Online Path Planning for AUV in Dynamic Ocean Scenarios: A Lightweight Neural Dynamics Network Approach-Reference-Cited by-同舟云学术

Online Path Planning for AUV in Dynamic Ocean Scenarios: A Lightweight Neural Dynamics Network Approach

Published:2024-02 Issue:2 Volume:9 Page:3782-3795
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Han Song¹^ORCID,Zhao Jiaao¹^ORCID,Li Xinbin¹^ORCID,Yu Junzhi²^ORCID,Wang Shuili³^ORCID,Liu Zhixin¹^ORCID

Affiliation:

1. Key Lab of Industrial Computer Control Engineering of Hebei Province, Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Yanshan University, Qinhuangdao, Hebei, China

2. State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing, China

3. Blue Park Smart (Beijing) Energy Technology Company, Ltd., Beijing, China

Funder

National Natural Science Foundation of China

S&T Program of Hebei

Hebei Innovation Capability Improvement Plan Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7274857/10510053/10411056.pdf?arnumber=10411056

Reference32 articles.

1. Multi-AUV dynamic trajectory optimization and collaborative search combined with task urgency and energy consumption scheduling in 3-D underwater environment with random ocean currents and uncertain obstacles

2. Fault-Tolerant Architecture for AUVs

3. A Multi-AUV Path Planning System Based on the Omni-Directional Sensing Ability

4. ACO-A*: Ant Colony Optimization Plus A* for 3-D Traveling in Environments With Dense Obstacles

5. Energy optimised D* AUV path planning with obstacle avoidance and ocean current environment

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