Vision-Based Line-Following Control of a Two-Wheel Self-Balancing Robot

Hsu, Chun‐Fei; Su, Chien-Ting; Kao, Wei-Fu; Lee, Bore-Kuen

doi:10.1109/icmlc.2018.8526952

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…1) in two environments for dynamic modeling of technical systems: Simulink and SimInTech. A spider-robot is described by a vector of phase coordinates [25][26][27][28][29][30][31][32][33][34]:…”

Section: Creating Modelsmentioning

confidence: 99%

Mathematical Model of a Robot-spider for Group Control Synthesis: Derivation and Validation

Kravchenko,

Efremov,

Zhilenkov

et al. 2023

Journal of Robotics and Control

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A six-legged spider robot is a complex object from the point of view of the problem of synthesizing a system for controlling its movement. To synthesize an advanced control system for such a robot, which must solve non-trivial problems of overcoming obstacles, functioning under conditions of external disturbances, etc., we first solve the problem of synthesizing an information model of the object, on the basis of which its control system will subsequently be built.The paper compares two methods for synthesizing the information model of a six-legged spider-robot. In the first method, an information model is automatically synthesized from a CAD model of a spider-robot in a MATLAB-based graphical programming environment Simulink. In the second method, the information model is synthesized in the environment of dynamic modeling of technical systems SimInTech on the basis of a system of differential equations in the Cauchy form. Control loops and external influences are added to the information models synthesized in each of the modeling environments. The study showed that each of the resulting models has both its own individual advantages and disadvantages. They are mainly related to taking into account the mutual influence of various blocks of models on each other. It is shown that, in the end, the two models complement each other and make it possible to obtain an advanced basis for further synthesis of the motion control system.The results obtained in this work make it possible to use information models as a basis for the development of a control system for a physical model of a six-legged spider-robot, printed on a 3D printer and assembled on the basis of the Arduino hardware platform.

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Section: Creating Modelsmentioning

confidence: 99%

Mathematical Model of a Robot-spider for Group Control Synthesis: Derivation and Validation

Kravchenko,

Efremov,

Zhilenkov

et al. 2023

Journal of Robotics and Control

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show abstract

“…In addition, they consume low power and can be placed in small spaces [9]. However, several studies [10][11][12] have shown that the use of cameras can be an alternative for line detection by capturing images and describing the ground environment while sending them to an image processing system to detect the line. However, in the last decades, authors have experimented with new techniques such as color segmentation, edge detection, or more advanced methods with convolutional neural networks to identify and separate the background line and allow more accurate tracking [13].…”

Section: Introductionmentioning

confidence: 99%

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

Cesar Minaya,

Rosero,

Zambrano

et al. 2024

JAMRIS

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The paper presents an approach for controlling a line-following robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following Robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following Robot on a running track with different configurations is then evaluated. The results show that the line-following Robot responded more efficiently with an artificial neural network control algorithm than a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the Robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following Robot to adapt to environmental conditions and overcome obstacles on the track more effectively.

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“…It also has low power and good performance [3]. DC motors have been widely applied in various applications, such as in mobile robots [4] [5][6] [7], mini conveyors [8], and electric vehicles [9].…”

Section: Introductionmentioning

confidence: 99%

Improved LQR Control Using PSO Optimization and Kalman Filter Estimator

et al. 2022

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The electric motor is a device that converts electrical power into mechanical power. The DC motor has some advantages compared to the AC motor on its easier way to control the speed or position and wide adjustable range. The alternative solution for a control method with simple, easy to design, and can be implemented in a multi-input multi-output system is the integral state feedback such as LQR. The weakness of LQR control is that there is no exact method to determine the best value of matrix Q and R used to calculate the state feedback gain. Therefore, it was chosen by trial (manually tuned). This paper proposed an improved LQR control using PSO optimization and the Kalman filter estimator. The PSO is used to find optimal Q and R value while the Kalman filter is used to reduce the number of sensors used to measure the system state. Both performance and energy consumption were compared by using IAE and total energy consumption. The simulation results showed that the proposed method is superior in performance and energy compared to manually tuned LQR. The hardware implementation proves that the proposed method has the smallest IAE, whereas the smallest energy is manually tuned LQR. The proposed method can reduce the IAE by 11.28% with 1% higher energy compared to manually tuned LQR.

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Vision-Based Line-Following Control of a Two-Wheel Self-Balancing Robot

Cited by 11 publications

References 17 publications

Mathematical Model of a Robot-spider for Group Control Synthesis: Derivation and Validation

Mathematical Model of a Robot-spider for Group Control Synthesis: Derivation and Validation

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

Improved LQR Control Using PSO Optimization and Kalman Filter Estimator

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