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쿼드콥터의 곡률 기반 3차원 경로 계획 알고리즘

Curvature-based 3D Path Planning Algorithm for Quadcopter

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초록

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.

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.

참고문헌 (8)

  1. Daniel Mellinger, "Minimum snap trajectory generation and control for quadrotors", 2011 IEEE International Conference on Robotics and Automation, pp.2520~2525 (2011), IEEE
  2. Amylia Ait Saadi, "UAV Path Planning Using Optimization Approaches: A Survey", Archives of Computational Methods in Engineering, 29(6), pp.4233~4284 (2022), Springer Science and Business Media LLC[DOI]
  3. Ji Zhang, "Falco: Fast likelihood‐based collision avoidance with extension to human‐guided navigation", Journal of Field Robotics, 37(8), pp.1300~1313 (2020), Wiley[DOI]
  4. Boyu Zhou, "Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight", IEEE Robotics and Automation Letters, 4(4), pp.3529~3536 (2019), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
  5. Jesus Tordesillas, "FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments", IEEE Transactions on Robotics, 38(2), pp.922~938 (2022), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
  6. Michael Blösch, "Vision based MAV navigation in unknown and unstructured environments", 2010 IEEE International Conference on Robotics and Automation (2010), IEEE
  7. Mina Kamel, "Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles", IFAC-PapersOnLine, 50(1), pp.3463~3469 (2017), Elsevier BV[DOI]
  8. Boris Houska, "ACADO toolkit—An open‐source framework for automatic control and dynamic optimization", Optimal Control Applications and Methods, 32(3), pp.298~312 (2011), Wiley[DOI]

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