Academic Journal
Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments.
| Τίτλος: | Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments. |
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| Συγγραφείς: | Ren, Hexuan |
| Πηγή: | Frontiers in Neurorobotics; 2026, p1-12, 12p |
| Θεματικοί όροι: | Multisensor data fusion, Robotic path planning, Reinforcement learning, Intelligent agents |
| Περίληψη: | Autonomous navigation of embodied agents in complex unstructured environments demands tightly coupled multimodal perception and real-time path planning capabilities, forming a core technical bottleneck in physical-world robot deployment. Heterogeneous sensor data from visual, LiDAR, and depth modalities remain difficult to align and fuse under varying illumination and terrain conditions, while dynamic obstacle configurations impose severe latency constraints that existing planning algorithms fail to satisfy simultaneously. This paper proposes an integrated end-to-end framework combining a Cross-Modal Attention Fusion (CMAF) module, a Kalman-Graph Neural Network (K-GNN) dynamic obstacle predictor, and a two-layer Proximal Policy Optimization path planning architecture. The Cross-Modal Attention Fusion module fuses three-modal features through a multi-head attention mechanism, achieving a mean Intersection over Union of 78.6% with a fusion latency of 5.3 ms on a self-built unstructured environment dataset. The Kalman-Graph Neural Network couples Kalman filter physical motion priors with graph neural network interaction modeling to predict short-term trajectories of multiple moving obstacles online. The two-layer planner integrates fused perception features with a global semantic topology path to output local velocity commands in real time, reducing average planning time to 18.4 ms. Experiments on a Gazebo simulation platform and a self-developed four-wheeled robot across 60 unstructured test cases demonstrate a navigation success rate of 94.5%, surpassing the strongest baseline by 7.8 percentage points and satisfying real-time operational requirements. [ABSTRACT FROM AUTHOR] |
| Copyright of Frontiers in Neurorobotics is the property of Frontiers Media S.A. 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.) | |
| Βάση Δεδομένων: | Biomedical Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edm&genre=article&issn=16625218&ISBN=&volume=&issue=&date=20260617&spage=1&pages=1-12&title=Frontiers in Neurorobotics&atitle=Research%20on%20embodied%20agent%20multimodal%20perception%20and%20real-time%20path%20planning%20algorithms%20for%20complex%20unstructured%20environments.&aulast=Ren%2C%20Hexuan&id=DOI:10.3389/fnbot.2026.1846108 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ren%2C+Hexuan%22">Ren, Hexuan</searchLink> – Name: TitleSource Label: Source Group: Src Data: Frontiers in Neurorobotics; 2026, p1-12, 12p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+agents%22">Intelligent agents</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Autonomous navigation of embodied agents in complex unstructured environments demands tightly coupled multimodal perception and real-time path planning capabilities, forming a core technical bottleneck in physical-world robot deployment. Heterogeneous sensor data from visual, LiDAR, and depth modalities remain difficult to align and fuse under varying illumination and terrain conditions, while dynamic obstacle configurations impose severe latency constraints that existing planning algorithms fail to satisfy simultaneously. This paper proposes an integrated end-to-end framework combining a Cross-Modal Attention Fusion (CMAF) module, a Kalman-Graph Neural Network (K-GNN) dynamic obstacle predictor, and a two-layer Proximal Policy Optimization path planning architecture. The Cross-Modal Attention Fusion module fuses three-modal features through a multi-head attention mechanism, achieving a mean Intersection over Union of 78.6% with a fusion latency of 5.3 ms on a self-built unstructured environment dataset. The Kalman-Graph Neural Network couples Kalman filter physical motion priors with graph neural network interaction modeling to predict short-term trajectories of multiple moving obstacles online. The two-layer planner integrates fused perception features with a global semantic topology path to output local velocity commands in real time, reducing average planning time to 18.4 ms. Experiments on a Gazebo simulation platform and a self-developed four-wheeled robot across 60 unstructured test cases demonstrate a navigation success rate of 94.5%, surpassing the strongest baseline by 7.8 percentage points and satisfying real-time operational requirements. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Frontiers in Neurorobotics is the property of Frontiers Media S.A. 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3389/fnbot.2026.1846108 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Robotic path planning Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Intelligent agents Type: general Titles: – TitleFull: Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ren, Hexuan IsPartOfRelationships: – BibEntity: Dates: – D: 17 M: 06 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16625218 Titles: – TitleFull: Frontiers in Neurorobotics Type: main |
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