Model‐Free Deep Reinforcement Learning with Multiple Line‐of‐Sight Guidance Laws for Autonomous Underwater Vehicles Full‐Attitude and Velocity Control
Autonomous underwater vehicles (AUVs) are increasingly utilized, driving the need for enhanced autonomy. Conventional proportional–integral–derivative (PID) algorithms require frequent control parameter adjustments under varying voyage conditions, which increases operational and experimental costs....
Saved in:
| Main Authors: | Chengren Yuan, Changgeng Shuai, Zhanshuo Zhang, Jianguo Ma, Yuan Fang, YuChen Sun |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-08-01
|
| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202400991 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on Autonomous Guidance and Track Following Technology of Autonomous-rail Rapid Tram
by: PENG Jing, et al.
Published: (2020-01-01) -
Integration of Sensor Data and Mathematical Modeling of Underwater Robot Behavior Using a Digital Twin
by: M. D. Gladyshev, et al.
Published: (2025-06-01) -
A Simulation and Training Platform for Remote-Sighted Assistance
by: Xuantuo Huang, et al.
Published: (2024-12-01) -
Control and Real-Time Monitoring of Autonomous Underwater Vehicle Through Underwater Wireless Optical Communication
by: Dongwook Jung, et al.
Published: (2025-05-01) -
Employee strategic goal sight and strategic action: the moderating role of openness to experience
by: Feng Hu, et al.
Published: (2025-04-01)