Path planning of Unmanned Surface Vehicle based on convolutional and long short-term memory neural network
Author:
Affiliation:
1. Binzhou Polytechnic,Students Office,Binzhou,China
2. Binzhou Polytechnic,NOC Department,Binzhou,China
3. Harbin Engineering University,College of Intelligent Systems Science and Engineering,Harbin,China
Funder
Research and Development
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10401265/10401324/10401721.pdf?arnumber=10401721
Reference24 articles.
1. Local reactive obstacle avoidance approach for high-speed unmanned surface vehicle
2. Use of relaxation methods in sampling-based algorithms for optimal motion planning
3. Learning of Multi-Context Models for Autonomous Underwater Vehicles
4. Asymptotically Near-Optimal RRT for Fast, High-Quality Motion Planning
5. Sliding Mode Control Techniques and Artificial Potential Field for Dynamic Collision Avoidance in Rendezvous Maneuvers
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