted to simulate the leg movements of hexapod robot between signals. Furthermore, a physical prototype was designed for the gait control test on field-programmable gate array (FPGA), and the algorithm of the rhythmic output of the model was programmed in Verilog, a hardware description language. Finally, the effectiveness of our gait control method was verified through the simulation on Xilinx. The results show that the phase difference of the CPG network remained stable; the designed hexapod robot moved at about 5.15cm/s stably in a tripodal gait, and outperformed wheeled and tracked robots in terrain adaptation. The research findings lay a solid basis for the design of all-terrain multi-leg robots.

Download Full-text

Walking quality guaranteed central pattern generator control method

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406213488854 ◽

2013 ◽

Vol 228 (3) ◽

pp. 569-579

Author(s):

Jiaqi Zhang ◽

Xiaolei Han ◽

Xueying Han

Keyword(s):

Central Pattern Generator ◽

Control Method ◽

Central Pattern Generators ◽

Pattern Generator ◽

Walking Robots ◽

High Quality ◽

Traditional Methods ◽

Walking Gait ◽

Pattern Generators ◽

Periodic Inputs

Creating effective locomotion for a legged robot is a challenging task. Central pattern generators have been widely used to control robot locomotion. However, one significant disadvantage of the central pattern generator method is its inability to design high-quality walks because it only produces sine or quasi-sine signals for motor control as compared to most cases in which the expected control signals are more advanced. Control accuracy is therefore diminished when traditional methods are replaced by central pattern generators resulting in unaesthetically pleasing walking robots. In this paper, we present a set of solutions, based on testings of Sony’s four-legged robotic dog (AIBO), which produces the same walking quality as traditional methods. First, we designed a method based on both evolution and learning to optimize the walking gait. Second, a central pattern generator model was put forth to enabled AIBO to learn from arbitrary periodic inputs, which resulted in the replication of the optimized gait to ensure high-quality walking. Lastly, an accelerator sensor feedback was introduced so that AIBO could detect uphill and downhill terrains and change its gait according to the surrounding environment. Simulations were performed to verify this method.

Download Full-text

A Feedback-Based CPG Control Method for a Single-Legged Hopping Robot

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.912-914.944 ◽

2014 ◽

Vol 912-914 ◽

pp. 944-947

Author(s):

Ting Ting Wang ◽

Fu Sheng Zha ◽

Peng Fei Wang

Keyword(s):

Neural Networks ◽

Central Pattern Generator ◽

Control Mechanism ◽

Control Method ◽

Pattern Generator ◽

Robot Motion ◽

Environment Changes ◽

Hopping Robot ◽

The Neural Networks ◽

Simulation Results

Biological CPG (central pattern generator) is responsible for the generation of animals' rhythmic motion such as hopping and running. In this paper, a novel feedback-based CPG control method is proposed, in which output of the CPG network is totally activated and contained by sensory feedbacks. The network responses to environment changes quickly with multi-feedbacks included, and has strong adaption to disturbance and non-flat terrain. This control method is applied to the controlling of a single-legged hopping robot to show the effectiveness of the approach. The simulation results prove that this control mechanism closely couples environment and robot motion, and dynamically stable hopping emerges from feedbacks contained in the neural networks.

Download Full-text