ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration
Author:
Affiliation:
1. College of Design and Engineering, National University of Singapore,Department of Mechanical Engineering
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10160211/10160212/10160565.pdf?arnumber=10160565
Reference27 articles.
1. Sampling-based robotic information gathering algorithms
2. Randomized algorithm for informative path planning with budget constraints
3. Fast and Compute-Efficient Sampling-Based Local Exploration Planning via Distribution Learning
4. Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments
5. Autonomous UAV Exploration of Dynamic Environments Via Incremental Sampling and Probabilistic Roadmap
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1. Deep Reinforcement Learning With Dynamic Graphs for Adaptive Informative Path Planning;IEEE Robotics and Automation Letters;2024-09
2. RASLS: Reinforcement Learning Active SLAM Approach with Layout Semantic;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
3. Learning Hierarchical Graph-Based Policy for Goal-Reaching in Unknown Environments;IEEE Robotics and Automation Letters;2024-06
4. Deep Reinforcement Learning-Based Large-Scale Robot Exploration;IEEE Robotics and Automation Letters;2024-05
5. The surface edge explorer (SEE): A measurement-direct approach to next best view planning;The International Journal of Robotics Research;2024-02-15
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