Reinforcement Learning for Underwater Spatiotemporal Path Planning, with Application to an Autonomous Marine Current Turbine
Author:
Affiliation:
1. Florida Atlantic University,Department of Electrical Engineering and Computer Science,Boca Raton,FL,USA,33431
2. Florida Atlantic University,Department of Ocean and Mechanical Engineering,Boca Raton,FL,USA,33431
Funder
National Science Foundation
U.S. Department of Energy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10155646/10155787/10155898.pdf?arnumber=10155898
Reference29 articles.
1. Spatiotemporal Optimization Through Gaussian Process-Based Model Predictive Control: A Case Study in Airborne Wind Energy
2. Using interpolation to improve path planning: The Field D* algorithm
3. Planning-Augmented Hierarchical Reinforcement Learning
4. Comparison of Deep Reinforcement Learning and Model Predictive Control for Real-Time Depth Optimization of a Lifting Surface Controlled Ocean Current Turbine
5. AUV path planning: an A* approach to path planning with consideration of variable vehicle speeds and multiple, overlapping, time-dependent exclusion zones
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