Design and Development of a Wi-Fi-Enabled Helping Robot for Enhanced Human Assistance

Abstract:

In recent years, vicinity of robotics has made considerable evolves, resulting in an assortment of robots that can assist and collaborate with people in a variety of situations. The integration of robotics into human environments offers the potential to address challenges and provide assistance in scenarios where human intervention may be limited or impractical. Various challenges faced by different demographic groups require specialized solutions. For example, the elderly may need assistance with mobility, while differently-abled individuals may require support with daily tasks. The robot will be designed to navigate through indoor environments, recognize objects, and respond to user commands through natural language processing. To design and implement a human-helping robot that can provide support in daily activities, enhancing the quality of life for individuals.

• Periodical: Engineering Headway (Volume 16)
• Pages: 43-48
• DOI: https://doi.org/10.4028/p-lqCZ1Q
• Online since: January 2025
• Authors: S. Ayyappan*, J. Gokul, J. Priyadharshini, S. Santhiya, M. Santhosh
• Keywords: Collaborating with Humans, Helping Robots, Integration of Robotics, Providing Assistance

References

[1] Mark Billinghurst, Adrian Clark, and Gun Lee. 2015. A survey of augmented reality. Foundations and Trends in Human-Computer Interaction 8, 2-3 (2015), 73–272.

DOI: 10.1561/1100000049

[2] Z. Li, C. Yang, and E. Burdet, "An overview of biomedical robotics and bio-mechatronics systems and applications," IEEE Trans. Syst., Man, Cybern., Syst., vol. 46, no. 7, p.869–874, Jul. 2016.

DOI: 10.1109/tsmc.2016.2571786

[3] O.M. Omisore et al., "Non-iterative geometric approach for inverse kinematics of redundant lead-module in a radiosurgical snake-like robot," BioMed. Eng. Online, vol. 16, no. 1, p.1–21, Dec. 2017.

DOI: 10.1186/s12938-017-0383-2

[4] L. Le, A. Patterson, and M. White, "Supervised autoencoders: Improving generalization performance with unsupervised regularizers," in Proc. Adv. Neural Inf. Process. Syst., 2018, p.107–117.

[5] B. Sadrfaridpour and Y. Wang, "Collaborative assembly in hybrid manufacturing cells: An integrated framework for human–robot interaction," IEEE Trans. Automat. Sci. Eng., vol. 15, no. 3, p.1178–1192, Jul. 2018.

DOI: 10.1109/tase.2017.2748386

[6] R. Chalapathy and S. Chawla, "Deep learning for anomaly detection: A survey," 2019, arXiv:1901.03407.

[7] T. Ji, S. T. Vuppala, G. Chowdhary, and K. Driggs-Campbell, "Multimodal anomaly detection for unstructured and uncertain environments," in Proc. Conf. Robot Learn., 2020, p.1443–1455.

[8] Z. Zhang, E. Kayacan, B. Thompson, and G. Chowdhary, "High precision control and deep learning-based corn stand counting algorithms for agricultural robot," Auton. Robots, vol. 44, no. 7, p.1289–1302, 2020.

DOI: 10.1007/s10514-020-09915-y

[9] "Coronavirus Graphs: Worldwide Cases and Deaths - Worldometer." [Online].Available: https://www.worldometers.info/coronavirus/worldwide-graphs/#totalcases. [Accessed: 10-Nov-2020].

[10] A. Casalino, A. M. Zanchettin, L. Piroddi, and P. Rocco, "Optimal scheduling of human–robot collaborative assembly operations with time Petri nets," IEEE Trans. Automat. Sci. Eng., vol. 18, no. 1, p.70–84, Jan. 2021.

DOI: 10.1109/tase.2019.2932150

[11] Optimal planning and allocation of Plug-in Hybrid Electric Vehicles charging stations using a novel hybrid optimization technique, Ayyappan Subramaniam, LRS Ravi Singh - Plos one, 2023.

DOI: 10.1371/journal.pone.0284421