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A Collaborative Hierarchical Online Motion Planner for UAV in an Uncertainty Environment
Hierarchical Motion Planning
Rapidly-exploring Random Tree
|Issue Date:||2021-07-01 19:57:18 (UTC+8)|
Path planning has always been an important topic in the research of UAV automated navigation. The purpose of this work is to consider the safety and efficiency of UAVs when planning and updating the path in real-time during the movement such that the UAV can reach the goal configuration successfully. Global path planning usually requires a great amount of computing for complex or large areas, which may be beyond the onboard computing power of many UAVs. Even if the computation can be done on board, the long planning time may not guarantee the safety of UAV navigation. In this paper, we investigate the pros and cons of various path planning algorithms and choose Rapidly-exploring Random Tree (RRT) as a base planning algorithm for UAVs. We proposed a collaborative hierarchical computing architecture, which uses asynchronous cooperative planning of the computing resources onboard and at the base station. Our architecture aims to tackle the difficulty in single-layer path planning where the immediacy of planning and the optimality of the path can not be ensured at the same time. Our system can be used to plan the path for a UAV in an uncertain environment in real-time and ensure its safety during the flight and the effectiveness of the output path. We have conducted experiments in simulation for a typical UAV under four different environmental conditions. The experimental results show that our method can effectively reduce flight time or path length and choose a more downwind path if the wind field information is provided.
|Reference:|| Wikipedia. "Unmanned aerial vehicle." Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Unmanned_aerial_vehicle&oldid=955084723 (accessed 7 May 2020 05:46 UTC.|
 Wikipedia. "Motion planning." Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Motion_planning&oldid=950519176 (accessed 7 May 2020 05:25 UTC.
 H. Yang, Q. Jia, and W. Zhang, "An Environmental Potential Field Based RRT Algorithm for UAV Path Planning," in 2018 37th Chinese Control Conference (CCC), 2018: IEEE, pp. 9922-9927.
 L. Yang, J. Qi, J. Xiao, and X. Yong, "A literature review of UAV 3D path planning," in Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014: IEEE, pp. 2376-2381.
 G. A. Thanellas, V. C. Moulianitis, and N. A. Aspragathos, "A spatially wind aware quadcopter (UAV) path planning approach," IFAC-PapersOnLine, vol. 52, no. 8, pp. 283-288, 2019/01/01/ 2019, doi: https://doi.org/10.1016/j.ifacol.2019.08.084.
 K. Yang, S. Keat Gan, and S. Sukkarieh, "A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV," Advanced Robotics, vol. 27, no. 6, pp. 431-443, 2013.
 W. Zu, G. Fan, Y. Gao, Y. Ma, H. Zhang, and H. Zeng, "Multi-uavs cooperative path planning method based on improved rrt algorithm," in 2018 IEEE International Conference on Mechatronics and Automation (ICMA), 2018: IEEE, pp. 1563-1567.
 L. De Filippis, G. Guglieri, and F. Quagliotti, "A minimum risk approach for path planning of UAVs," Journal of Intelligent & Robotic Systems, vol. 61, no. 1-4, pp. 203-219, 2011.
 A. González-Sieira, M. Mucientes, and A. Bugarín, "Motion planning under uncertainty in graduated fidelity lattices," Robotics and Autonomous Systems, vol. 109, pp. 168-182, 2018.
 F. Li, S. Zlatanova, M. Koopman, X. Bai, and A. Diakité, "Universal path planning for an indoor drone," Automation in Construction, vol. 95, pp. 275-283, 2018.
 Z. Beck, W. T. L. Teacy, A. Rogers, and N. R. Jennings, "Collaborative online planning for automated victim search in disaster response," Robotics and Autonomous Systems, vol. 100, pp. 251-266, 2018/02/01/ 2018, doi: https://doi.org/10.1016/j.robot.2017.09.014.
 S. M. LaValle, J. J. Kuffner, and B. Donald, "Rapidly-exploring random trees: Progress and prospects," Algorithmic and computational robotics: new directions, no. 5, pp. 293-308, 2001.
 S. Karaman and E. Frazzoli, "Incremental sampling-based algorithms for optimal motion planning," Robotics Science and Systems VI, vol. 104, no. 2, 2010.
 J. J. Kuffner and S. M. LaValle, "RRT-connect: An efficient approach to single-query path planning," in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), 2000, vol. 2: IEEE, pp. 995-1001.
 T.-Y. Li and Y.-C. Shie, "An incremental learning approach to motion planning with roadmap management," in Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), 2002, vol. 4: IEEE, pp. 3411-3416.
 K. Liang, Z. Chun-xia, and G. Jian-hui, "Path Planning Based on Fuzzy Rolling Rapidly-exploring Random Tree for Mobile Robot," School of Computer Science and Technology,NUST,Nanjing 210094,China, vol. 34, no. 5, pp. 642-648, 2010.
 A. Ravankar, A. A. Ravankar, Y. Kobayashi, Y. Hoshino, and C. Peng, "Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges," Sensors (Basel, Switzerland), vol. 18, 2018.
 Wikipedia. "Robot Operating System." Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Robot_Operating_System&oldid=985900454 (accessed 4 November 2020 02:47 UTC.
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