The UAV dynamic path planning algorithm research based on Voronoi diagram

Xia, Chen; Chen, Xiangmin

doi:10.1109/ccdc.2014.6852323

Cited by 43 publications

(18 citation statements)

References 1 publication

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“…The first one is the graphbased algorithm, such as the Voronoi diagram, the probabilistic roadmap and the Dijkstra's algorithm. The Voronoi diagram has been widely used in robot path planning (Bhattacharya and Gavrilova 2007;Candeloro et al 2017;Chen and Chen 2014;Garrido et al 2006). The inspection area is divided into points and these points are used to generate the Voronoi diagram.…”

Section: Introductionmentioning

confidence: 99%

A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

et al. 2019

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Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream. To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional (3-D) space, is a strategic problem due to its large-scale, complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.

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Section: Introductionmentioning

confidence: 99%

A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

et al. 2019

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“…Mission planning applications, such as the one in [18], are often complemented by road-map [27][28][29] and posebased [30][31][32] path planning methods depending on the path planning application. In addition, the impact of continuous commutation links have been included in flight/ path planning [33], something that very soon may be necessary in all UAV flight planning algorithms.…”

Section: Multiple Aircraft Path Planning Literaturementioning

confidence: 99%

Heterogeneous Aerial Platform Adaptive Mission Planning Using Genetic Algorithms

et al. 2017

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The focus of this study was to examine an automated mission planner that utilized a heterogeneous set of small aerial assets simultaneously surveying several Points of Interest (POI). The concept mission was to aerially search for an unknown Target of Interest (TOI) located amongst a set of POIs. In order to develop a planning system that is capable of meeting mission requirements, an adaptive mission planner, based on a Genetic Algorithm (GA), was investigated that seeks to task a heterogeneous set of unmanned aerial vehicles. Initially, a set of fixed wing UAVs were tasked to survey a set of POIs in search of a TOI, a POI that requires additional or long term surveillance. Once a TOI was located, a multi-rotor UAV was deployed to visit the TOI and additional POIs. Remaining POIs that were not tasked to the multirotor UAV were then re-tasked amongst the fixed wing aircraft set. The mission planner was implemented using a GA that planned initial and post TOI identification UAV paths. Mission simulations were conducted and the mission time increase was analyzed against different TOI locations and number of UAVs searching. Simulation results indicated that the deployment of a multi-rotor UAV not only provided additional surveillance of the TOI, but reduced overall mission times as well.

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“…Without path planning, autonomous cars could not be materialized. Currently, there are many path planning algorithms that have been developed based on methods such as Voronoi diagram (VD) [1], [2], cell decomposition (CD) [3], [4], genetic algorithm (GA) [5], [6] and visibility graph (VG), to name a few. A path planning algorithm's performance is normally measured based on three criteria which include the computation time, path optimality and completeness.…”

Section: Introductionmentioning

confidence: 99%

Optimal Path Planning using Equilateral Spaces Oriented Visibility Graph Method

Latip¹,

Omar²,

Debnath³

2017

IJECE

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Path planning has been an important aspect in the development of autonomous cars in which path planning is used to find a collision-free path for the car to traverse from a starting point Sp to a target point Tp. The main criteria for a good path planning algorithm include the capability of producing the shortest path with a low computation time. Low computation time makes the autonomous car able to re-plan a new collision-free path to avoid accident. However, the main problem with most path planning methods is their computation time increases as the number of obstacles in the environment increases. In this paper, an algorithm based on visibility graph (VG) is proposed. In the proposed algorithm, which is called Equilateral Space Oriented Visibility Graph (ESOVG), the number of obstacles considered for path planning is reduced by introducing a space in which the obstacles lie. This means the obstacles located outside the space are ignored for path planning. From simulation, the proposed algorithm has an improvement rate of up to 90% when compared to VG. This makes the algorithm is suitable to be applied in real-time and will greatly accelerate the development of autonomous cars in the near future.

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The UAV dynamic path planning algorithm research based on Voronoi diagram

Cited by 43 publications

References 1 publication

A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Heterogeneous Aerial Platform Adaptive Mission Planning Using Genetic Algorithms

Optimal Path Planning using Equilateral Spaces Oriented Visibility Graph Method

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