Mobile Robot Navigation Using Planning Algorithm and Sliding Mode Control in a Cluttered Environment

Islem Reguii; Imen Hassani; Chokri Rekik

doi:10.18196/jrc.v3i2.13765

Authors

Islem Reguii Control and Energy Management Laboratory (CEM Lab), University of Sfax, National Engineering School of Sfax
Imen Hassani Control and Energy Management Laboratory (CEM Lab), University of Sfax, National Engineering School of Sfax
Chokri Rekik Control & Energy Management Laboratory (CEM), University of Sfax, Sfax Engineering School, BP W, 1173-3038 Sfax, Tunisia.

DOI:

https://doi.org/10.18196/jrc.v3i2.13765

Keywords:

Mobile Robot, Planning Algorithm, Navigation, Obstacle Avoidance, Sliding Mode

Abstract

The research contribution of the present work is to solve the path planning and path tracking problems in static and dynamic environments. A new Planning Navigation Algorithm Technique is developed in order to solve the problem of navigation with obstacle avoidance. The basic idea of this algorithm searches for a safe path for navigation. First, this algorithm is focused to identify an optimal collision-free route to a spatially defined objective. Then, in each displacement, the developed algorithm handles to maximize the distance between the obstacles and minimize the distance to the goal. This is to obtain the optimal trajectory for navigation. On the other side, a sliding mode controller is adopted to solve the tracking trajectory task. The basic idea of this control system is to allow the robot mobile to track the desired trajectory with minimum error. In addition, the comparative study between the proposed approach and the previous work is presented in order to demonstrate the satisfaction of the proposed strategy. Finally, simulation results which are developed using Matlab software are presented to show the robustness and efficiency of the developed algorithm and the reactivity of the proposed sliding mode controller.

References

W. Rahmaniar and A. Wicaksono, “Design and Implementation of A Mobile Robot for Carbon Monoxide Monitoring,” Journal of Robotics and Control (JRC), vol. 2, no. 1, pp. 1-6, 2021.

H. Zhang, W. Lin, and A. Chen, “Path Planning for the Mobile Robot: A Review,” Symmetry, vol. 10, no. 10, p. 450, Oct. 2018,

I. Hassani, I. Ergui and C. Rekik, “Turning Point and Free Segments Strategies for Navigation of Wheeled Mobile Robot,” International Journal of Robotics and Control Systems, vol. 2, no. 1, pp. 172-186, 2022.

S. Sahloul, D. Ben Halima and C. Rekik, “Tracking trajectory of a mobile robot using sliding mode control,” International Multi-conference on Systems, Signals and Devices (SSD’18), 2018, pp. 1351-1355.

U. O. Rosas, O. Montiel and R. Sepulveda, “Mobile robot path planning using membrane evolutionary artificial potential field,” Applied Soft Computing, vol. 77, pp. 236-251, 2019.

S. Sahloul, D. Ben Halima and C. Rekik, “An hybridization of globallocal methods for autonomous mobile robot navigation in partially-known environments,” Journal of Robotics and Control (JRC), vol. 2, no. 4, pp. 351-355, 2021.

M. Boujelben, C. Rekik and N. Derbel, “A multi-agent architecture with hierarchical fuzzy controller for a mobile robot,” International journal of Robotics and Automation, vol. 30, no. 3, pp. 289-298, 2015.

I. Hassani, I. Maalej and C. Rekik, “Robot path planning with avoiding obstacles using free segments and turning points algorithms,” Mathematical problem in Engineering, vol. 2018, no. 6, pp. 1-13, 2018.

I. Hassani, I. Maalej and C. Rekik, “Mobile robot navigation using turning point algorithm and sliding mode controller,” International Multiconference on Systems, Signals and Devices (SSD’18), pp. 1345-1350, 2018.

A. N. Ouda, A. Mohamed, “Autonomous fuzzy heading control for a multi-wheeled combat vehicle,” International Journal of Robotics and Control Systems, vol. 1, no. 1, pp. 90-101, 2021.

C. B. Jabeur and H. Seddik, “Optimized Neural Networks-PID Controller with Wind Rejection Strategy for a Quad-Rotor,” Journal of Robotics and Control (JRC), vol. 3, no. 1, pp. 62-72, 2022.

C. Rekik, M. Jallouli, N. Derbel, “Optimal trajectory of a mobile robot using hierarchical fuzzy logic controller,” International Journal of Computer Applications in Technology, vol. 53, no. 4, pp. 348-357, 2016.

I. Hassani, I. Maalej and C. Rekik, “Control points searching algorithm for autonomous mobile robot navigation with obstacle avoidance,” 19th international conference on Sciences and Techniques of Automatic control and computer engineering (STA’19), 2019, pp. 158-163.

Y. K. Ever, “Using simplified swarm optimization on path planning for intelligent mobile robot,” International Conference on Theory and Application of Soft Computing with Words and Perception, ICSCCW, 2017, pp. 83-90.

K. Hao, J. Zhao , B. Wang , Y. Liu and C. Wang, “The application of an adaptive genetic algorithm based on collision detection in path planning of mobile robots,” International Journal of Computational Intelligence and Neuroscience, vol. 2021, pp. 1-20, 2021.

W. Rahmaniar and A. E. Rakhmania, “Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms,” Journal of Robotics and Control (JRC), vol. 3, no. 3, pp.1-7, 2021.

A. V. F. M. de Oliveira and M. A. C. Fernandes, “Dynamic planning navigation strategy for mobile terrestrial robots,” Robotica, vol. 34, pp. 568-583, 2016.

H. Cen and B. K. Singh, “Nonholonomic wheeled mobile robot trajectory tracking control based on improved sliding mode variable structure,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1-9, 2021.

T. T. Pham and M. T. Le, “Omnidirectional mobile robot trajectory tracking control with diversity of inputs,” International Journal of Mechanical Engineering and Robotics Research, vol. 10, no. 11, pp. 639-644, 2021.

J. A. Abougarair, K. I. M. Aburakhis and M. M. Edardar, “Adaptive neural networks based robust output feedback controllers for nonlinear systems,” International Journal of Robotics and Control Systems, vol. 2, no. 1, pp. 37-56, 2022.

Y. Koubaa, M. Boukattaya and T. Damak, “Intelligent control for an uncertain mobile robot with external disturbances estimator,” New Trends in Robot Control, Springer, 2020, pp. 155-176.

L. T. Anh, T. T. T. Nga and Vu Van Hoc b, “PID-type iterative learning control for output tracking gearing transmission systems,” International Journal of Robotics and Control Systems, vol. 1, no. 3, pp. 256-268, 2021.

A. Maarif and N. R. Setiawan, “Control of DC motor using integral state feedback and comparison with PID: simulation and arduino implementation,” Journal of Robotics and Control (JRC), vol. 2, no. 5, pp. 256-268, 2021.

Iswanto Iswanto and Irfan Ahmad, “Second Order Integral Fuzzy Logic Control Based Rocket Tracking Control,” Journal of Robotics and Control (JRC), 2021, vol. 2, pp. 594-604 , 2021.

F. Cherni, Y. Boutereaa, C. Rekik and N. Derbel, “Chattering reduction on the control input of a nonholonomic mobile robot using fuzzy logic controller,” Journal of Engineering Science and Technology Review, vol. 9, no. 3, pp. 41-48, 2016.

A. Bavarsad, A. Fakharian and M. B. Menhaj, “Optimal sliding mode controller for an active transfemoral prosthesis using state-dependent riccati equation approach,” Arabian Journal for Science and Engineering, vol. 45, no. 8, pp. 6559-6572, 2020.

M. S. Mahmoud, A. Alameer and M. M. Hamdan,“ An adaptive sliding mode control for single machine infinite bus system under unknown uncertainties,” International Journal of Robotics and Control Systems, vol. 1, no. 3, pp. 226-243, 2021.

H. Sira-Ram´ırez, M. A. Aguilar-Orduna and E. W. Zurita-Bustamante, “On the sliding mode control of MIMO nonlinear systems: An inputoutput approach,” International Journal of Robust Nonlinear Control, vol. 29, no. 3, pp. 715-735, 2019.