Abstract
Direct communication between humans and autonomous underwater vehicles (AUVs) is a relatively under-explored area in human-robot interaction research, although many tasks (e.g., surveillance, inspection, and search-and-rescue) require close diver-robot collaboration. Suboptimal AUV positioning relative to its human collaborators can lead to poor quality interaction and lead to excessive cognitive and physical load for divers. In this paper, we introduce a novel method for AUVs to autonomously navigate and achieve diver-relative positioning to begin interaction. Our method is based only on monocular vision, requires no global localization, and is computationally efficient. We present our algorithm and its implementation on board a physical AUV, performing extensive evaluations in the form of closed-water tests in a controlled pool. Our results show that the proposed monocular vision-based algorithm performs reliably and efficiently, operating entirely on-board the AUV.
| Original language | English (US) |
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| Title of host publication | 2022 IEEE International Conference on Robotics and Automation, ICRA 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1076-1082 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781728196817 |
| DOIs | |
| State | Published - 2022 |
| Event | 39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States Duration: May 23 2022 → May 27 2022 |
Publication series
| Name | 2022 International Conference on Robotics and Automation (ICRA) |
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Conference
| Conference | 39th IEEE International Conference on Robotics and Automation, ICRA 2022 |
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| Country/Territory | United States |
| City | Philadelphia |
| Period | 5/23/22 → 5/27/22 |
Bibliographical note
Funding Information:The authors wish to thank their families, research collaborators, trial participants, and funding partners, in particular: Sophie Fulton, Jiyeon Hwang, Md Jahidul Islam, Spencer Ludlam, August Walter, the LoCO AUV team, the National Science Foundation, and the Minnesota Robotics Institute.
Funding Information:
The authors are with the Department of Computer Science and Engineering and the Minnesota Robotics Institute, University of Minnesota Twin Cities, Minneapolis, MN, USA. {1fulto081,2jungseok,3junaed}@umn.edu *This work was supported by the US National Science Foundation awards IIS-#184536 & #00074041, and the MnRI Seed Grant. The first two authors made equal contribution to the work and should both be cited as first author (ex. Fulton and Hong et al.)
Publisher Copyright:
© 2022 IEEE.