Survey on computational-intelligence-based UAV path planning

The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path that connects a start state and a goal state while meeting the required constraints. Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches for addressing comp...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 158; pp. 54 - 64
Main Authors: Zhao, Yijing, Zheng, Zheng, Liu, Yang
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 15.10.2018
Elsevier Science Ltd
Subjects:
Aeronautics
Algorithms
Artificial intelligence
Computation
Computational intelligence
Electronic publishing
Environment models
Identification methods
Intelligence
Learning-based algorithms
Path planning
Planning
Polls & surveys
Three dimensional models
Two dimensional models
Unmanned aerial vehicle
Unmanned aerial vehicles
World problems
Learning-based algorithms
Path planning
Unmanned aerial vehicle
Computational intelligence
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path that connects a start state and a goal state while meeting the required constraints. Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches for addressing complex real-world problems for which mathematical or traditional modelling does not perform well. It has been applied in the field of UAVs since it can yield effective, accurate and rapid solutions. This article provides an overview of studies on UAV path planning based on CI methods published in major journals and conference proceedings. We survey relevant studies with respect to different CI algorithms utilized in UAV path planning, the types of time domain in UAV path planning, namely, offline and online, and the types of environment models, namely, 2D and 3D. It is observed that CI methods outperform traditional methods on online and 3D problems. The analysis is useful for identifying key results from UAV path planning research and is leveraged in this article to highlight trends and open issues.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.05.033