Unmanned Aerial Vehicle route optimization using ant system algorithm

Jevtić, Aleksandar; Andina, Diego; Jaimes, Aldo; Gómez, José Luís Graña; Jamshidi, Mo

doi:10.1109/sysose.2010.5544109

Cited by 21 publications

(20 citation statements)

References 7 publications

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“…In the map coverage scenario, they showed that the Ant System algorithm could be applied to the optimization of the UAV system. As a comparison of another method, the NNS (nearest neighbor search) shows that the algorithm is more effective in finding a more suitable route than the nearest neighbor search [4]. Felipe and Jose (2010) proposed a Dijkstra algorithm for fixed-wing UAV trajectory planning based on field height.…”

Section: General Literature Reviewmentioning

confidence: 99%

Path Planning Algorithms for Unmanned Aerial Vehicles

Dhulkefl¹,

Durdu²

2019

IJTSRD

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In this paper, the shortest path for Unmanned Aerıal Vehicles (UAVs) is calculated with two-dimensional (2D) path planning algorithms in the environment including obstacles and thus the robots could perform their tasks as soon as possible in the environment. The aim of this paper is to avoid obstacles and to find the shortest way to the target point. Th e simulation environment was created to evaluate the arrival time on the path planning algorithms (A* and Dijkstra algorithms) for the UAVs. As a result, real-time tests were performed with UAVs

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Section: General Literature Reviewmentioning

confidence: 99%

Path Planning Algorithms for Unmanned Aerial Vehicles

Dhulkefl¹,

Durdu²

2019

IJTSRD

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“…When comparing another approach, NNS (nearest neighbor search) means that the algorithm is more successful in finding a better route than the nearest neighbor search. (Jevtić et al, 2010). The proposed area height-based Dijkstra algorithm MDA (Modified Dijkstra Algorithm) approach for UAV fixed wing trajectory planning shows that EDA (Elevation Based Dijkstra Algorithm) significantly reduces the computation time (Medeiros and Da Silva, 2010).…”

Section: Introductionmentioning

confidence: 97%

Dijkstra Algorithm Using Uav Path Planning

Dhulkefl

Durdu

Terzioğlu

2020

Konya Journal of Engineering Sciences

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The use of unmanned aerial vehicles (UAV) is increasing today. UAVs can be divided into two parts, which are remote controlled and can travel automatically due to a certain battery problem. Recent research has also focused on the development and application of new algorithms to autonomously control these vehicles and determine the shortest flight paths. Together with these researches, UAVs are used in many civil activities such as weather forecasts, environmental studies and traffic control. Threedimensional (3D) path planning is an important issue for autonomously moving UAVs. The shortest path for Unmanned Aerial Vehicles (UAV) is determined by using two-dimensional (2D) path planning algorithms using the obstacles in the environment, and allows UAVs to perform their environmental tasks as soon as possible. The purpose of this study is to determine the shortest path to the target point and avoiding obstacles for UAVs using the Dijkstra algorithm. It was developed to evaluate the arrival time of the UAVs in the path planning algorithm with the simulation performed in the MATLAB program. In this study, the obstacles were defined for the purpose of the building with different heights and different widths and 2D and 3D models were carried out, assuming that the UAV flies at certain heights. In addition, the flight of the UAVs in the route planning determined in the real applications was carried out and the data such as battery consumption, amount of battery spent, speed, amount of travel were examined.

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“…Some researchers hoped to establish a unified potential function to solve this problem [5]; unfortunately, it still requires regular obstacle to avoid huge calculation requirements, which is nearly intolerable for realtime path planning. Some researchers have attempted to find the solution in intelligent optimization algorithms, such as genetic algorithm (GA) [6,7] and ant colony algorithm (ACA) [8,9]. The former has good global searching maneuverability and can quickly find all of the solutions without falling into local optimal [7].…”

Section: Introductionmentioning

confidence: 99%

“…Ant colony algorithm is sensitive to the initial parameters. An inappropriate setting decreases the search rate and yields poor results [9]. Furthermore, the real-time path planning of UAVs requires high efficiency and accuracy; these algorithms may not be proper solutions.…”

Section: Introductionmentioning

confidence: 99%

Efficient UAV Path Planning with Multiconstraints in a 3D Large Battlefield Environment

Zhan

Wang

Chen

2014

Mathematical Problems in Engineering

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This study introduces an improvedA*algorithm for the real-time path planning of Unmanned Air Vehicles (UAVs) in a 3D large-scale battlefield environment to solve the problem that UAVs require high survival rates and low fuel consumption. The algorithm is able to find the optimal path between two waypoints in the target space and comprehensively takes factors such as altitude, detection probability, and path length into account. It considers the maneuverability constraints of the UAV, including the safety altitude, climb rate, and turning radius, to obtain the final flyable path. Finally, the authors test the algorithm in an approximately 2,500,000 square meter area containing radars, no-fly zones, and extreme weather conditions to measure its feasibility, stability, and efficiency.

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Unmanned Aerial Vehicle route optimization using ant system algorithm

Cited by 21 publications

References 7 publications

Path Planning Algorithms for Unmanned Aerial Vehicles

Path Planning Algorithms for Unmanned Aerial Vehicles

Dijkstra Algorithm Using Uav Path Planning

Efficient UAV Path Planning with Multiconstraints in a 3D Large Battlefield Environment

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