Open Access
Issue
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
Article Number 03028
Number of page(s) 12
Section Intelligent Systems and Computing in Industry, Robotics, and Smart Infrastructure
DOI https://doi.org/10.1051/itmconf/20257803028
Published online 08 September 2025
  1. Khatib, O.: ‘Real-time obstacle avoidance for manipulators and mobile robots’, The International Journal of Robotics Research, 1986, 5, (1), pp. 90–98 [CrossRef] [Google Scholar]
  2. Wang, B.H., Wang, D.B., Ali, Z.A., Ting Ting, B., Wang, H.: ‘An overview of various kinds of wind effects on unmanned aerial vehicle’, Measurement and Control, 2019, 52, (7-8), pp. 731–739 [Google Scholar]
  3. Mohamed, E.M., Hashima, S., Aldosary, A., Hatano, K., Abdelghany, M.A.: ‘Gateway selection in millimeter wave UAV wireless networks using multi-player multi-armed bandit’, Sensors, 2020, 20, (14), pp. 3947 [Google Scholar]
  4. Lin, Y., Wang, T., Wang, S.: ‘UAV-assisted emergency communications: An extended multi-armed bandit perspective’, IEEE Communications Letters, 2019, 23, (5), pp. 938–941 [Google Scholar]
  5. Amrallah, A., Mohamed, E.M., Tran, G.K., Sakaguchi, K.: ‘UAV trajectory optimization in a post-disaster area using dual energy-aware bandits’, Sensors, 2023, 23, (3), pp. 1402 [Google Scholar]
  6. Wang, Z., Hong, T.: ‘Reinforcement learning for building controls: The opportunities and challenges’, Applied Energy, 2020, 269, pp. 115036 [CrossRef] [Google Scholar]
  7. Arroyo, J., Manna, C., Spiessens, F., Helsen, L.: ‘Reinforced model predictive control (RL-MPC) for building energy management’, Applied Energy, 2022, 309, pp. 118346 [Google Scholar]
  8. Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: ‘Learning-based model predictive control: Toward safe learning in control’, Annual Review of Control, Robotics, and Autonomous Systems, 2020, 3, (1), pp. 269–296 [Google Scholar]
  9. Schwenzer, M., Ay, M., Bergs, T., Abel, D.: ‘Review on model predictive control: An engineering perspective’, The International Journal of Advanced Manufacturing Technology, 2021, 117, (5), pp. 1327–1349 [Google Scholar]
  10. Larrabee, T., Chao, H., Rhudy, M., Gu, Y., Napolitano, M.R.: ‘Wind field estimation in UAV formation flight’. Proc. 2014 American Control Conference, June 2014, pp. 5408–5413 [Google Scholar]
  11. Xiang, X., Wang, Z., Mo, Z., Chen, G., Pham, K., Blasch, E.: ‘Wind field estimation through autonomous quadcopter avionics’. Proc. IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), September 2016, pp. 1–6 [Google Scholar]
  12. Hrabar, S.: ‘3D path planning and stereo-based obstacle avoidance for rotorcraft UAVs’, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, September, pp. 807–814. IEEE. [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.