Locomotion Control of a Hexapod Robot with Tripod Gait Using Central Pattern Generator Network

2016 ◽  
pp. 625-640
Author(s):  
Dong Bo Sheng ◽  
Trong Hai Nguyen ◽  
Huy Hung Nguyen ◽  
Hak Kyeong Kim ◽  
Bong Huan Jun ◽  
...  
Keyword(s):  
Central Pattern Generator ◽  
Pattern Generator ◽  
Hexapod Robot ◽  
Locomotion Control ◽  
Tripod Gait

scholarly journals The Design and Building of a Hexapod Robot with Biomimetic Legs

2019 ◽  
Vol 9 (14) ◽  
pp. 2792
Author(s):  
Hwang ◽  
Liu ◽  
Yang ◽  
Lin
Keyword(s):  
Central Pattern Generator ◽  
Distributed Control ◽  
Pattern Generator ◽  
Mathematical Function ◽  
Distributed Control System ◽  
Hexapod Robot ◽  
Computational System ◽  
Computational Burden ◽  
Tripod Gait ◽  
The Brain

A hexapod robot with biomimetic legs was built to implement a distributed control system, where a mechanism is proposed to serve as the central pattern generator and a computer to act as the brain-stem, cooperating with the central pattern generator through wireless communication. The proposed mechanism is composed of two modules, i.e., the tripod gait generator and the Theo Jansen Linkage. The tripod gait generator is a device that uses a single motor to generate a tripod gait, while the Theo Jansen Linkage rhythmically executes the legged motion. In a sense, we are trying to implement the locomotion of a robot by means of a hybrid computational system, including the mechanism part and the electronic processors part. The complex mathematical function of the foot movement is realized by the ensemble of links of the Theo Jansen Linkage, so as to alleviate the computational burden. Besides, the proposed design, based on non-collocated actuators, is intended to minimize the number of actuators while reducing the building cost of the robot.

A NOVEL CPG WITH PROPRIOCEPTION AND ITS APPLICATION ON THE LOCOMOTION CONTROL OF QUADRUPED ROBOT

2009 ◽  
Vol 06 (01) ◽  
pp. 33-46 ◽  
Author(s):  
LEI SUN ◽  
MAX Q.-H. MENG ◽  
SHUAI LI ◽  
HUAWEI LIANG ◽  
TAO MEI
Keyword(s):  
Central Pattern Generator ◽  
Sensory Information ◽  
Pattern Generator ◽  
Quadruped Robot ◽  
Locomotion Control ◽  
Working Status ◽  
Self Protection ◽  
Dynamic Connectivity

This paper proposes a novel central pattern generator (CPG) model with proprioceptive mechanism and the dynamic connectivity mechanism. It not only contains the sensory information of the environment but also contains the information of the actuators and automatically tunes the parameters of CPG corresponding to the actuators information and inner sensory information. The position of the joints linked directly with the output of CPG is introduced to the CPG to find its proprioceptive system, spontaneously making the robot realize the actuator working status, further changing the CPG output to fit the change and decrease the influence of the problematic joints or actuators on the robot being controlled. So the damage would be avoided and self-protection is implemented. Its application on the locomotion control of a quadruped robot demonstrates the effectiveness of the proposed approach.

scholarly journals Triple-layered central pattern generator-based controller for 3D locomotion control of snake-like robots

2017 ◽  
Vol 14 (6) ◽  
pp. 172988141773810 ◽  
Author(s):  
Guifang Qiao ◽  
Ying Zhang ◽  
Xiulan Wen ◽  
Zhong Wei ◽  
Junyu Cui
Keyword(s):  
Central Pattern Generator ◽  
Pattern Generator ◽  
Locomotion Control

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

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.

scholarly journals Adaptive Locomotion Control of a Hexapod Robot via Bio-Inspired Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Wenjuan Ouyang ◽  
Haozhen Chi ◽  
Jiangnan Pang ◽  
Wenyu Liang ◽  
Qinyuan Ren
Keyword(s):  
Control Systems ◽  
Pattern Generator ◽  
Hexapod Robot ◽  
Kinematics Analysis ◽  
Environment Change ◽  
Locomotion Control ◽  
Adaptive Locomotion ◽  
Control Approach ◽  
Locomotion Pattern ◽  
Two Parameters

In this paper, an adaptive locomotion control approach for a hexapod robot is proposed. Inspired from biological neuro control systems, a 3D two-layer artificial center pattern generator (CPG) network is adopted to generate the locomotion of the robot. The first layer of the CPG is responsible for generating several basic locomotion patterns and the functional configuration of this layer is determined through kinematics analysis. The second layer of the CPG controls the limb behavior of the robot to adapt to environment change in a specific locomotion pattern. To enable the adaptability of the limb behavior controller, a reinforcement learning (RL)-based approach is employed to tune the CPG parameters. Owing to symmetrical structure of the robot, only two parameters need to be learned iteratively. Thus, the proposed approach can be used in practice. Finally, both simulations and experiments are conducted to verify the effectiveness of the proposed control approach.

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