UAV path planning using potential field and modified receding horizon A* 3D algorithm

Khuswendi, Taufik; Hindersah, Hilwadi; Adiprawita, Widyawardana

doi:10.1109/iceei.2011.6021579

Cited by 27 publications

(14 citation statements)

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“…In a second step, they define virtual points equipped with attractive forces that help the UAV to avoid local minima points. In [12] the authors present an autonomous navigation system for UAVs. Their method is based on a combination of virtual potential fields and the A* algorithm.…”

Section: The State Of the Artmentioning

confidence: 99%

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UAV-Based Data Gathering Using An Artificial Potential Fields Approach

Tazibt

Achir

Mühlethaler

et al. 2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: The State Of the Artmentioning

confidence: 99%

“…UAV path Planning has been used (combined) with many other methods such as Genetic Algorithms [13], A* Algorithms [12] and the Artificial Potential Field (APF) [16]. The APF method is commonly used in path planning because of its concise mathematical description and the suitability for the real time control [14] [6].…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Data Gathering Using An Artificial Potential Fields Approach

Tazibt

Achir

Mühlethaler

et al. 2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

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“…Some representative techniques used in path planning methods and based on continuous and discrete environment sampling include: RRT (rapidly-exploring random tree) [15][16][17][18]; PRM (probabilistic road maps) [19][20][21][22][23]; Voronoi diagrams [24][25][26]; and artificial potential [27][28][29][30]. Nevertheless, it is important to note that RRT and PRM make random explorations (continuous sampling) of the defined environment.…”

Section: Introductionmentioning

confidence: 99%

Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

et al. 2019

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A relevant task in unmanned aerial vehicles (UAV) flight is path planning in 3 D environments. This task must be completed using the least possible computing time. The aim of this article is to combine methodologies to optimise the task in time and offer a complete 3 D trajectory. The flight environment will be considered as a 3 D adaptive discrete mesh, where grids are created with minimal refinement in the search for collision-free spaces. The proposed path planning algorithm for UAV saves computational time and memory resources compared with classical techniques. With the construction of the discrete meshing, a cost response methodology is applied as a discrete deterministic finite automaton (DDFA). A set of optimal partial responses, calculated recursively, indicates the collision-free spaces in the final path for the UAV flight.

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“…The set of control points that define the collision-free space is calculated using specific path planning methods based on continuous and discrete environment sampling. Some examples of these techniques are: the rapidly-exploring random tree (RRT) [20][21][22][23]; probabilistic road maps (PRM) [24][25][26][27][28]; heuristic planners (genetic algorithms-GA) [29,30]; swarm intelligence [31][32][33][34]; fuzzy logic [35,36]); Voronoi diagrams [37][38][39]; artificial potential [40][41][42][43]; and recursive rewarding modified adaptive cell decomposition (RR-MACD) [44].…”

Section: Introductionmentioning

confidence: 99%

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

et al. 2019

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Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.

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UAV path planning using potential field and modified receding horizon A* 3D algorithm

Cited by 27 publications

References 1 publication

UAV-Based Data Gathering Using An Artificial Potential Fields Approach

UAV-Based Data Gathering Using An Artificial Potential Fields Approach

Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

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