Building Intelligent Robots: The Role of Computer Vision and Mechanical Engineering in Control Systems
DOI:
https://doi.org/10.5281/Keywords:
Intelligent robots, Computer vision, Mechanical engineering, Robot control systems, Object detection, Sensor fusion, Autonomous systems, Real-time motion controlAbstract
The development of intelligent robot control systems has gained significant attention in recent years due to advancements in computer vision and mechanical engineering. Integrating these two disciplines enhances robots' ability to perceive, process, and act autonomously within dynamic environments. This paper explores the synergies between computer vision and mechanical engineering in designing intelligent control systems for robots. By leveraging computer vision techniques, robots can interpret visual data to make informed decisions, while mechanical engineering principles ensure precise and efficient execution of physical tasks. This study highlights key innovations, including object detection, real-time motion control, and sensor fusion, that enable robots to operate autonomously in complex environments. The integration of these technologies is critical for various applications, ranging from industrial automation to autonomous vehicles and medical robotics.
References
[1] G. Liu and B. Zhu, "Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering," International Journal of Computer Science and Information Technology, vol. 3, no. 1, pp. 219-226, 2024.
[2] P. Zhou et al., "Reactive human–robot collaborative manipulation of deformable linear objects using a new topological latent control model," Robotics and Computer-Integrated Manufacturing, vol. 88, p. 102727, 2024.
[3] F. Zacharias, C. Schlette, F. Schmidt, C. Borst, J. Rossmann, and G. Hirzinger, "Making planned paths look more human-like in humanoid robot manipulation planning," in 2011 IEEE International Conference on Robotics and Automation, 2011: IEEE, pp. 1192-1198.
[4] C. Yang, P. Zhou, and J. Qi, "Integrating visual foundation models for enhanced robot manipulation and motion planning: A layered approach," arXiv preprint arXiv:2309.11244, 2023.
[5] J. Scholz and M. Stilman, "Combining motion planning and optimization for flexible robot manipulation," in 2010 10th IEEE-RAS International Conference on Humanoid Robots, 2010: IEEE, pp. 80-85.
[6] A. Rosyid, C. Stefanini, and B. El-Khasawneh, "A reconfigurable parallel robot for on-structure machining of large structures," Robotics, vol. 11, no. 5, p. 110, 2022.
[7] D. Martínez, G. Alenya, and C. Torras, "Planning robot manipulation to clean planar surfaces," Engineering Applications of Artificial Intelligence, vol. 39, pp. 23-32, 2015.
[8] K. Hauser and V. Ng-Thow-Hing, "Randomized multi-modal motion planning for a humanoid robot manipulation task," The International Journal of Robotics Research, vol. 30, no. 6, pp. 678-698, 2011.
[9] L. Han, Z. Li, J. C. Trinkle, Z. Qin, and S. Jiang, "The planning and control of robot dextrous manipulation," in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), 2000, vol. 1: IEEE, pp. 263-269.
[10] K. Bouyarmane and A. Kheddar, "Humanoid robot locomotion and manipulation step planning," Advanced Robotics, vol. 26, no. 10, pp. 1099-1126, 2012.
[11] A. Billard and D. Kragic, "Trends and challenges in robot manipulation," Science, vol. 364, no. 6446, p. eaat8414, 2019.
[12] J. Baranda et al., "On the Integration of AI/ML-based scaling operations in the 5Growth platform," in 2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2020: IEEE, pp. 105-109.
[13] A. Chennupati, "The evolution of AI: What does the future hold in the next two years," World Journal of Advanced Engineering Technology and Sciences, vol. 12, no. 1, pp. 022-028, 2024.
[14] S. S. Gill et al., "Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots," Internet of Things and Cyber-Physical Systems, vol. 4, pp. 19-23, 2024.
[15] F. Tahir and M. Khan, "Big Data: the Fuel for Machine Learning and AI Advancement," EasyChair, 2516-2314, 2023.
