- Home
- Search Results
- Page 1 of 1
Search for: All records
-
Total Resources4
- Resource Type
-
03000010000
- More
- Availability
-
40
- Author / Contributor
- Filter by Author / Creator
-
-
Kobilarov, Marin (4)
-
Baraban, Gabriel (2)
-
Hager, Gregory D. (2)
-
Sheckells, Matthew (2)
-
Bai, Jin (1)
-
Garimella, Gowtham (1)
-
Katyal, Kapil D. (1)
-
Kim, Soowon (1)
-
Kothiyal, Siddharth (1)
-
Mishra, Subhransu (1)
-
Sefati, Shahriar (1)
-
Wang, Weiyao (1)
-
#Tyler Phillips, Kenneth E. (0)
-
#Willis, Ciara (0)
-
& Abreu-Ramos, E. D. (0)
-
& Abramson, C. I. (0)
-
& Abreu-Ramos, E. D. (0)
-
& Adams, S.G. (0)
-
& Ahmed, K. (0)
-
& Ahmed, Khadija. (0)
-
- Filter by Editor
-
-
& Spizer, S. M. (0)
-
& . Spizer, S. (0)
-
& Ahn, J. (0)
-
& Bateiha, S. (0)
-
& Bosch, N. (0)
-
& Brennan K. (0)
-
& Brennan, K. (0)
-
& Chen, B. (0)
-
& Chen, Bodong (0)
-
& Drown, S. (0)
-
& Ferretti, F. (0)
-
& Higgins, A. (0)
-
& J. Peters (0)
-
& Kali, Y. (0)
-
& Ruiz-Arias, P.M. (0)
-
& S. Spitzer (0)
-
& Sahin. I. (0)
-
& Spitzer, S. (0)
-
& Spitzer, S.M. (0)
-
(submitted - in Review for IEEE ICASSP-2024) (0)
-
-
Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Garimella, Gowtham ; Sheckells, Matthew ; Kim, Soowon ; Baraban, Gabriel ; Kobilarov, Marin ( , IEEE Robotics and Automation Letters)
-
Sefati, Shahriar ; Mishra, Subhransu ; Sheckells, Matthew ; Katyal, Kapil D. ; Bai, Jin ; Hager, Gregory D. ; Kobilarov, Marin ( , 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
-
Baraban, Gabriel ; Kothiyal, Siddharth ; Kobilarov, Marin ( , 2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO))This work considers autonomous fruit picking using an aerial grasping robot by tightly integrating vision-based perception and control within a learning framework. The architecture employs a convolutional neural network (CNN) to encode images and vehicle state information. This encoding is passed into a sub-task classifier and associated reference waypoint generator. The classifier is trained to predict the current phase of the task being executed: Staging, Picking, or Reset. Based on the predicted phase, the waypoint generator predicts a set of obstacle-free 6-DOF waypoints, which serve as a reference trajectory for model-predictive control (MPC). By iteratively generating and following these trajectories, the aerial manipulator safely approaches a mock-up goal fruit and removes it from the tree. The proposed approach is validated in 29 flight tests, through a comparison to a conventional baseline approach, and an ablation study on its key features. Overall, the approach achieved comparable success rates to the conventional approach, while reaching the goal faster.more » « less