Journal of Information Processing Systems

  • 제16권6호
  • /
  • Pages.1324-1342
  • /
  • 2020
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

Boundary-RRT* Algorithm for Drone Collision Avoidance and Interleaved Path Re-planning

  • Park, Je-Kwan (Dept. of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Chung, Tai-Myoung (College of Information and Communication Engineering, Sungkyunkwan University)
  • 투고 : 2020.05.28
  • 심사 : 2020.10.22
  • 발행 : 2020.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Various modified algorithms of rapidly-exploring random tree (RRT) have been previously proposed. However, compared to the RRT algorithm for collision avoidance with global and static obstacles, it is not easy to find a collision avoidance and local path re-planning algorithm for dynamic obstacles based on the RRT algorithm. In this study, we propose boundary-RRT*, a novel-algorithm that can be applied to aerial vehicles for collision avoidance and path re-planning in a three-dimensional environment. The algorithm not only bounds the configuration space, but it also includes an implicit bias for the bounded configuration space. Therefore, it can create a path with a natural curvature without defining a bias function. Furthermore, the exploring space is reduced to a half-torus by combining it with simple right-of-way rules. When defining the distance as a cost, the proposed algorithm through numerical analysis shows that the standard deviation (σ) approaches 0 as the number of samples per unit time increases and the length of epsilon ε (maximum length of an edge in the tree) decreases. This means that a stable waypoint list can be generated using the proposed algorithm. Therefore, by increasing real-time performance through simple calculation and the boundary of the configuration space, the algorithm proved to be suitable for collision avoidance of aerial vehicles and replanning of local paths.

키워드

참고문헌

  1. P. Fiorini and Z. Shiller, "Motion planning in dynamic environments using velocity obstacles," The International Journal of Robotics Research, vol. 17, no. 7, pp. 760-772, 1998. https://doi.org/10.1177/027836499801700706
  2. J. A. Douthwaite, S. Zhao, and S. Mihaylova, "Velocity obstacle approaches for multi-agent collision avoidance," Unmanned Systems, vol. 7, no. 1, pp. 55-64, 2019. https://doi.org/10.1142/s2301385019400065
  3. Y. I. Jenie, E. J. van Kampen, C. C. de Visser, J. Ellerbroek, and J. M. Hoekstra, "Three-dimensional velocity obstacle method for uncoordinated avoidance maneuvers of unmanned aerial vehicles," Journal of Guidance, Control, and Dynamics, vol. 39, no. 10, pp. 2312-2323, 2016. https://doi.org/10.2514/1.G001715
  4. X. Yang, Y. Zhang, and W. Zhou, "Obstacle avoidance method of three-dimensional obstacle spherical cap," Journal of Systems Engineering and Electronics, vol. 29, no. 5, pp. 1058-1068, 2018. https://doi.org/10.21629/JSEE.2018.05.16
  5. H. S. Park, K. H. Choi, and K. H. Chung, "Test-case generation for Simulink/Stateflow Model using a separated RRT space," KIPS Transactions on Software and Data Engineering, vol. 2, no. 7, pp. 471-478, 2013. https://doi.org/10.3745/KTSDE.2013.2.7.471
  6. M. Cap, P. Novak, J. Vokrinek, and M. Pechoucek, "Multi-agent RRT: sampling-based cooperative pathfinding," in Proceedings of International conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Saint Paul, MN, 2013, pp. 1263-1264.
  7. J. Nasir, F. Islam, U. Malik, Y. Ayaz, O. Hasan, M. Khan, and M. S. Muhammad, "RRT*-SMART: a rapid convergence implementation of RRT," International Journal of Advanced Robotic Systems, vol. 10, no. 7, article no. 299, 2013.
  8. C. Urmson and R. Simmons, "Approaches for heuristically biasing RRT growth," in Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453), Las Vegas, NV, 2003, pp. 1178-1183.
  9. 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 Proceedings of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, 2014, pp. 2997-3004.
  10. X. Wu, L. Xu, R. Zhen, and X. Wu, "Biased sampling potentially guided intelligent bidirectional RRT* algorithm for UAV path planning in 3D environment," Mathematical Problems in Engineering, vol. 2019, article no. 5157403, 2019.
  11. K. Naderi, J. Rajamaki, and P. Hamalainen, "RT-RRT* a real-time path planning algorithm based on RRT," in Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games, Paris, France, 2015, pp. 113-118.
  12. P. Yao, H. Wang, and Z. Su, "Hybrid UAV path planning based on interfered fluid dynamical system and improved RRT," in Proceedings of the 41st Annual Conference of the IEEE Industrial Electronics Society (IECON), Yokohama, Japan, 2015, pp. 000829-000834.
  13. S. Karaman and E. Frazzoli, "Sampling-based algorithms for optimal motion planning," The International Journal of Robotics Research, vol. 30, no. 7, pp. 846-894, 2011. https://doi.org/10.1177/0278364911406761
  14. J. Munkres, "Algorithms for the assignment and transportation problems," Journal of the Society for Industrial and Applied Mathematics, vol. 5, no. 1, pp. 32-38, 1957. https://doi.org/10.1137/0105003
  15. Z. Shi and W. K. Ng, "A collision-free path planning algorithm for unmanned aerial vehicle delivery," in Proceedings of 2018 International Conference on Unmanned Aircraft Systems (ICUAS), Dallas, TX, 2018, pp. 358-362.
  16. S. M. LaValle and J. J. Kuffner, "Randomized kinodynamic planning," in Proceedings of 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C), Detroit, MI, 1999, pp. 473-479.
  17. S. M. LaValle, "Rapidly-exploring random trees: a new tool for path planning," Department of Computer Science, Iowa State University, Ames, IA, 1998.
  18. Y. B. Chen, G. C. Luo, Y. S. Mei, J. Q. Yu, and X. L. Su, "UAV path planning using artificial potential field method updated by optimal control theory," International Journal of Systems Science, vol. 47, no. 6, pp. 1407-1420, 2016. https://doi.org/10.1080/00207721.2014.929191
  19. M. U. Farooq, Z. Zhang, and M. Ejaz, "Quadrotor UAVs flying formation reconfiguration with collision avoidance using probabilistic roadmap algorithm," in Proceedings of 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC), Dalian, China, 2017, pp. 866-870.
  20. L. G. D. Veras, F. L. Medeiros, and L. N. Guimaraes, "systematic literature review of sampling process in rapidly-exploring random trees," IEEE Access, vol. 7, pp. 50933-50953, 2019. https://doi.org/10.1109/access.2019.2908100
  21. D. Alejo, J. M. Diaz-Banez, J. A. Cobano, P. Perez-Lantero, and A. Ollero, "The velocity assignment problem for conflict resolution with multiple aerial vehicles sharing airspace," Journal of Intelligent & Robotic Systems, vol. 69, no. 1-4, pp. 331-346, 2013. https://doi.org/10.1007/s10846-012-9768-4
  22. J. J. Rebollo, A. Ollero, and I. Maza, "Collision avoidance among multiple aerial robots and other noncooperative aircraft based on velocity planning," in Proceedings of the 7th Conference on Mobile Robots and Competitions, Paderne, Portugal, 2007.
  23. J. Van den Berg, M. Lin, and D. Manocha, "Reciprocal velocity obstacles for real-time multi-agent navigation," in Proceedings of 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, 2008, pp. 1928-1935.
  24. Federal Aviation Administration, "Aerodynamics of flight," in Pilot's Handbook of Aeronautical Knowledge. Washington, DC: Federal Aviation Administration, 2016, pp. 5-38.
  25. Federal Aviation Administration, Department of Transportation, "Section 91.113: Right-of-way rules: except water operations," 2004 [Online]. Available: https://www.law.cornell.edu/cfr/text/14/91.113.
  26. Y. I. Jenie, E. J. van Kampen, C. C. de Visser, J. Ellerbroek, and J. M. Hoekstra, "Selective velocity obstacle method for deconflicting maneuvers applied to unmanned aerial vehicles," Journal of Guidance, Control, and Dynamics, vol. 38, no. 6, pp. 1140-1146, 2015. https://doi.org/10.2514/1.G000737
  27. J. Farmer, "The volume of a torus using cylindrical and spherical coordinates," Australian Senior Mathematics Journal, vol. 19, no. 2, pp. 49-58, 2005.
  28. L. Liu, R. Shi, S. Li, and J. Wu, "Path planning for UAVS based on improved artificial potential field method through changing the repulsive potential function," in Proceedings of 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), Nanjing, China, 2016, pp. 2011-2015.
  29. L. Wang, J. Kan, J. Guo, and C. Wang, "Improved ant colony optimization for ground robot 3D path planning," in Proceedings of 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Zhengzhou, China, 2018, pp. 106-1066.
  30. M. Radmanesh and M. Kumar, "Flight formation of UAVs in presence of moving obstacles using fast-dynamic mixed integer linear programming," Aerospace Science and Technology, vol. 50, pp. 149-160, 2016. https://doi.org/10.1016/j.ast.2015.12.021