DOI QR코드

DOI QR Code

Survey on Developing Path Planning for Unmanned Aerial Vehicles

무인비행체 경로계획 기술 동향

  • Y.S. Kwon ;
  • J.H. Cha
  • 권용선 (자율비행연구실) ;
  • 차지훈 (자율비행연구실)
  • Published : 2024.08.01

Abstract

Recent advancements in autonomous flight technologies for Unmanned Aerial Vehicles (UAVs) have greatly expanded their applicability for various tasks, including delivery, agriculture, and rescue. This article presents a comprehensive survey of path planning techniques in autonomous navigation and exploration that are tailored for UAVs. The robotics literature has studied path and motion planning, from basic obstacle avoidance to sophisticated algorithms capable of dynamic decision-making in challenging environments. In this article, we introduce popular path and motion planning approaches such as grid-based, sampling-based, and optimization-based planners. We further describe the contributions from the state-of-the-art in exploration planning for UAVs, which have been derived from these well-studied planners. Recent research, including the method we are developing, has improved performance in terms of efficiency and scalability for exploration tasks in challenging environments without human intervention. On the basis of these research and development trends, this article discusses future directions in UAV path planning technologies, illustrating the potential for UAVs to perform complex tasks with increased autonomy and efficiency.

Keywords

Acknowledgement

본 논문은 한국전자통신연구원 연구개발지원사업의 일환으로 수행되었음[2022-0-00021, 골든타임 확보를 위한 실종자 수색 다수 드론 자율비행 핵심기술 개발].

References

  1. https://builtin.com/articles/drone-delivery-companies
  2. Forbes, "Farm with a view: How drone technology is taking agriculture to a new level," 2023. 2. 23.
  3. Telenor, "When every second counts: Mobile 5G solution could be a game changer in emergency situations," 2022. 11. 8.
  4. 김수성, 정성구, 차지훈, "드론 자율비행 기술 동향," 전자통신동향분석, 제36권 제2호, 2021, pp. 1-11. https://doi.org/10.22648/ETRI.2021.J.360201
  5. S.M. LaValle, Planning Algorithms, Cambridge University Press, 2006.
  6. O. Khatib, "Real-time obstacle avoidance for manipulators and mobile robots," Int. J. Robot. Res., vol. 5, no. 1, 1986, pp. 90-98. https://doi.org/10.1177/027836498600500106
  7. E.W. Dijkstra, "A note on two problems in connexion with graphs," vol. 1, 1959, pp. 269-271.
  8. P.E. Hart et al., "A formal basis for the heuristic determination of minimum cost paths," IEEE Trans. Syst. Sci. Cybern., vol. 4, no. 2, 1968, pp. 100-107. https://doi.org/10.1109/TSSC.1968.300136
  9. L.E. Kavraki et al., "Probabilistic roadmaps for path planning in high-dimensional configuration spaces," IEEE Trans. Robot. Autom., vol. 12, no. 4, 1996, pp. 566-580. https://doi.org/10.1109/70.508439
  10. S. LaValle, "Rapidly-exploring random trees: A new tool for path planning," Technical Report, Computer Science Department, Iowa State University, 1998.
  11. S. Karaman and E. Frazzoli, "Sampling-based algorithms for optimal motion planning," Int. J. Robot. Res., vol. 30, no. 7, 2011, pp. 846-894. https://doi.org/10.1177/0278364911406761
  12. M. Zucker et al., "CHOMP: Covariant hamiltonian optimization for motion planning," Int. J. Robot. Res., vol. 32, no. 9, 2013, pp. 1164-1193. https://doi.org/10.1177/0278364913488805
  13. A. Bircher et al., "Receding horizon "next-best-view" planner for 3d exploration," in Proc. ICRA, (Stockholm, Sweden), May 2016.
  14. T. Dang et al., "Graph-based subterranean exploration path planning using aerial and legged robots," J. Field Robot., vol. 37, no. 8, 2020, pp. 1363-1388. https://doi.org/10.1002/rob.21993
  15. M. Kulkarni et al., "Autonomous teamed exploration of subterranean environments using legged and aerial robots," in Proc. ICRA, (Philadelphia, PA, USA), May 2022.
  16. B. Zhou et al., "FUEL: Fast UAV exploration using incremental frontier structure and hierarchical planning," IEEE Robot. Autom. Lett., vol. 6, no. 2, 2021, pp. 779-786. https://doi.org/10.1109/LRA.2021.3051563
  17. B. Zhou et al., "RACER: Rapid collaborative exploration with a decentralized multi-uav system," IEEE Trans. Robot., vol. 39, no. 3, 2023, pp. 1816-1835.
  18. T. Tian et al., "Search and rescue under the forest canopy using multiple UAVs," Int. J. Robot. Res., vol. 39, no. 10-11, 2020, pp. 1201-1221. https://doi.org/10.1177/0278364920929398
  19. Bartolomei et al., "Fast multi-UAV decentralized exploration of forests," IEEE Robot. Autom. Lett., vol. 8, no. 9, 2023, pp. 5576-5583. https://doi.org/10.1109/LRA.2023.3296037
  20. A. Stentz, "Optimal and efficient path planning for partially-known environments," in Proc. ICRA, (San Diego, CA, USA), May 1994.
  21. R. Bohlin and L.E. Kavraki, "Path planning using lazy PRM," in Proc. ICRA, (San Francisco, CA, USA), Apr. 2000.
  22. J.D. Gammell, S.S. Srinivasa, and T.D. Barfoot, "Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic," in Proc. IEEE/RSJ IROS, (Chicago, IL, USA), Sept. 2014.
  23. J. Schulman et al., "Finding locally optimal, collisionfree trajectories with sequential convex optimization," in Proc. RSS IX, (Berlin, Germany), June 2013.