SOFT COMPUTING CONTROLLED BASED DYNAMIC MODEL OF FOUR WHEELED MOBILE ROBOT

Main Article Content

K. SUGANYA
V. ARULMOZHI

Abstract

A Soft Computing based dynamic model of four-wheeled mobile robot is developed. The controller takes into account of wheel slippage and skid effects, forward-backward velocity with respect of two motors. In this paper the two motors are controlled by teleoperation scheme based adaptive neural network fuzzy inference system. The camera captures the robot moving to target point images that are sent to host computer that is master robot. Based on the master robot determines actions corresponding response may follow the sensor through teleoperation that is slave robot. This system stability and satisfactory performance is assured by Lyapunov function. It is supporting the mobile robot can track a reference trajectory without deviation. Finally, simulation result shows that our controller can tracks unexpected corners and maintain the stability.

Keywords:
Path planning control, wheeled mobile robot, Lyapunov function

Article Details

How to Cite
SUGANYA, K., & ARULMOZHI, V. (2019). SOFT COMPUTING CONTROLLED BASED DYNAMIC MODEL OF FOUR WHEELED MOBILE ROBOT. Asian Journal of Mathematics and Computer Research, 26(4), 230–242. Retrieved from http://www.ikprress.org/index.php/AJOMCOR/article/view/4805
Section
Original Research Article

References

Pawel Malysz, Sirouspour S. A task-space weighting matrix approach to semi-autonomous teleoperation control. In. Proce of the 2011 IEEE/RSJ. Int conf on Intelligent robots and syst. Sanfrancisco. 2011;645-652.

Suganya K, Arulmozhi V. Soft computing controller based path planning wheeled mobile robot. IEEE International Conference on Advances in Computer Applications (ICACA). 2016;230-234.

Suganya K, Arulmozhi V. Sliding mode control with soft computing based path planning wheeled mobile robot. International Conference on Advanced Computing and Communication Systems (ICACCS). 2017;1-5.

Jayasree KR, Jayasree PR, Vivek A. Dynamic target tracking using four wheeled mobile robot with optimal path planning technique. International Conference on Circuit, Power and Computing Technologies (ICCPCT). 2017;1–6.

Kecskés I, Balogh Z, Odry P. Modeling and fuzzy control of a four-wheeled mobile robot. IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics. 2012;205–210.

Wang M, Liu JNK. Fuzzy logic based real-time robot navigation in unknown environment with dead ends. Robot. Autonomous Syst. 2008;56:625-643.

Zhu A, Yang SX. Neurofuzzy-based approach to mobile robot navigation in unknown environments. IEEE trans. Syst. man, Cybern. C. 2007;4:610-621.

Ali MA, Mailah M. A simulation and experimental study on wheeled mobile robot path control in road roundabout environment. International Journal of Advanced Robotic Systems. 2019;1–17.

Erfani S, Jafari A, Hajiahmad A. Comparison of two data fusion methods for localization of wheeled mobile robot in farm conditions. Artificial Intelligence in Agriculture. 2019;1:48–55.

Ibrahim F, Abouelsoud AA, Ahmed MR, El Bab F, Tetsuya Ogata. Discontinuous stabilizing control of skid-steering mobile robot (SSMR). Journal of Intelligent & Robotic Systems. 2019;253-266.

Okada T, Mahmoud A, Botelho WT, Shimizu T. Trajectory estimation of a skid-steering mobile robot propelled by independently driven wheels. Robotica. 2012;30:123–132.

Arcara P, Melchiorri C. Control schemes for teleoperation with time delay: A comparative study. Robot, Autono, Syst. 2002;38:49-64.

Truong XT, Ngo TD. Toward socially aware robot navigation in dynamic and crowded environments: a proactive social motion model. IEEE Transactions on Automation Science and Engineering. 2017;4:1743–1760.

Tsui W, Masmoudi MS, Karray F, Masmoudi M. Soft-computing based embedded design of an intelligent wall/lane-following vehicle. IEEE/ASME Transactions on Mechatronics. 2008;125-135.