ArticleNature communications2021
Learning efficient navigation in vortical flow fields.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Mixing of a binary passive particle system using smart active particles.Scientific reports · 2025Article
- Trajectory planning for drone landing, incorporating wind-sensing capabilities, operational and safety objectives, and reinforcement learning.Communications engineering · 2025Article
- Learning to Navigate in Chemical Fields Without A Map at Low Reynolds Numbers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Chemotactic navigation in robotic swimmers via reset-free hierarchical reinforcement learning.Nature communications · 2025Article
- Sensing flow gradients is necessary for learning autonomous underwater navigation.Nature communications · 2025Article
- Surfing vortex rings for energy-efficient propulsion.PNAS nexus · 2025Article
- A platform-agnostic deep reinforcement learning framework for effective Sim2Real transfer towards autonomous driving.Communications engineering · 2024Article
- Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation.Sensors (Basel, Switzerland) · 2024Article
- Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination.Scientific reports · 2023Article
- Fish response to the presence of hydrokinetic turbines as a sustainable energy solution.Scientific reports · 2023Article
- A Fast-Tracking-Particle-Inspired Flow-Aided Control Approach for Air Vehicles in Turbulent Flow.Biomimetics (Basel, Switzerland) · 2022Article
- Finite-horizon, energy-efficient trajectories in unsteady flows.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Efficient point-to-point navigation in the presence of a background flow field is important for robotic applications such as ocean surveying. In such applications, robots may only have knowledge of their immediate surroundings or be faced with time-varying currents, which limits the use of optimal control techniques. Here, we apply a recently introduced Reinforcement Learning algorithm to discover time-efficient navigation policies to steer a fixed-speed swimmer through unsteady two-dimensional flow fields. The algorithm entails inputting environmental cues into a deep neural network that determines the swimmer's actions, and deploying Remember and Forget Experience Replay. We find that the resulting swimmers successfully exploit the background flow to reach the target, but that this success depends on the sensed environmental cue. Surprisingly, a velocity sensing approach significantly outperformed a bio-mimetic vorticity sensing approach, and achieved a near 100% success rate in reaching the target locations while approaching the time-efficiency of optimal navigation trajectories.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.