UAV Motion Strategies in Uncertain Dynamic Environments: A Path Planning Method Based on Q-Learning Strategy Article Swipe
Jun-hui Cui
,
Ruixuan Wei
,
Zongcheng Liu
,
Kai Zhou
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/app8112169
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/app8112169
A solution framework for UAV motion strategies in uncertain dynamic environments is constructed in this paper. Considering that the motion states of UAV might be influenced by some dynamic uncertainties, such as control strategies, flight environments, and any other bursting-out threats, we model the uncertain factors that might cause such influences to the path planning of the UAV, unified as an unobservable part of the system and take the acceleration together with the bank angle of the UAV as a control variable. Meanwhile, the cost function is chosen based on the tracking error, then the control instructions and flight path for UAV can be achieved. Then, the cost function can be optimized through Q-learning, and the best UAV action sequence for conflict avoidance under the moving threat environment can be obtained. According to Bellman’s optimization principle, the optimal action strategies can be obtained from the current confidence level. The method in this paper is more in line with the actual UAV path planning, since the generation of the path planning strategy at each moment takes into account the influence of the UAV control strategy on its motion at the next moment. The simulation results show that all the planning paths that are created according to the solution framework proposed in this paper have a very high tracking accuracy, and this method has a much shorter processing time as well as a shorter path it can create.
Related Topics
Concepts
Motion planning
Unobservable
Computer science
Path (computing)
Control theory (sociology)
Control (management)
Function (biology)
Acceleration
Mathematical optimization
Artificial intelligence
Mathematics
Robot
Programming language
Classical mechanics
Physics
Evolutionary biology
Biology
Econometrics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app8112169
- https://www.mdpi.com/2076-3417/8/11/2169/pdf?version=1541495274
- OA Status
- gold
- Cited By
- 26
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2899746873
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2899746873Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app8112169Digital Object Identifier
- Title
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UAV Motion Strategies in Uncertain Dynamic Environments: A Path Planning Method Based on Q-Learning StrategyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-11-06Full publication date if available
- Authors
-
Jun-hui Cui, Ruixuan Wei, Zongcheng Liu, Kai ZhouList of authors in order
- Landing page
-
https://doi.org/10.3390/app8112169Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/8/11/2169/pdf?version=1541495274Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/8/11/2169/pdf?version=1541495274Direct OA link when available
- Concepts
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Motion planning, Unobservable, Computer science, Path (computing), Control theory (sociology), Control (management), Function (biology), Acceleration, Mathematical optimization, Artificial intelligence, Mathematics, Robot, Programming language, Classical mechanics, Physics, Evolutionary biology, Biology, EconometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
26Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 2, 2023: 5, 2022: 7, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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