4.7 Article

Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment

Journal

AEROSPACE SCIENCE AND TECHNOLOGY
Volume 47, Issue -, Pages 269-279

Publisher

ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
DOI: 10.1016/j.ast.2015.09.037

Keywords

Unmanned aerial vehicle (UAV); Three-dimensional (3D) real-time path planning; Lyapunov Guidance Vector Field (LGVF); Interfered Fluid Dynamical System (IFDS); Varying receding-horizon optimization

Funding

  1. National Natural Science Foundation of China [61175084]
  2. Program for Changjiang Scholars and Innovative Research Team in University [IRT13004]

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According to the tactical requirements of unmanned aerial vehicle (UAV) for tracking target and avoiding obstacle in complex dynamic environment, a three-dimensional (3D) real-time path planning method is proposed by combing the improved Lyapunov Guidance Vector Field (LGVF), the Interfered Fluid Dynamical System (IFDS) and the strategy of varying receding-horizon optimization from Model Predictive Control (MPC). First, in order to track the moving target in 3D environment, the LGVF method is improved by introducing flight height into the traditional Lyapunov function, and the generated velocity can guide UAV converge gradually to the limit cycle in horizontal plane and the optimal height in vertical plane. Then, the IFDS method imitating the phenomenon of fluid flow is utilized to plan the collision-free path. To achieve the mission of tracking moving target and avoid static or dynamic obstacle at the same time, the guidance vector field by LGVF is taken as the original fluid of IFDS. As the fluid system still remains stable under the influence of obstacles, the disturbed streamline from the interfered fluid can be regarded as the planned path. Third, as the quality of route is mainly influenced by the repulsive and tangential parameters of IFDS, the real-time suboptimal route can be planned by the varying receding-horizon optimization according to the predicted motion. The experimental results prove that the proposed hybrid method is applicable to various dynamic environments. (C) 2015 Elsevier Masson SAS. All rights reserved.

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