Sensing flow gradients is necessary for learning autonomous underwater navigation
Abstract Aquatic animals are much better at underwater navigation than robotic vehicles. Robots face major challenges in deep water because of their limited access to global positioning signals and flow maps. These limitations, and the changing nature of water currents, support the use of reinforcem...
Saved in:
| Main Authors: | Yusheng Jiao, Haotian Hang, Josh Merel, Eva Kanso |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58125-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Mapping spatial patterns to energetic benefits in groups of flow-coupled swimmers
by: Sina Heydari, et al.
Published: (2024-12-01) -
Adaptive Deep Reinforcement Learning for Efficient 3D Navigation of Autonomous Underwater Vehicles
by: Elena Politi, et al.
Published: (2024-01-01) -
Reinforcement learning for autonomous underwater vehicles (AUVs): navigating challenges in dynamic and energy-constrained environments
by: Mohab M. Eweda, et al.
Published: (2024-12-01) -
An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs
by: Arif Wibisono, et al.
Published: (2024-01-01) -
Towards Visual Navigation of an Autonomous Underwater Vehicle in Areas with Posidonia Oceanica
by: Francisco Bonin-Font, et al.
Published: (2017-12-01)