Open Access
Issue
JNWPU
Volume 40, Number 5, October 2022
Page(s) 1055 - 1064
DOI https://doi.org/10.1051/jnwpu/20224051055
Published online 28 November 2022
  1. WEI C, ZHANG F, YIN C, et al. Research on UAV intelligent obstacle avoidance technology during inspection of transmission line[C]//The 2015 International Conference on Applied Mechanics, Mechatronics and Intelligent Systems, 2015: 319-327 [Google Scholar]
  2. HWANGBO M, KUFFNER J, KANADE T. Efficient two-phase 3D motion planning for small fixed-wing uavs[C]//IEEE International Conference on Robotics and Automation, 2007: 10-14 [Google Scholar]
  3. HENG L, MEIER L, TANSKANEN P, et al, Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing[C]//IEEE International Conference on Robotics and Automation, 2011: 2472-2477 [Google Scholar]
  4. KUFFNER J, LAVALLE S. RRT-connect: an efficient approach to single query path planning[C]//IEEE International Conference on Robotics and Automation, 2000: 995-1001 [Google Scholar]
  5. DEITS R, TEDRAKE R. Efficient mixed-integer planning for UAVs in cluttered environments[C]//IEEE International Conference on Robotics and Automation, 2016: 42-49 [Google Scholar]
  6. SHIM D, CHUNG H, KIM H J, et al. Sastry, autonomous exploration in unknown urban environments for unmanned aerial vehicles[C]//AIAA Guidance, Navigation, and Control Conference and Exhibit, 2005: 1-8 [Google Scholar]
  7. YANG Kunshan. Application and real-time of ivnage sematic segmentation based on deep lecrning in 3D rocomstmction system[D]. Chengdu: University of Electronic Science and Technolgey of China, 2019 (in Chinese) [Google Scholar]
  8. WAYDO S. Vehicle motion planning using stream functions[C]//IEEE International Conference on Robotics and Automation, 2003, 2: 2484-2491 [Google Scholar]
  9. TAI L, LI S, LIU M. A deep-network solution towards model-less obstacle avoidance[C]//2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016: 2759-2764 [Google Scholar]
  10. SuttonR S, BartoA G. Reinforcement learning: An introduction[J]. Cambridge: MIT Press, 2018 [Google Scholar]
  11. VANNESTE S, BELLEKENS B, WEYN M. 3DVFH+: real-time three-dimensional obstacle avoidance using an octomap[C]//Procedings of the 1st International Workshop on Model-Diwen Robot Scitwane Engineering Foundations, 2014 [Google Scholar]
  12. KIM M S, HAN D K, PARK J H, et al. Motion planning of robot manipulators for a smoother path using a twin delayed deep deterministic policy gradient with hindsight experience replay[J]. Applied Sciences, 2020, 10(2): 575 [CrossRef] [Google Scholar]
  13. PETERS J, VIJAYAKUMAR S, SCHAAL S. Natural actor-critic[C]//European Conference on Machine Learining, Berlin, 2005: 280-291 [Google Scholar]
  14. SRDJAN J, VICTOR G, ANDREW B. Airway anatomy of airsim high-fidelity simulator[J]. Anesthesiology, 2013, 118(1): 229–230 [Article] [CrossRef] [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.