Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 12, 2021
Advances in Modeling and Optimization of Manufacturing Processes
Article Number 24
Number of page(s) 8
DOI https://doi.org/10.1051/smdo/2021024
Published online 27 October 2021
  1. H. Shakhatreh, A.H. Sawalmeh, A. Al-Fuqaha, Z. Dou, E. Almaita, I. Khalil, M. Guizani, Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges, IEEE Access 7, 48572–48634 (2019) [Google Scholar]
  2. P. Radoglou-Grammatikis, P. Sarigiannidis, T. Lagkas, I. Moscholios, A compilation of UAV applications for precision agriculture, Comput. Netw. 172, 107148 (2020) [Google Scholar]
  3. S. Park, Y. Choi, Applications of unmanned aerial vehicles in mining from exploration to reclamation: A review, Minerals 10, 663 (2020) [CrossRef] [Google Scholar]
  4. A. Valsan, B. Parvathy, G.H. Vismaya Dev, R.S. Unnikrishnan, P.K. Reddy, A. Vivek, Unmanned aerial vehicle for search and rescue mission, in: 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) (IEEE, 2020), pp. 684–687 [CrossRef] [Google Scholar]
  5. A. Sigala, B. Langhals, Applications of Unmanned Aerial Systems (UAS): A delphi study projecting future UAS missions and relevant challenges, Drones 4, 8 (2020) [CrossRef] [Google Scholar]
  6. B. Esakki, S. Mathiyazhagan, M. Moses, K.J. Rao, S. Ganesan, Development of 3D-printed floating Quadrotor for collection of algae in remote water bodies, Comput. Electron. Agric. 164, 104891 (2019) [CrossRef] [Google Scholar]
  7. H. Shin, J. Chae, A performance review of collision-free path planning algorithms, Electronics 9, 316 (2020) [CrossRef] [Google Scholar]
  8. S.K. Debnath, R. Omar, S. Bagchi, E.N. Sabudin, M.H.A.S. Kandar, K. Foysol, T.K. Chakraborty, Different cell decomposition path planning methods for unmanned air vehicles-A review, in: Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019, Springer, Singapore, 2020, pp. 99–111 [Google Scholar]
  9. D. González, J. Pérez, V. Milanés, F. Nashashibi, A review of motion planning techniques for automated vehicles, IEEE Trans. Intell. Transp. Syst. 17, 1135–1145 (2015) [Google Scholar]
  10. D.D. Zhu, J.Q. Sun, A new algorithm based on Dijkstra for vehicle path planning considering intersection attribute, IEEE Access 9, 19761–19775 (2021) [CrossRef] [Google Scholar]
  11. S. Wu, Y. Du, Y. Zhang, Mobile robot path planning based on a generalized wavefront algorithm, Math. Prob. Eng. 2020 (2020) [MathSciNet] [Google Scholar]
  12. D.R. Thompson, S. Chien, Y. Chao, P. Li, B. Cahill, J. Levin, O. Schofield et al., Spatiotemporal path planning in strong, dynamic, uncertain currents, in: 2010 IEEE International Conference on Robotics and Automation (IEEE, 2010), pp. 4778–4783 [CrossRef] [Google Scholar]
  13. S.H. Tang, C.F. Yeong, E.L.M. Su, Comparison between normal waveform and modified wavefront path planning algorithm for mobile robot, in: Applied Mechanics and Materials, vol. 607, Trans Tech Publications Ltd, 2014, pp. 778–781 [CrossRef] [Google Scholar]
  14. S. Koenig, M. Likhachev, D. Furcy, Lifelong Planning A*, Artif. Intell. 155, 93–146 (2004) [CrossRef] [Google Scholar]
  15. I. Noreen, A. Khan, Z. Habib, Optimal path planning using memory efficient A*, in: Proceedings of the IEEE International Conference on Frontiers of Information Technology, Islamabad, Pakistan, 19–21 December 2016, pp. 142–146 [Google Scholar]
  16. Z. Bu, R.E. Korf, A*+ BFHS: A hybrid heuristic search algorithm, arXiv preprint arXiv:2103.12701 (2021) [Google Scholar]
  17. K. Daniel, A. Nash, S. Koenig, Theta any-angle path planning on grids, J. Artif. Intell. Res. 39, 533–579 (2010) [CrossRef] [Google Scholar]
  18. C. Wu, X. Huang, Y. Luo, S. Leng, F. Wu, An improved sparse hierarchical lazy theta* algorithm for UAV real-time path planning in unknown three-dimensional environment, in: 2020 IEEE 20th International Conference on Communication Technology (ICCT), IEEE, 2020, pp. 673–677 [CrossRef] [Google Scholar]
  19. D. Ferguson, N. Kalra, A. Stentz, Replanning with RRTs, in: Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, FL, USA, 15–19 May 2006 [Google Scholar]
  20. C.W. Warren, Fast path planning using modified A* method, in: Proceedings of the IEEE International Conference on Robotics and Automation, Atlanta, GA, USA, 2–6 May 1993 [Google Scholar]
  21. A. Botea, M. Muller, J. Schaeffer, Near optimal hierarchical path-finding, J. Game Dev. 1, 1–30 (2004) [Google Scholar]
  22. M. Radmanesh, M. Kumar, P.H. Guentert, M. Sarim, Overview of path-planning and obstacle avoidance algorithms for UAVs: A comparative study, Unmanned Syst. 6, 95–118 (2018) [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.