Zobrazeno 1 - 10
of 436
pro vyhledávání: '"Satyandra K Gupta"'
Autor:
Shaurya Shriyam, Satyandra K Gupta
Publikováno v:
International Journal of Advanced Robotic Systems, Vol 16 (2019)
This article presents an approach for assessing contingency resolution strategies using temporal logic. We present a framework for nominal mission modeling, then specifying contingency resolution strategies and evaluating their effectiveness for the
Externí odkaz:
https://doaj.org/article/73706bf1a10943e6a9a0adb5b0c4f9ef
Autor:
Hugh A. Bruck, Satyandra K. Gupta
Publikováno v:
Biomimetics, Vol 8, Iss 6, p 485 (2023)
Flapping Wing Air Vehicles (FWAVs) have proven to be attractive alternatives to fixed wing and rotary air vehicles at low speeds because of their bio-inspired ability to hover and maneuver. However, in the past, they have not been able to reach their
Externí odkaz:
https://doaj.org/article/d746b643469b40fa897716cad0a7cef4
Autor:
Rishi K. Malhan, Shantanu Thakar, Ariyan M. Kabir, Pradeep Rajendran, Prahar M. Bhatt, Satyandra K. Gupta
Publikováno v:
IEEE Transactions on Automation Science and Engineering. 20:193-205
Publikováno v:
Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction.
Autor:
Prahar M. Bhatt, Ashish Kulkarni, Alec Kanyuck, Rishi K. Malhan, Luis S. Santos, Shantanu Thakar, Hugh A. Bruck, Satyandra K. Gupta
Publikováno v:
The International Journal of Advanced Manufacturing Technology. 119:3545-3570
Publikováno v:
IEEE Transactions on Automation Science and Engineering. 19:191-206
Mobile manipulators are being deployed for transporting parts between machines and work stations in warehouses and shop floors. To increase the efficiency of operations, these mobile manipulators are required to complete the tasks as fast as possible
Autor:
Neel Dhanaraj, Yeo Jung Yoon, Rishi Malhan, Prahar M. Bhatt, Shantanu Thakar, Satyandra K. Gupta
Publikováno v:
Procedia Computer Science. 200:1528-1539
Publikováno v:
Fracture, Fatigue, Failure and Damage Evolution, Volume 3 ISBN: 9783031174667
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24c1c8a165ae0c5bc2d43ae953b34425
https://doi.org/10.1007/978-3-031-17467-4_12
https://doi.org/10.1007/978-3-031-17467-4_12
Autor:
Omey M. Manyar, Junyan Cheng, Reuben Levine, Vihan Krishnan, Jernej Barbič, Satyandra K. Gupta
Publikováno v:
Journal of Computing and Information Science in Engineering. 23
Deep learning-based image segmentation methods have showcased tremendous potential in defect detection applications for several manufacturing processes. Currently, majority of deep learning research for defect detection focuses on manufacturing proce