Journal of Aerospace System Engineering (항공우주시스템공학회지)

The Society for Aerospace System Engineering (항공우주시스템공학회)
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Autonomous Flight System of UAV through Global and Local Path Generation

전역 및 지역 경로 생성을 통한 무인항공기 자율비행 시스템 연구

  • Ko, Ha-Yoon (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Baek, Joong-Hwan (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Choi, Hyung-Sik (Korea Aerospace Research Institute)
  • 고하윤 (한국항공대학교 항공전자정보공학부) ;
  • 백중환 (한국항공대학교 항공전자정보공학부) ;
  • 최형식 (한국항공우주연구원 무인기체계부)
  • Received : 2018.12.11
  • Accepted : 2019.06.01
  • Published : 2019.06.30
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Abstract

In this paper, a global and local flight path system for autonomous flight of the UAV is proposed. The overall system is based on the ROS robot operating system. The UAV in-built computer detects obstacles through 2-D Lidar and generates real-time local path and global path based on VFH and Modified $RRT^*$-Smart, respectively. Additionally, a movement command is issued based on the generated path on the UAV flight controller. The ground station computer receives the obstacle information and generates a 2-D SLAM map, transmits the destination point to the embedded computer, and manages the state of the UAV. The autonomous UAV flight system of the is verified through a simulator and actual flight.

본 논문에서는 무인항공기의 자율 비행을 위한 전역 및 지역 경로 비행 시스템을 제안한다. 전체적인 시스템은 ROS 로봇 운영체제를 기반으로 구축하였다. 무인항공기에 탑재된 임베디드 컴퓨터는 2-D Lidar를 이용하여 장애물을 검출하고, 실시간으로 VFH 기반의 지역 경로와 제안하는 Modified $RRT^*$-Smart 기반의 전역 경로를 생성한다. 또한, 무인항공기의 비행컨트롤러에 Mavros 통신 프로토콜을 이용하여 생성된 경로에 따른 이동 명령을 내린다. 지상국 컴퓨터는 장애물 정보를 수신하여 2-D SLAM 지도를 생성하고, 목적 지점을 임베디드 컴퓨터에 전달하며 무인항공기의 상태를 관장한다. 제안하는 무인항공기의 자율 비행 시스템을 3-D 공간 상의 시뮬레이터 및 실제 비행을 통해 검증하였다.

Keywords

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Fig. 1 Block Diagram of Autonomous Flight System

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Fig. 2 UAV and 2-D Lidar's TF Structure

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Fig. 3 GAZEBO Simulation

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Fig. 4 Rviz 3-D Visualization Tool

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Fig. 5 Local Avoidance Path Generation Based VFH

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Fig. 6 Polar Histogram and Threshold

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Fig. 7 Block Diagram of Modified RRT*-Smart Algorithm

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Fig. 8 RRT*-Smart Algorithm (left), Modified RRT*-Smart Algorithm (right)

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Fig. 9 Global Avoidance Path Generation (left), Local Avoidance Path Generation and Global Avoidance Path Regeneration by Proximity Obstacle (right)

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Fig. 10 Global Path Regeneration

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Fig. 11 UAV Used for Outdoor Flight

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Fig. 12 Outdoor Flight Environment

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Fig. 13 2-D Map and Global Path Flight

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Fig. 14 Local Avoidance Path Generation and Global Avoidance Path Regeneration by Proximity Obstacle (left), Global Avoidance Path Generation (right)

Table 1 Performance Comparison of RRT*-Smart and Modified RRT*-Smart

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Table 2 Main Components and Used Model

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Table 3 Specification of Intel NUC Embedded Computer

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References

  1. P. Narayan, P. Meyer, and D. Campbell, "Embedding human expert cognition into autonomous UAS trajectory planning." IEEE transactions on cybernetics, vol. 43, no. 2, pp. 530-543, April 2013. https://doi.org/10.1109/TSMCB.2012.2211349
  2. Hanseok Ryu1, Heejae Byun, Sanghyuk Park, "Efficient Path Planning for Long Term Solar UAV Flight," Journal of Aerospace System Engineering, Vol.8, No.4, pp.32-38, December, 2014. https://doi.org/10.20910/JASE.2014.8.4.032
  3. M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote, J. Leibs, E. Berger, R. Wheeler, and A. Ng "ROS: an open-source Robot Operating System.", In ICRA Workshop on Open Source Software, vol. 3, no. 3.2, pp. 1-6, 2009.
  4. J. Meyer, A. Sendobry, S. Kohlbrecher, U. Klingauf, and O. V. Stryk, "Comprehensive simulation of quadrotor uavs using ros and gazebo.", In International conference on simulation, modeling, and programming for autonomous robots, Tsukuba, Japan, pp. 400-411, November, 2012.
  5. S. Kohlbrecher, J. Meyer, T. Graber, K. Petersen, U. Klingauf, and O. V. Stryk "HECTOR open source modules for autonomous mapping and navigation with rescue robots.", Robot Soccer World Cup, 17th ed. Heidelberg: Springer, 2013.
  6. Z. Wu, and L. Feng, "Obstacle prediction-based dynamic path planning for a mobile robot.", International Journal of Advancements in Computing Technology, vol. 4, no. 3, pp. 118-124, May 2012. https://doi.org/10.4156/ijact.vol4.issue3.16
  7. A. Babinec, F. Duchon, M. Dekan, P. Paszto, and M. Kelemen, "VFH* TDT (VFH* with Time Dependent Tree): A new laser rangefinder based obstacle avoidance method designed for environment with non-static obstacles.", Robotics and autonomous systems, vol. 62, no. 8, pp. 1098-1115, 2014. https://doi.org/10.1016/j.robot.2014.05.003
  8. M. S. LaValle, "Rapidly-exploring random trees: A new tool for path planning.", 1998.
  9. F. Islam, J. Nasir, U. Malic, Y. Ayaz, and O. Hasan, "RRT*-Smart: Rapid convergence implementation of RRT* towards optimal solution", In International Conference Mechatronics and Automation(ICMA), Chengdu: China, pp. 1651-1656, Aug 2012.
  10. K. Wei, and B. Ren. "A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm.", Sensors, vol. 18, no. 2, Feb 2018.
  11. J. Farinella, C. Clayton, and S. Bhandari, "UAV Collision Avoidance using a Predictive Rapidly-Exploring Random Tree (RRT).", Proceedings of the AIAA Infotech@ Aerospace, San Diego: CA, USA, pp. 4-8, 2016.