Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

October 9, 2024


Pages


DOI

Article

Nonlinear Modeling Analysis of Stabilization Behavior of Robotic Gait Control System Based on Image Processing Techniques

Check for updates


Authors

Dawang Shen Affiliation:
Maoming Polytechnic, Maoming, Guangdong, 525000, China.
and Hui Zhang Affiliation:
Maoming Polytechnic, Maoming, Guangdong, 525000, China.


Abstract

Existing robot gait control methods have problems such as high gait energy consumption and difficulty in generating optimal gait strategies. This paper designs a robot gait automatic control system based on the X86 platform and robot sensor interface. The main board of the robot controller is designed based on CISC, which ensures high stability and anti-interference capabilities. The information provided by ultrasonic sensors and infrared sensors is used to adjust and execute the robot’s trajectory, action sequence, and gait. To extract the target in the gait image, the frame difference algorithm is employed, and the phase and amplitude factors are collected after the Radon and Fourier-Mellin transform to identify gait characteristics for robot gait recognition. The robot’s two-dimensional spatial dynamics model is constructed continuously, and its dynamics equations are derived. The residual fusion technique is used to combine image data with sensor data. A system for stabilizing gait control has been designed. The tracking error rate of the robot is not more than ±10% in general, and a variety of gait patterns can be used to cross the obstacles, which verifies the effectiveness of the designed system for realizing the gait control and performance of the robot.


Keywords

Robot, Gait control, Sensors, Dynamics modeling, Radon transformation, 68P30


Citation

Shen, D. & Zhang, H. (2024). Nonlinear modeling analysis of stabilization behavior of robotic gait control system based on image processing techniques. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-2884
0 Total citations
0.00 FWCI
0 Recent citations
(2 years)
20 References
Open Access Yes
View full metrics

Published by: Engineering Journals

Engineering Journals Logo