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 reinforcement learning approaches, where the navigator learns through trial-and-error interactions with the flow environment. But is it feasible to learn underwater navigation in the agent's Umwelt, without any land references? Here, we tasked an artificial swimmer with learning to reach a specific destination in unsteady flows by relying solely on egocentric observations, collected through on-board flow sensors in the agent's body frame, with no reference to a geocentric inertial frame. We found that while sensin

SUBMITTER: Jiao Y 

PROVIDER: S-EPMC11953274 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature