Humanoid Ai Robot: A Member of Our Next The Generation Family

Year : 2024 | Volume :11 | Issue : 01 | Page : –
By

    Narasimha Chary

  1. CH.GVN Prasad

  1. Associate Professor, Department of Computer Science and Engineering, Sri Indu College of Engineering & Technology, Sheriguda, Telangana, India
  2. Professor, Department of Computer Science and Engineering, Sri Indu College of Engineering & Technology, Sheriguda, Telangana, India

Abstract

Humanoid robots represent a swiftly advancing area of study and innovation, seeking to produce robots with traits and abilities resembling those of humans. These robots have diverse uses, such as aiding humans in different activities or exploring dangerous or inaccessible areas. Advancing humanoid robots entails combining cutting-edge technologies, including robotics, natural language processing, computer vision, and artificial intelligence. Combining computer science and engineering, robotics is an interdisciplinary subject of study. A subfield of robotics research known as “humanoid robotics” studies robots with human features that can imitate human behavior and intelligence. This study compares various humanoid robot versions that are already on the market and discusses recent developments in the field of humanoid robotics research. The problems and potential applications of artificial intelligence to complicated problem solving in humanoid robots are also briefly covered in this paper.

Keywords: Humanoid, robots, automated machinery, jumping pad, Autism Spectrum, Dynamic Moment Primitives, gait, Artificial intelligence, Research, Reinforcement learning.

[This article belongs to Journal of Advancements in Robotics(joarb)]

How to cite this article: Narasimha Chary, CH.GVN Prasad.Humanoid Ai Robot: A Member of Our Next The Generation Family.Journal of Advancements in Robotics.2024; 11(01):-.
How to cite this URL: Narasimha Chary, CH.GVN Prasad , Humanoid Ai Robot: A Member of Our Next The Generation Family joarb 2024 {cited 2024 Apr 22};11:-. Available from: https://journals.stmjournals.com/joarb/article=2024/view=143920


References

  1. Yoonkwon Hwang, Atsushi Konno and Masaru Uchiyama,” Whole Body Cooperative Tasks and Static Stability Evaluations for a Humanoid Robot,” in Proceedings of the 2003 IEEWRSJ InU. Conference on Intelligent Robots and Systems Las Vegas, Nevada ‘ October
  2. Jingguo Wang, Yangmin Li and Changwu Qiu,” Analysis of Dynamic Stability Constraints for a Mobile Humanoid Robot,” in Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics Bangkok, Thailand, February 21 – 26, 2009.
  3. Shobarani, R., Sharmila, R., Kathiravan, M. N., Pandian, A. A., Chary, C. N., & Vigneshwaran, K. (2023, April). Melanoma Malignancy Prognosis Using Deep Transfer Learning. In 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) (pp. 1-6). IEEE.
  4. Seung-Joon Yi, Stephen G. McGill, Byoung-Tak Zhang, Dennis Hong and Daniel Lee, “Active Stabilization of a Humanoid Robot for Real- Time Imitation of a Human Operator,” in International Conference on Humanoid Robots Nov.29-Dec., 2012.
  5. Trupthi, M., CH, N. C., SNEHA, S., Prasad, M. H., & K, M. S. (2024). Lcddf: an adaptive and learning based framework with feature selection for efficient detection of concept drift in data streams.. https://doi.org/10.21203/rs.3.rs-3893993/v1.
  6. Dingsheng Luo, Xiaoqiang Han, Yaoxiang Ding, Yang Ma, Zhan Liu and Xihong Wu, “Learning Push Recovery for a Bipedal Humanoid Robot with Dynamical Movement Primitives,” in 5th International Conference on Humanoid Robots Nov.-5, 2015.
  7. CH, Dr. “Narasimha Chary,”.” Comprehensive Study On Multi-Operator Base Stations Cell Binary And Multi-Class Models Using Azure Machine Learning”,” A Journal Of Composition Theory 14.6 (2021).
  8. Enrico Mingo Hoffman, Nicolas Perrin, Nikos G. Tsagarakis and Darwin Caldwell, “Upper limb compliant strategy exploiting external physical constraints for humanoid fall avoidance,” in the IEEE-RAS International Conference on Humanoid Robots (Humanoids).October 5-7 , 2013. Atlanta, GA.
  9. Chary, Ch Narasimha, et al. “An Efficient Survey on various Data Mining Classification Algorithms in Bioinformatics.” International Journal of Engineering and Techniques 4 (2018).
  10. Libo Meng, Zhangguo Yu, Xuechao Chen, Weimin Zhang, Marco Ceccarelli6, Kenji Hashimoto, Atsuo Takanishi, Qiang Huang Wenjuan Guo, Lin Xie and Huaxin Liu, “A Falling Motion Control of Humanoid Robots Based on Biomechanical Evaluation of Falling Down of Humans,” in 15th International Conference on Humanoid Robots Nov 3- 5, 2015, Seoul, Korea.
  11. Vincent Samy, St´ephane Caron, Karim Bouyarmane and Abderrahmane Kheddar, “Post-Impact Adaptive Compliance for Humanoid Falls Using Predictive Control of a Reduced Model,” in 17th International Conference on Humanoid Robotics (Humanoids) Birmingham, UK, November 15-17, 2017.
  12. Prasad, D. C. G. V. N., V. V. Vuna, K. G. Guptha, and V. K. Reddy. “General Framework Factorization for Context-Aware Recommendation of ECommerce on Automotives”. IJRDO -Journal of Computer Science Engineering, Vol. 1, no. 6, June 2015, pp. 01-09, doi:10.53555/cse.v1i6.935
  13. S. Saeedvand, M. Jafari, H. S. Aghdasi, and J. Baltes, “A comprehensive survey on humanoid robot development,” Knowledge Engineering Review. 2019. 12. T. H. S. Li, P.
  14. Kuo, T. N. Tsai, and P. C. Luan, “CNN and LSTM Based Facial Expression Analysis Model for a Humanoid Robot,” IEEE Access, 2019.
Regular Issue Subscription Review Article
Volume 11
Issue 01
Received April 8, 2024
Accepted April 11, 2024
Published April 22, 2024
Views: 16