Volume 40, Number 5, October 2022
|Page(s)||1055 - 1064|
|Published online||28 November 2022|
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- WAYDO S. Vehicle motion planning using stream functions[C]//IEEE International Conference on Robotics and Automation, 2003, 2: 2484-2491 [Google Scholar]
- 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]
- SuttonR S, BartoA G. Reinforcement learning: An introduction[J]. Cambridge: MIT Press, 2018 [Google Scholar]
- 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]
- 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]
- PETERS J, VIJAYAKUMAR S, SCHAAL S. Natural actor-critic[C]//European Conference on Machine Learining, Berlin, 2005: 280-291 [Google Scholar]
- SRDJAN J, VICTOR G, ANDREW B. Airway anatomy of airsim high-fidelity simulator[J]. Anesthesiology, 2013, 118(1): 229–230 [Article] [CrossRef] [Google Scholar]
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