Reinforcement Learning-Based Trajectory Optimization for Data Muling With Underwater Mobile Nodes

Reinforcement Learning-Based Trajectory Optimization for Data Muling With Underwater Mobile Nodes

This paper addresses trajectory optimization problems for underwater data muling with mobile nodes. In the underwater data muling scenario, multiple autonomous underwater vehicles (AUVs) sample a mission area, and autonomous surface vehicles (ASVs) visit the navigating AUVs to retrieve the collected...

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Bibliographic Details
Main Authors: Qiang Fu, Aijun Song, Fuming Zhang, Miao Pan
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Underwater acoustic communications
data muling
autonomous underwater vehicles
autonomous surface vehicles
trajectory optimization
reinforcement learning
Online Access:https://ieeexplore.ieee.org/document/9749248/

Internet

https://ieeexplore.ieee.org/document/9749248/

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