UAV 3D environment obstacle avoidance trajectory planning based on improved artificial potential field method

Yu, Wenqiang; Yonggeng, LU

doi:10.1088/1742-6596/1885/2/022020

Cited by 11 publications

(5 citation statements)

References 2 publications

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“…As far as the function of UAV avoiding dynamic obstacles in complex environments, 32 the artificial potential field method is widely used. 33,34 However, considering advantages of artificial potential field for local path planning in a simple environment, this article adopts the hierarchical idea for path planning.…”

Section: Hierarchical Planning Approachmentioning

confidence: 99%

3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles

Cao

Wang

Liu

et al. 2022

International Journal of Advanced Robotic Systems

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This research proposes a multifaceted approach of three-dimensional trajectory planning based on the combination of Rapidly-exploring Random Tree–Connect algorithm and artificial potential field method to improve the path search ability and dynamic obstacles avoidance capability of unmanned aerial vehicles. Firstly, an improved method of the target gravity is developed by controlling the sampling range to reduce invalid sampling and speed up the convergence speed of the algorithm so as to lessen the restriction of low efficiency and random sampling of the Rapidly-exploring Random Tree–Connect algorithm. Moreover, the regulation factor is introduced into the artificial potential field method to deal with the problem of target unreachable in the trajectory planning. Then the improved Rapidly-exploring Random Tree–Connect algorithm is implemented to plan the global path in a complex environment. This step is carried out via selecting the local target point on the global path found in the global plan, dividing the complex environment into simple environment and utilizing the artificial potential field method to achieve the effect of avoiding unknown dynamic obstacles in the simple environment. Finally, cubic B-spline is employed to smoothing of the planned trajectory. The simulation results demonstrate that the combination of two improved algorithms improves the path search ability and dynamic barrier avoidance capability of the unmanned aerial vehicles.

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Section: Hierarchical Planning Approachmentioning

confidence: 99%

3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles

Cao

Wang

Liu

et al. 2022

International Journal of Advanced Robotic Systems

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show abstract

“…In [19], the basic potential field function of the traditional artificial potential field method is improved, and the traditional spherical potential field is proposed to be improved to the ellipsoidal potential field. The improved algorithm is compared and simulated in MATLAB in order to address the inefficiency of the traditional artificial potential field method in complex environments for obstacle avoidance.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Avoidance and Path Planning for UAV Using Laguerre Polynomial

2022

IJIES

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Recently, path planning algorithms have been one of the primary and important functions of unmanned aerial vehicles (UAVs). Path planning algorithms in UAVs focused on path length, average path length, computation time, and standard deviation from the mean path length. In spite of this, it faced many difficulties and problems, such as many obstacles, path segmentation, and the increasing number of obstacles and paths in urban environments. This work proposes polynomial functions for path planning and obstacle avoidance. Since it enables us to plan the path in static internal environments, it enables us to plan the path quickly and with less computing time because it does not require high memory and does not require pre-compute of the path. Instead, the route is plotted in real time, Where the appropriate equation is entered into the program, so that the vehicle follows the curve of the entered equation. An accurate data set and metrics were used to measure the efficiency of the proposed method. The experimental results showed a clear improvement in the work of the polynomial function on A*, PSO and genetic algorithms, as this improvement appears very clearly when compared to the computing time, which was reduced by 15% in the method of polynomial functions where the path calculation took only parts of The second, as well as the path length was halved in the polynomial method as the results showed, which reduces the time of battery and memory consumption, the cost of calculating the path and the time to reach the goal.

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“…In [8], Peng Yang et al modified the UAV path planning space by replacing the Cartesian coordinate system with a polar coordinate system and used an estimation of distribution algorithms (EDAs) to search for optimal paths. Many works on UAV path planning has focused on singleobjective optimal path planning problems, either by considering only a single objective or by integrating multiple objectives into a single objective using linear weighting [9]- [11]. However, this approach can be subjective as the decision maker sets the coefficients for the weighting of multiple objectives, which may dramtically affect the outcome of the optimization, while the most suitable paths that perform well on smaller targets may be missed.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning

Yuhang

Zhang

2023

IEEE Access

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This paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems in three dimensions, we suggest an advanced estimation of distribution algorithm. The main idea of this algorithm is to integrate the adaptive deflation of the selection rate, adaptive evolution of the covariance matrix, comprehensive evaluation of individual convergence and diversity, and reference point-based non-dominated ranking. A multi-UAV path planning model involving multi-objective optimization is established, and the designed algorithm is simulated and compared with other three high-dimensional multi-objective optimization algorithms. The results show that the AEMO-EDA proposed in this paper has stronger convergence and wider population distribution diversity in applying to the multi-UAV cooperative path planning model, as well as better global convergence. The algorithm can provide an stable path for each UAV and promote the intelligent operation of the UAV system.

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UAV 3D environment obstacle avoidance trajectory planning based on improved artificial potential field method

Cited by 11 publications

References 2 publications

3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles

3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles

Obstacle Avoidance and Path Planning for UAV Using Laguerre Polynomial

An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning

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