SRAD: Autonomous Decision‐Making Method for UAV Based on Safety Reinforcement Learning.
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| Title: | SRAD: Autonomous Decision‐Making Method for UAV Based on Safety Reinforcement Learning. |
|---|---|
| Authors: | Xiao, Wenwen1 (AUTHOR), Luo, Xiangfeng1 (AUTHOR) luoxf@shu.edu.cn, Xie, Shaorong1 (AUTHOR) |
| Source: | Expert Systems. May2025, Vol. 42 Issue 5, p1-18. 18p. |
| Subjects: | Image segmentation, Learning modules, Prior learning |
| Abstract: | Unmanned aerial vehicles (UAVs) are increasingly vital across numerous sectors, from logistics and rescue operations to military endeavours and beyond. However, ensuring safety in the decision‐making processes surrounding UAV operations in real‐world settings has become an urgent and complex challenge. At present, the main methods to minimise the risk of drone decision‐making include utilising pre‐established control rules, expert prior knowledge and regularisation constraints. However, these methodologies require UAVs to meet demanding prerequisites, including the acquisition of extensive decision‐making experience and the establishment of comprehensive rules. Regrettably, these strict requirements often lead to frequent UAV crashes in uncertain environments and subsequent mission failures. In order to tackle these issues, we propose a self‐decision‐making method for quadcopter UAVs based on safe reinforcement learning. Our method utilises a multilevel cascading feature semantic space for reinforcement learning, integrating depth images, greyscale images, semantic segmentation images and object detection results as inputs. This approach aims to facilitate safe autonomous learning. Moreover, we integrate real offline labelled data to enhance the safety policy. Depending on the varying levels of risk encountered during the UAV's decision‐making process, we dynamically select different safety policies. Through this iterative process, the UAV progressively eliminates extreme actions and reverts to the UAV learning policy module. Experimental results indicate that our method not only ensures safe decision‐making for UAVs in uncertain environments but also exhibits superior safety decision‐making efficacy compared to certain baseline methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 184494874 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: SRAD: Autonomous Decision‐Making Method for UAV Based on Safety Reinforcement Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiao%2C+Wenwen%22">Xiao, Wenwen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Xiangfeng%22">Luo, Xiangfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> luoxf@shu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xie%2C+Shaorong%22">Xie, Shaorong</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems%22">Expert Systems</searchLink>. May2025, Vol. 42 Issue 5, p1-18. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+modules%22">Learning modules</searchLink><br /><searchLink fieldCode="DE" term="%22Prior+learning%22">Prior learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Unmanned aerial vehicles (UAVs) are increasingly vital across numerous sectors, from logistics and rescue operations to military endeavours and beyond. However, ensuring safety in the decision‐making processes surrounding UAV operations in real‐world settings has become an urgent and complex challenge. At present, the main methods to minimise the risk of drone decision‐making include utilising pre‐established control rules, expert prior knowledge and regularisation constraints. However, these methodologies require UAVs to meet demanding prerequisites, including the acquisition of extensive decision‐making experience and the establishment of comprehensive rules. Regrettably, these strict requirements often lead to frequent UAV crashes in uncertain environments and subsequent mission failures. In order to tackle these issues, we propose a self‐decision‐making method for quadcopter UAVs based on safe reinforcement learning. Our method utilises a multilevel cascading feature semantic space for reinforcement learning, integrating depth images, greyscale images, semantic segmentation images and object detection results as inputs. This approach aims to facilitate safe autonomous learning. Moreover, we integrate real offline labelled data to enhance the safety policy. Depending on the varying levels of risk encountered during the UAV's decision‐making process, we dynamically select different safety policies. Through this iterative process, the UAV progressively eliminates extreme actions and reverts to the UAV learning policy module. Experimental results indicate that our method not only ensures safe decision‐making for UAVs in uncertain environments but also exhibits superior safety decision‐making efficacy compared to certain baseline methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=184494874 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/exsy.70004 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Image segmentation Type: general – SubjectFull: Learning modules Type: general – SubjectFull: Prior learning Type: general Titles: – TitleFull: SRAD: Autonomous Decision‐Making Method for UAV Based on Safety Reinforcement Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiao, Wenwen – PersonEntity: Name: NameFull: Luo, Xiangfeng – PersonEntity: Name: NameFull: Xie, Shaorong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02664720 Numbering: – Type: volume Value: 42 – Type: issue Value: 5 Titles: – TitleFull: Expert Systems Type: main |
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