Dynamic Obstacle Avoidance for USVs Using Cross-Domain Deep Reinforcement Learning and Neural Network Model Predictive Controller

Dynamic Obstacle Avoidance for USVs Using Cross-Domain Deep Reinforcement Learning and Neural Network Model Predictive Controller

This work presents a framework that allows Unmanned Surface Vehicles (USVs) to avoid dynamic obstacles through initial training on an Unmanned Ground Vehicle (UGV) and cross-domain retraining on a USV. This is achieved by integrating a Deep Reinforcement Learning (DRL) agent that generates high-leve...

Full description

Bibliographic Details
Main Authors: Jianwen Li, Jalil Chavez-Galaviz, Kamyar Azizzadenesheli, Nina Mahmoudian
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Sensors
Subjects:
unmanned surface vehicle
deep reinforcement learning
collision avoidance
model predictive control
Online Access:https://www.mdpi.com/1424-8220/23/7/3572

Internet

https://www.mdpi.com/1424-8220/23/7/3572

Similar Items