Generalised regression neural network (GRNN) architecture-based motion planning and control of an e-puck robot in V-REP software platform

• Autorzy: Panwar Vikas Singh , Pandey Anish , Hasan Muhammad Ehtesham

Identyfikatory

DOI

10.2478/ama-2021-0027

• Języki publikacji: EN

Abstrakty

EN

This article focuses on the motion planning and control of an automated differential-driven two-wheeled E-puck robot using Generalized Regression Neural Network (GRNN) architecture in the Virtual Robot Experimentation Platform (V-REP) software platform among scattered obstacles. The main advantage of this GRNN over the feedforward neural network is that it provides accurate results in a short period with minimal error. First, the designed GRNN architecture receives real-time obstacle information from the Infra-Red (IR) sensors of an E-puck robot. According to IR sensor data interpretation, this architecture sends the left and right wheel velocities command to the E-puck robot in the V-REP software platform. In the present study, the GRNN architecture includes the MIMO system, i.e., multiple inputs (IR sensors data) and multiple outputs (left and right wheel velocities). The three-dimensional (3D) motion and orientation results of the GRNN architecture-controlled E-puck robot are carried out in the V-REP software platform among scattered and wall-type obstacles. Further on, compared with the feedforward neural network, the proposed GRNN architecture obtains better navigation path length with minimum error results.

Słowa kluczowe

EN

e-puck robot; generalised regression neural network architecture; virtual robot experimentation platform software; scattered obstacle; Infra-Red sensor

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2021
• Tom: Vol. 15, no. 4
• Strony: 209--214
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Panwar Vikas Singh

• School of Mechanical Engineering, KIIT Deemed to be University, An Institute of Eminence, Patia, Bhubaneswar, PIN - 751024, Odisha, India

• https://orcid.org/0000-0001-5874-2945

autor

Pandey Anish

• School of Mechanical Engineering, KIIT Deemed to be University, An Institute of Eminence, Patia, Bhubaneswar, PIN - 751024, Odisha, India

• https://orcid.org/0000-0001-9089-3727

autor

Hasan Muhammad Ehtesham

• School of Mechanical Engineering, KIIT Deemed to be University, An Institute of Eminence, Patia, Bhubaneswar, PIN - 751024, Odisha, India

Bibliografia

• 1. Almeida T., Santos V., Mozos O. M., Lourenço B. (2021), Comparative Analysis of Deep Neural Networks for the Detection and Decoding of Data Matrix Landmarks in Cluttered Indoor Environments. Journal of Intelligent & Robotic Systems, 103(1), 1-14.
• 2. Ben Jabeur C., Seddik H. (2020), Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot, Asian Journal of Control, 22(1), 1–19.
• 3. Elmi Z., Efe M. Ö. (2020), Online path planning of mobile robot using grasshopper algorithm in a dynamic and unknown environment, Journal of Experimental & Theoretical Artificial Intelligence, 32, 1–19.
• 4. Hadi N. H., Younus K. K. (2020), Path tracking and backstepping control for a wheeled mobile robot (WMR) in a slipping environment, IOP Conference Series: Materials Science and Engineering, 671, 1–17.
• 5. Khan H., Khatoon S., Gaur P. (2021), Comparison of various controller design for the speed control of DC motors used in two wheeled mobile robots. International Journal of Information Technology, 13(2), 713-720.
• 6. Long Y., Zuo Z., Su Y., Li J., Zhang H. (2020), An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles, The Journal of Navigation, 73(3), 1–16.
• 7. Narasimhan G. E., Bettyjane J. (2020), Implementation and study of a novel approach to control adaptive cooperative robot using fuzzy rules. International Jopurnal of Information Technology, 1–8. https://doi.org/10.1007/s41870-020-00459-z
• 8. Nedjah N., Junior L. S. (2019), Review of methodologies and tasks in swarm robotics towards standardization, Swarm and Evolutionary Computation, 50, 1–26.
• 9. Osaba E., Del Ser J., Iglesias A., Yang X. S. (2019), Soft Computing for Swarm Robotics: New Trends and Applications, Journal of Computational Science, 39, 1–4.
• 10. Pandey A., Kashyap A. K., Parhi D. R., Patle, B. K. (2019), Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture, World Journal of Engineering, 16(2), 275–286.
• 11. Pandey A., Parhi D. R. (2016), New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural network architecture, World Journal of Engineering, 13(2), 129–141.
• 12. Pandey K. K., Parhi D. R. (2019), Trajectory Planning and the Target Search by the Mobile Robot in an Environment Using a Behavior-Based Neural Network Approach, Robotica, 37(1), 1–15.
• 13. Protik P., Das S., Islam M. R. (2019, October). Chemical Reaction Optimization for Mobile Robot Path Planning. International Joint Conference on Computational Intelligence, Springer, Singapore, 191–203.
• 14. Quan Y., Ouyang H., Zhang C., Li S., Gao L. (2021), Mobile Robot Dynamic Path Planning Based on Self-adaptive Harmony Search Algorithm and Morphin Algorithm. IEEE Access, 10.1109/ACCESS.2021.3098706
• 15. Singh N.H, Thongam K. (2018), Neural network-based approaches for mobile robot navigation in static and moving obstacles environments, Intelligent Service Robotics, 12(1), 55–67.
• 16. Specht D. F. (1991), A general regression neural network. IEEE transactions on neural networks, 2(6), 568–576.
• 17. Teli T. A., Wani M. A. (2021), A fuzzy based local minima avoidance path planning in autonomous robots. International Journal of Information Technology, 13(1), 33-40.
• 18. Tripathy H. K., Mishra S., Thakkar H. K., Rai D. (2021), CARE: A Collision-Aware Mobile Robot Navigation in Grid Environment using Improved Breadth First Search. Computers & Electrical Engineering, 94, 107327.
• 19. Wang M. (2021), Real-time path optimization of mobile robots based on improved genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 235(5), 646-651.
• 20. Zhao T., Xiang Y., Dian S., Guo R., Li S. (2020), Hierarchical interval type-2 fuzzy path planning based on genetic optimization, Journal of Intelligent & Fuzzy Systems, 32, 1-12.