로봇학회논문지 (The Journal of Korea Robotics Society)

  • 제18권1호
  • /
  • Pages.1-9
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  • 2023
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  • 1975-6291(pISSN)
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  • 2287-3961(eISSN)
한국로봇학회 (Korea Robotics Society)
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다중 무인 항공기 이용 감시 및 탐색 경로 계획 생성

Path Planning for Search and Surveillance of Multiple Unmanned Aerial Vehicles

  • Sanha Lee (Department of Mechanical Engineering, Ulsan National Institute of Science and Technology) ;
  • Wonmo Chung (Department of Mechanical Engineering, Ulsan National Institute of Science and Technology) ;
  • Myunggun Kim (Department of Mechanical Engineering, Ulsan National Institute of Science and Technology) ;
  • Sang-Pill Lee (LIG NEX1) ;
  • Choong-Hee Lee (LIG NEX1) ;
  • Shingu Kim (LIG NEX1) ;
  • Hungsun Son (Department of Mechanical Engineering, Ulsan National Institute of Science and Technology)
  • 투고 : 2022.09.02
  • 심사 : 2022.11.14
  • 발행 : 2023.02.28
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초록

This paper presents an optimal path planning strategy for aerial searching and surveying of a user-designated area using multiple Unmanned Aerial Vehicles (UAVs). The method is designed to deal with a single unseparated polygonal area, regardless of polygonal convexity. By defining the search area into a set of grids, the algorithm enables UAVs to completely search without leaving unsearched space. The presented strategy consists of two main algorithmic steps: cellular decomposition and path planning stages. The cellular decomposition method divides the area to designate a conflict-free subsearch-space to an individual UAV, while accounting the assigned flight velocity, take-off and landing positions. Then, the path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the vehicle-starting position, and it recursively updates the nearest endpoint set to generate the shortest path. The path planning policy produces four path candidates by alternating the starting point (left or right edge), and the travel direction (vertical or horizontal). The optimal-selection policy is enforced to maximize the search efficiency, which is time dependent; the policy imposes the total path-length and turning number criteria per candidate. The results demonstrate that the proposed cellular decomposition method improves the search-time efficiency. In addition, the candidate selection enhances the algorithmic efficacy toward further mission time-duration reduction. The method shows robustness against both convex and non-convex shaped search area.

키워드

과제정보

This project was funded by LIG NEX1 (UC190053ID, 2.200913) under Data link and Ground Control Software Standardization for Small UAVs

참고문헌

  1. D. Murugan, A. Garg, and D. Singh, "Development of an Adaptive Approach for Precision Agriculture Monitoring with Drone and Satellite Data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 12, pp. 5322-5328, Dec., 2017, DOI: 10.1109/JSTARS.2017.2746185. 
  2. J. J. Roldan-Gomez, E. Gonzalez-Gironda, and A. Barrientos, "A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters' Efficiency and Safety," Applied Sciences, vol. 11, no. 1, pp. 363, Jan., 2021, DOI: 10.3390/app11010363. 
  3. G. Silano, T. Baca, R. Penicka, D. Liuzza, and M. Saska, "Power Line Inspection Tasks with Multi-Arial Robot Systems via Signal Temporal Logic Specifications," IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 4169-4176, Apr., 2021, DOI: 10.1109/LRA.2021.3068114. 
  4. D. C. Schedl, I. Kurmi, and O. Bimber, "An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioning," Science Robotics, vol. 6, no. 55, Jun., 2021, DOI: 10.1126/scirobotics.abg1188. 
  5. H. Choset, E. Acar, A. A. Rizzi, and J. Luntz, "Exact Cellular Decompositions in Terms of Critical Points of Morse Functions," IEEE International Conference on Robotics and Automation (ICRA), San Francisco, USA, 2000, DOI: 10.1109/ROBOT.2000.846365. 
  6. Y. Bouzid, Y. Bestaoui, and H. Siguerdidjane, "Quadrotor-UAV Optimal Coverage Path Planning in Cluttered Environment with a Limited Onboard Energy," IEEE International Workshop on Intelligent Robots and Systems (IROS), Vancouver, Canada, pp. 979~984, 2017, DOI: 10.1109/IROS.2017.8202264. 
  7. F. Balampanis, I. Maza, and A. Ollero, "Spiral-like Coverage Path Planning for Multiple Heterogeneous UAS Operating in Coastal Regions," International Conference on Unmanned Aircraft Systems (ICUAS), Miami, USA, 2017, DOI: 10.1109/ICUAS.2017.7991461. 
  8. H. Choset and P. Pignon, "Coverage Path Planning: The Boustrophedon Cellular Decomposition," Field and Service Robotics, Springer-Verlag London Limited, 1998, pp. 203-209, DOI: 10.1007/978-1-4471-1273-0_32. 
  9. T. M. Cabreira, P. R. Ferreira, C. D. Franco, and G. C. Buttazzo, "Grid-based Coverage Path Planning with Minimum Energy over Irregular-shaped Areas with UAVs," International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, USA, 2019, DOI: 10.1109/ICUAS.2019.8797937. 
  10. C. Wu, C. Dai, X. Gong, Y. Liu, J. Wang, X. D. Gu, and C. C. L. Wang, "Energy-efficient Coverage Path Planning for General Terrain Surfaces," IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2584-2591, Jul., 2019, DOI: 10.1109/LRA.2019.2899920. 
  11. C. D. Franco and G. Buttazzo, "Energy-aware Coverage Path Planning of UAVs," IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Vila Real, Portugal, 2015, DOI: 10.1109/ICARSC.2015.17. 
  12. L. H. Nam, L. Huang, X. J. Li, and J. F. Xu, "An Approach for Coverage Path Planning for UAVs," International Workshop on Advanced Motion Control (AMC), Auckland, New Zealand, 2016, DOI: 10.1109/AMC.2016.7496385. 
  13. M. Torres, D. A. Pelta, J. L. Verdegay, and J. C. Torres, "Coverage Path Planning with Unmanned Aerial Vehicles for 3D Terrain Construction," Expert Systems with Applications, vol. 55, no. 15, pp. 441-451, Aug., 2016, DOI: 10.1016/j.eswa.2016.02.007. 
  14. E. Gonzalez, O. Alvarez, Y. Diaz, C. Parra, and C. Bustacara, "BSA: A Complete Coverage Algorithm," IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, 2005, DOI: 10.1109/ROBOT.2005.1570413. 
  15. Y. Hong, S. Jung, S. Kim, and J. Cha, "Autonomous Mission of Multi-UAV for Optimal Area Coverage," Sensors, vol. 21, no. 7, Apr., 2021, DOI: 10.3390/s21072482.