A Preliminary Analysis by using FCGA for Developing Low Power Neural Network Controller Autonomous Mobile Robot Navigation

Authors

  • M. Preetha Professor & Head, Department of CSE, Prince Shri Venkateshwara Padmavathy Engineering College
  • Archana A B Assistant Professor in CSE-Cyber Security Department, Madanappalli Institute of Technology & Science
  • K. Ragavan Assistant Professor Senior Grade I, School of Computer Science and Engineering, Vellore Institute of Technology, VIT University, Vellore -632014, Tamilnadu, India
  • T. Kalaichelvi Professor, Dept of Artificial Intelligence and Data Science Panimalar Engineering College
  • M. Venkatesan Professor, ECE Department, PBR Visvodaya Institute of Technology and Science, Kavali, AP

Keywords:

Autonomous Mobile Robot, FPGA, Neural Network, Re-configurability, Path Planning and Obstacle Avoidance

Abstract

The desire for autonomous robots that can plan their paths and avoid obstacles is quickly increasing. In this study, we provide a machine - learning system design for robotic systems that detects and avoids obstacles using an artificial neural forecasting models and FPGA distributive processing algorithms. On the FPGA Virtex-II pro kit, the back propagation approach is used to implement learning and prediction. A floating point based computing approach is used to increase the neural network's flexibility and accuracy. The suggested paradigm is based on the notion of re-configurability, which lowers the cost and footprint of implementation. This suggested mobile robots robot design uses pipelined architecture that enhances predicting speed and lowers forecast latency. For emulation, the Xilinx 14.3 ISE simulation is utilised. The suggested methodology for managing the autonomous robot yielded good throughput and low power consumption in Place and Route outcomes.

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References

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Published

27.12.2023

How to Cite

Preetha, M. ., A B, A. ., Ragavan, K., Kalaichelvi, T. ., & Venkatesan, M. . (2023). A Preliminary Analysis by using FCGA for Developing Low Power Neural Network Controller Autonomous Mobile Robot Navigation. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 39–42. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4199

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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