EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 42 / No 2 (April 2024) JNWPU, 42 2 (2024) 310-318 References
Open Access
Issue
JNWPU
Volume 42, Number 2, April 2024
Page(s) 310 - 318
DOI https://doi.org/10.1051/jnwpu/20244220310
Published online 30 May 2024
  1. WU Wenliang, ZHOU Xingshe, SHEN Bo, et al. A review of swarm robotic systems property evaluation research[J]. Acta Automatica Sinica, 2022, 48(5): 1153–1172. [Article] (in Chinese) [Google Scholar]
  2. KOU K, YANG G, ZHANG W, et al. UAV autonomous navigation based on multi-modal perception: a deep hierarchical reinforcement learning method[C]//China Intelligent Robotics Annual Conference, 2023 [Google Scholar]
  3. ZHANG Yunyan, WEI Yao, LIU Hao, et al. End-to-end UAV obstacle avoidance decision based on deep reinforcement learning[J]. Journal of Northwestern Polytechnical University, 2022, 40(5): 1055–1064. [Article] (in Chinese) [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. ALMAHAMID F, GROLINGER K. Autonomous unmanned aerial vehicle navigation using reinforcement learning: a systematic review[J]. Engineering Applications of Artificial Intelligence, 2022, 115(1): 105321 [CrossRef] [Google Scholar]
  5. ARAFAT M Y, ALAM M M, MOH S. Vision-based navigation techniques for unmanned aerial vehicles: review and challenges[J]. Drones, 2023, 7(2): 89. [Article] [CrossRef] [Google Scholar]
  6. GANDHI D, PINTO L, GUPTA A. Learning to fly by crashing[C]//IEEE International Conference on Intelligent Robots and Systems, 2017 [Google Scholar]
  7. KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84–90. [Article] [CrossRef] [Google Scholar]
  8. MACIEL-PEARSON B G, AKCAY S, ATAPOUR-ABARGHOUEI A, et al. Multi-task regression-based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 4116–4123. [Article] [Google Scholar]
  9. CHOI S Y, CHA D. Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art[J]. Advanced Robotics, 2019, 33(6): 265–277. [Article] [CrossRef] [Google Scholar]
  10. MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529–533. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  11. WALVEKAR A, GOEL Y, JAIN A, et al. Vision based autonomous navigation of quadcopter using reinforcement learning[C]//International Conference on Automation, Electronics and Electrical Engineering, 2019 [Google Scholar]
  12. KABAS B. Autonomous UAV navigation via deep reinforcement learning using ppo[C]//Signal Processing and Communications Applications Conference, 2022 [Google Scholar]
  13. SCHULMAN J, WOLSKI F, DHARIWAL P, et al. Proximal policy optimization algorithms[J/OL]. (2017-07-20)[2023-03-27]. [Article] [Google Scholar]
  14. HE L, AOUF N, WHIDBORNE J F, et al. Integrated moment-based lgmd and deep reinforcement learning for UAV obstacle avoidance[C]//IEEE International Conference on Robotics and Automation, 2020 [Google Scholar]
  15. HAARNOJA T, ZHOU A, ABBEEL P, et al. Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor[C]//International Conference on Machine Learning, 2018 [Google Scholar]
  16. SHAH S, DEY D, KAPOOR A. Airsim: high-fidelity visual and physical simulation for autonomous vehicles[C]//The 11th International Conference on Field and Service Robotics, 2017 [Google Scholar]
  17. LILLICRAP T P, HUNT J J, PRITZEL A, et al. Continuous control with deep reinforcement learning[J/OL]. (2015-09-09)[2023-03-27]. [Article] [Google Scholar]
  18. FUJIMOTO S, HOOF H, MEGER D. Addressing function approximation error in actor-critic methods[C]//International Conference on Machine Learning, 2018 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.