Support vector machine-based two-wheeled mobile robot motion control in a noisy environment

2008 ◽  
Vol 222 (7) ◽  
pp. 733-743 ◽  
Author(s):  
L Jiang ◽  
M Deng ◽  
A Inoue
Keyword(s):  
Support Vector Machine ◽  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Support Vector ◽  
Robot Motion ◽  
Control Parameters ◽  
Local Minima ◽  
Noisy Environment ◽  
Virtual Force ◽  
Control Scheme

In this paper, a support vector machine (SVM)-based control scheme of a two-wheeled mobile robot is proposed in a noisy environment. The noisy environment is defined as the measured data with uncertainty. The proposed control scheme can control the robot by consideration of local minima, where the controller is based on the Lyapunov function candidate and considers virtual force information. The SVM method is used for estimating the control parameters from the noisy environment. Four simulation results are presented to show the effectiveness of the proposed control scheme in the noisy environment, while the performance of a former method degrades significantly.

scholarly journals Support Vector Machine Based Mobile Robot Motion Control and Obstacle Avoidance

Robotics ◽  
2013 ◽  
pp. 85-111 ◽  
Author(s):  
Lihua Jiang ◽  
Mingcong Deng
Keyword(s):  
Mobile Robot ◽  
Potential Function ◽  
Motion Control ◽  
Plane Surface ◽  
Support Vector ◽  
Function Estimation ◽  
Local Minima ◽  
Noise Effect ◽  
Virtual Force ◽  
Measured Information

Considering the noise effect during the navigation of a two wheeled mobile robot, SVM and LS-SVM based control schemes are discussed under the measured information with uncertainty, and in the different environments. The noise effect is defined as uncertainty in the measured data. One of them focuses on using a potential function and constructing a plane surface for avoiding the local minima in the static environments, where the controller is based on Lyapunov function candidate. Another one addresses to use a potential function and to define a new detouring virtual force for escaping from the local minima in the dynamic environments. Stability of the control system can be guaranteed. However, the motion control of the mobile robot would be affected by the noise effect. The SVM and LS-SVM for function estimation are used for estimating the parameter in the proposed controllers. With the estimated parameter, the noise effect during the navigation of the mobile robot can be reduced.

PD-like controller with impedance for delayed bilateral teleoperation of mobile robots

Robotica ◽  
2015 ◽  
Vol 34 (9) ◽  
pp. 2151-2161 ◽  
Author(s):  
E. Slawiñski ◽  
S. García ◽  
L. Salinas ◽  
V. Mut
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Force Feedback ◽  
Robot Motion ◽  
Time Varying ◽  
Control Parameters ◽  
Bilateral Teleoperation ◽  
Teleoperation System ◽  
Control Scheme ◽  
The Stability

SUMMARYThis paper proposes a control scheme applied to the delayed bilateral teleoperation of mobile robots with force feedback in face of asymmetric and time-varying delays. The scheme is managed by a velocity PD-like control plus impedance and a force feedback based on damping and synchronization error. A fictitious force, depending on the robot motion and its environment, is used to avoid possible collisions. In addition, the stability of the system is analyzed from which simple conditions for the control parameters are established in order to assure stability. Finally, the performance of the delayed teleoperation system is shown through experiments where a human operator drives a mobile robot.

scholarly journals Support Vector Machine Based Mobile Robot Motion Control and Obstacle Avoidance

2013 ◽  
pp. 223-251
Author(s):  
Lihua Jiang ◽  
Mingcong Deng
Keyword(s):  
Mobile Robot ◽  
Potential Function ◽  
Motion Control ◽  
Plane Surface ◽  
Support Vector ◽  
Function Estimation ◽  
Local Minima ◽  
Noise Effect ◽  
Virtual Force ◽  
Measured Information

Considering the noise effect during the navigation of a two wheeled mobile robot, SVM and LS-SVM based control schemes are discussed under the measured information with uncertainty, and in the different environments. The noise effect is defined as uncertainty in the measured data. One of them focuses on using a potential function and constructing a plane surface for avoiding the local minima in the static environments, where the controller is based on Lyapunov function candidate. Another one addresses to use a potential function and to define a new detouring virtual force for escaping from the local minima in the dynamic environments. Stability of the control system can be guaranteed. However, the motion control of the mobile robot would be affected by the noise effect. The SVM and LS-SVM for function estimation are used for estimating the parameter in the proposed controllers. With the estimated parameter, the noise effect during the navigation of the mobile robot can be reduced.

Sensor Dynamic Modeling Based on LS-SVM and NGA

2008 ◽  
Vol 381-382 ◽  
pp. 439-442
Author(s):  
Qi Wang ◽  
Zhi Gang Feng ◽  
K. Shida
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Least Squares ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Support Vector ◽  
Local Minima ◽  
Highly Nonlinear ◽  
Sharing Function

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

scholarly journals Implementasi Electrinic Nose Dan Support Vector Machine Pada Aplikasi Olfactory Mobile Robot Dalam Mengenali Gas

2018 ◽  
Vol 7 (1) ◽  
pp. 69
Author(s):  
Rendyansyah - Rendyansyah ◽  
Aditya P.P. Prasetyo ◽  
Kemahyanto Exaudi
Keyword(s):  
Support Vector Machine ◽  
Mobile Robot ◽  
Support Vector

scholarly journals Adaptive control scheme based on the least squares support vector machine network

2011 ◽  
Vol 21 (4) ◽  
pp. 685-696 ◽  
Author(s):  
Tarek Mahmoud
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Adaptive Control ◽  
Least Squares ◽  
Nonlinear System Identification ◽  
Support Vector ◽  
Reference Trajectory ◽  
Control Scheme ◽  
Support Vector Machine Network ◽  
Adaptive Control Scheme

Adaptive control scheme based on the least squares support vector machine networkRecently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.

Wheeled Mobile Robot Tracking Control without Velocity Measurement

2013 ◽  
Vol 373-375 ◽  
pp. 231-237 ◽  
Author(s):  
Qiang Wang ◽  
Guang Tong ◽  
Xin Xing
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Velocity Measurement ◽  
Tracking Control ◽  
Wheeled Mobile Robot ◽  
Design Parameters ◽  
Wheeled Mobile Robots ◽  
Trajectory Tracking Control ◽  
Robot Tracking ◽  
Control Scheme

In this paper, a new robust trajectory tracking control scheme for wheeled mobile robots without velocity measurement is proposed. In the proposed controller, the velocity observer is used to estimate the velocity of wheeled mobile robot. The dynamics of wheeled mobile robot is considered to develop the controller. The proposed controller has the following features: i) The proposed controller has good robustness performance; ii) It is easy to improve tracking performance by setting only one design parameters.

Mobile Robot Traversability Prediction Based on Differential Evolution Support Vector Machine

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 257-264 ◽  
Author(s):  
Yan GUO ◽  
Aiguo SONG ◽  
Jiatong BAO ◽  
Jianwei CUI ◽  
Huatao ZHANG
Keyword(s):  
Support Vector Machine ◽  
Mobile Robot ◽  
Differential Evolution ◽  
Support Vector
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