Abstract
In industrialized contexts, the capacity of the controlling scheme to simulate the unstructured and structured environment characteristics is a critical component of robot-environment interaction. Commercial robots must do complicated tasks at fast rates while adhering to strict cycle durations and maintaining exceptional precision. The robot's capacity to detect the existence of surrounding items is still lacking in the real-world industrial setting. Despite anthropomorphic robot manufacturers may encounter issues with the robot's interaction with its surroundings, there has yet to be a comprehensive examination of the robot's performance in terms of elementary geometric volume awareness in multiple geometrical areas and the tools that will ultimately be placed over its flange. This paper illustrates how the robot interacts with the environment to perceive and prevent accidents with the items in the environment. Moreover, the geometric model will be expanded to include the robot tool's volume to improve the whole system's perception skills. The experiment results would be presented to verify the technique, demonstrating that a systematic geometric model can cope with complicated real-world situations.
Similar content being viewed by others
References
Bai J, Wu Y, Zhang J, Chen F (2015) Subset based deep learning for rgb-d object recognition. Neurocomputing 165:280–292
Barraquand J, Langlois B, Latombe J-C (1989) Robot motion planning with many degrees of freedom and dynamic constraints, In: Proceedings of the 5th international symposium on Robotics research, pp. 435–444, 0–262–13253–2, 1991, MIT Press, Cambridge, MA, USA.
Brighton H and Selina H (2015) Introducing artificial intelligence: a graphic guide, ser. Introducing... Icon Books Limited. [Online]. Available: https://books.google.com.pk/books?id=4GxGCgAAQBAJ.
Esteves JS, Carvalho A, and Couto C, (2003) Generalized geometric triangulation algorithm for mobile robot absolute self-localization,” In: Industrial Electronics, 2003. ISIE’03. 2003 IEEE International Symposium on, vol 1. IEEE, pp 346–351
Hou Y, Zhang H, and Zhou S (2015) Convolutional neural network-based image representation for visual loop closure detection,” In: Information and Automation, 2015 IEEE International Conference on. IEEE, pp 2238–2245.
Kuen J, Lim KM, Lee CP (2015) Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle. Pattern Recogn 48(10):2964–2982
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
Lun R, Zhao W (2015) A survey of applications and human motion recognition with microsoftkinect. Int J Pattern Recognit Artif Intell 29(05):1555008
Markkandan S, Sharma A, Singh SP, Solanki V, Sethuramalingam S, Singh SP (2021) SVM-based compliance discrepancies detection using remote sensing for organic farms. Arabian J Geosci. https://doi.org/10.1007/s12517-021-07700-4
Narmatha C (2020) Research scenario of medical data mining using fuzzy and graph theory. Int J Adv Trends Comput Sci Eng 9(1):349–355. https://doi.org/10.30534/ijatcse/2020/52912020
Rajkumar R, Dileepan D, Chinmay C, Suresh P (2021) Modified Minkowski fractal multiband antenna with circular-shaped split-ring resonator for wireless applications”. Measurement 182:109766
Romanelli F, Tampalini F (2008a) A control algorithm for the management of multiple dynamical geometrical areas for industrial manipulators, In: Proceedings of the RAAD 2008a 17th International Workshop on Robotics in Alpe-Adria-Danube Region, Ancona, Italy, September 15–17, 2008a.
Veeriah V, Zhuang N, and Qi G-J (2015) Differential recurrent neural networks for action recognition, In: Computer Vision (ICCV), 2015 IEEE International Conference on IEEE, pp 4041–4049.
Van Wichert G, Lawitzky G (2001) Man-machine interaction for robot applications in everyday environments, In: Proceedings of the 10th IEEE International Workshop on Robot and Human Interactive Communication, pp 343–346, 2001.
Winkler B (2007) Safe space sharing human-robot cooperation using a 3D time-of-flight camera, Techical Conference - Robots and Vision Show, 2007.
Wu J, Yildirim I, Lim JJ, Freeman B, and Tenenbaum J (2015) Galileo: perceiving physical object properties by integrating a physics engine with deep learning, Adv Neural Inform Process Syst pp 127–135.
Funding
The authors received no specific funding for this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
The manuscript has not been submitted to more than one journal for simultaneous consideration. The manuscript has not been published previously. The Research not involved human participants and/or animals.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ramkumar, D., Ashraf, M., Sathesh Kumar, K. et al. Defining multiple geometrical areas with modeling of elementary geometrical volumes in robot-environment interaction. Int J Syst Assur Eng Manag (2022). https://doi.org/10.1007/s13198-022-01708-z
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13198-022-01708-z