##plugins.themes.academic_pro.article.sidebar##

Download
pdf
Statistic
Read Counter : 75 Download : 74

##plugins.themes.academic_pro.article.main##

Abstract

This article explores the research conducted on the software and hardware aspects of industrial robots. Industrial robots have become integral to the manufacturing industry, and their performance relies heavily on the integration of reliable software and hardware systems. The research on software focuses on motion planning and control, perception and sensing, human-robot interaction, and the integration of artificial intelligence and machine learning. Hardware research investigates actuation systems, sensing technologies, collaborative and mobile robotics, and power and energy efficiency. The findings from this research contribute to enhancing the capabilities, safety, and efficiency of industrial robots, paving the way for their widespread adoption in various industries.

Keywords

productivity recognition efficiency

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
J.Sh. Sodikjanov, & Q.A.Khayitboyev. (2023). Research of Software-Hardware of Industrial Robots. Texas Journal of Engineering and Technology, 22, 13–16. Retrieved from https://zienjournals.com/index.php/tjet/article/view/4225

References

  1. Khatib, Oussama. “Robotics: The Road Ahead.”Science, vol. 337, no. 6099, 2012, pp. 1287-1288.
  2. Kootstra, Gert, et al. “Tactile Object Recognition: Beyond Contact Mechanics.” Advanced Robotics, vol. 28, no. 11, 2014, pp. 745-757.
  3. Bohg, Jeannette, et al. “Interactive Perception: Leveraging Action in Perception and Perception in Action.” IEEE Transactions on Robotics, vol. 33, no. 6, 2017, pp. 1273-1291.
  4. Dragan, Anca D., and Lee, Mark W. “A Policy-Blended Interaction Infrastructure for Robot-Assisted Instruction.” International Journal of Robotics Research, vol. 37, no. 13-14, 2018, pp. 1521-1542.
  5. Levine, Sergey, et al. “End-to-End Training of Deep Visuomotor Policies.” Journal of Machine Learning Research, vol. 17, no. 39, 2016, pp. 1-40.
  6. Kober, Jens, et al. “Reinforcement Learning in Robotics: A Survey.” International Journal of Robotics Research, vol. 32, no. 11, 2013, pp. 1238-1274.
  7. Iida, Fumiya, and Prattichizzo, Domenico. “Series Elastic Actuators in Robotics.” IEEE Transactions on Robotics, vol. 24, no. 5, 2008, pp. 1261-1273.
  8. Prattichizzo, Domenico, and Trinkle, Jeff. “Grasping.” Springer Handbook of Robotics, Springer, 2016, pp. 671-700.
  9. Kriegel, Simon, et al. “A Dense and Geometry-Aware Tactile Descriptor for Robust Grasp Classification.” IEEE Robotics and Automation Letters, vol. 5, no. 2, 2020, pp. 2937-2944.
  10. Haddadin, Sami, et al. “Robot Collisions: A Survey on Detection, Isolation, and Identification.” IEEE Transactions on Robotics, vol. 33, no. 6, 2017, pp. 1292-1312.
  11. Staub, Nicolas, et al. “On the Safety of Robots: A Biomechanical Perspective on Collaborative Robotics.” IEEE Robotics & Automation Magazine, vol. 25, no. 1, 2018, pp. 33-40.
  12. Zhou, Chenguang, et al. “A Review of Recent Advancements in Mobile Robot Systems.” Robotics and Autonomous Systems, vol. 117, 2019, pp. 27-51.
  13. Jafarnejadsani, Hamid, et al. “Energy Efficiency of Robot Joints: A Review.” Robotics and Autonomous Systems, vol. 91, 2017, pp. 86-104.
  14. Liu, Rui, et al. “Review on Power Optimization Strategies for Robotic Systems and Their Applications.” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 2, 2020, pp. 472-491.