Java in Robotics: Bridging Software Development and Hardware Control-Reference-Cited by-同舟云学术

Java in Robotics: Bridging Software Development and Hardware Control

Published:2023-03-28 Issue:1 Volume:12 Page:17-30
ISSN:2312-203X
Container-title:ABC Journal of Advanced Research
language:
Short-container-title:ABC j. adv. res.

Author:

Kothapalli Kanaka Rakesh Varma

Abstract

This research examines how Java bridges robotics software development with hardware control. The main goals are to assess Java's performance in robotic system integration, identify its drawbacks, and suggest ways to improve it. JRobotics, LeJOS, and ROSJava, are reviewed using secondary data to determine their effects on hardware interface, real-time performance, and data processing. According to major studies, Java's platform freedom and modularity enable software and hardware integration. Real-time performance, hardware interface, and memory management remain issues. The Real-Time Specification for Java (RTSJ) and specialized libraries provide partial solutions but need additional development. Policy implications include investing in Java library improvements and improving Java developer-robotics researcher cooperation. Research and optimization will improve Java's position in robotics, making robots more efficient and versatile.

Publisher

ABC Journals

Reference31 articles.

1. Addimulam, S., Mohammed, M. A., Karanam, R. K., Ying, D., Pydipalli, R., Patel, B., Shajahan, M. A., Dhameliya, N., & Natakam, V. M. (2020). Deep Learning-Enhanced Image Segmentation for Medical Diagnostics. Malaysian Journal of Medical and Biological Research, 7(2), 145-152. https://mjmbr.my/index.php/mjmbr/article/view/687

2. Ahmmed. S., Sachani, D. K., Natakam, V. M., Karanam, R. K. (2021). Stock Market Fluctuations and Their Immediate Impact on GDP. Journal of Fareast International University, 4(1), 1-6. https://www.academia.edu/121248146

3. Anumandla, S. K. R., Yarlagadda, V. K., Vennapusa, S. C. R., & Kothapalli, K. R. V. (2020). Unveiling the Influence of Artificial Intelligence on Resource Management and Sustainable Development: A Comprehensive Investigation. Technology & Management Review, 5, 45-65. https://upright.pub/index.php/tmr/article/view/145

4. Chaos, D., Chacón, J., Lopez-Orozco, J. A., Dormido, S. (2013). Virtual and Remote Robotic Laboratory Using EJS, MATLAB and LabVIEW. Sensors, 13(2), 2595-2612. https://doi.org/10.3390/s130202595

5. Fadziso, T., Mohammed, R., Kothapalli, K. R. V., Mohammed, M. A., Karanam, R. K. (2022). Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions. Silicon Valley Tech Review, 1(1), 14-34.