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Curvature-based 3D Path Planning Algorithm for Quadcopter

쿼드콥터의 곡률 기반 3차원 경로 계획 알고리즘

  • Received : 2023.06.07
  • Accepted : 2023.07.26
  • Published : 2023.08.31

Abstract

The increasing popularity of autonomous unmanned aerial vehicles (UAVs) can be attributed to their wide range of applications. 3D path planning is one of the crucial components enabling autonomous flight. In this paper, we present a novel 3D path planning algorithm that generates and utilizes curvature-based trajectories. Our approach leverages circular properties, offering notable advantages. First, circular trajectories make collision detection easier. Second, the planning procedure is streamlined by eliminating the need for the spline process to generate dynamically feasible trajectories. To validate our proposed algorithm, we conducted simulations in Gazebo Simulator. Within the simulation, we placed various obstacles such as pillars, nets, trees, and walls. The results demonstrate the efficacy and potential of our proposed algorithm in facilitating efficient and reliable 3D path planning for UAVs.

Keywords

Acknowledgement

This project was financially supported by the Institute of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government under grant No.UM22206RD2

References

  1. D. Mellinger and V. Kumar, "Minimum Snap Trajectory Generation and Control for Quadrotors," IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, pp. 2520-2525, 2011, DOI: 10.1109/ICRA.2011.5980409. 
  2. A. A. Saadi, A. Soukane, Y. Meraihi, A. B. Gabis, S. Mirjalili, and A. Ramdane-Cherif, "UAV Path Planning Using Optimization Approaches: A Survey," Archives of Computational Methods in Engineering, vol. 29, pp. 4233-4284, Apr., 2022, DOI: 10.1007/s11831-022-09742-7. 
  3. J. Zhang, C. Hu, R. G. Chadha, and S. Singh, "Falco: Fast Likelihood-based Collision Avoidance with Extension to Human-guided Navigation," Journal of Field Robotics, vol. 37, no. 8, pp. 1300-1313, Dec., 2020, DOI: 10.1002/rob.21952. 
  4. B. Zhou, F. Gao, L. Wang, C. Liu, and S. Shen, "Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight," IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3529-3536, Oct., 2019, DOI: 10.1109/LRA.2019.2927938. 
  5. J. Tordesillas, B. T. Lopez, M. Everett, and J. P. How, "Faster: Fast and safe trajectory planner for navigation in unknown environments," IEEE Transactions on Robotics, vol. 38, no. 2, pp. 922-938, Apr., 2022, DOI: 10.1109/TRO.2021.3100142. 
  6. M. Blosch, S. Weiss, D. Scaramuzza, and R. Siegwart, "Vision based MAV navigation in unknown and unstructured environments," IEEE International Conference on Robotics and Automation (ICRA), Anchorage, USA, 2010, DOI: 10.1109/ROBOT.2010.5509920. 
  7. M. Kamel, M. Burri, and R. Siegwart, "Linear vs nonlinear MPC for trajectory tracking applied to rotary wing micro aerial vehicles," IFAC-PapersOnLine, vol. 50, no. 1, pp. 3463-3469, Jul., 2017, DOI: 10.1016/j.ifacol.2017.08.849. 
  8. B. Houska, H. J. Ferreau, and M. Diehl, "ACADO Toolkit-An open-source framework for automatic control and dynamic optimization," Optimal Control Applications and Methods, vol. 32, no. 3, pp. 298-312, May, 2011, DOI: 10.1002/oca.939.