A ROS-based hybrid algorithm for the UAV path planning problem

Purpose It is possible with classical path planning algorithms to plan a path in a static environment if the instant position of the vehicle is known and the target and obstacle positions are constant. In a dynamic case, these methods used for the static environment are insufficient. The purpose of...

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Published inAircraft engineering Vol. 95; no. 5; pp. 784 - 798
Main Authors Canpolat Tosun, Demet, Işık, Yasemin
Format Journal Article
LanguageEnglish
Published Bradford Emerald Publishing Limited 27.03.2023
Emerald Group Publishing Limited
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ISSN1748-8842
1758-4213
1748-8842
DOI10.1108/AEAT-04-2022-0102

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Summary:Purpose It is possible with classical path planning algorithms to plan a path in a static environment if the instant position of the vehicle is known and the target and obstacle positions are constant. In a dynamic case, these methods used for the static environment are insufficient. The purpose of this study is to find a new method that can provide a solution to the four-rotor unmanned aerial vehicle (UAV) path planning problem in static and dynamic environments. Design/methodology/approach As a solution to the problem within the scope of this study, there is a new hybrid method in which the global A* algorithm and local the VFH+ algorithm are combined. Findings The performance of the designed algorithm was tested in different environments using the Gazebo model of a real quadrotor and the robot operating system (ROS), which is the widely used platform for robotic applications. Navigation stacks developed for mobile robots on the ROS platform were also used for the UAV, and performance benchmarks were carried out. From the proposed hybrid algorithm, remarkable results were obtained in terms of both planning and implementation time compared to ROS navigation stacks. Originality/value This study proposes a new hybrid approach to the path planning problem for UAVs operating in both static and dynamic environments.
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ISSN:1748-8842
1758-4213
1748-8842
DOI:10.1108/AEAT-04-2022-0102