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Perception and Control Technology for Intelligent Autonomous Unmanned Systems
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Perception and Control Technology for Intelligent Autonomous Unmanned Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 25 January 2026 | Viewed by 2159

Special Issue Editors

Prof. Dr. Chunhui Zhao

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
Interests: visual navigation of UAV; image processing; target tracking and recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Shuai Hao

E-Mail Website
Guest Editor
College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Interests: object detection; artificial intelligence; vision navigation; image fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

An autonomous unmanned system (AUS) is an electromechanical system that can perform a specified task under its own power autonomously. Such systems encompass unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs) and unmanned underwater vehicles (UUVs). The development of artificial intelligence technologies can enhance their capabilities and form intelligent autonomous unmanned systems (iAUSs). iAUS is an interdisciplinary field that relies on advances in big data, artificial intelligence and other sciences and technological domains to create autonomous unmanned systems with integrated mission abilities, motion planning, decision making and reasoning capabilities that are intelligent, autonomous and collaborative.

Target detection, tracking, recognition, positioning and other sensing technologies, as well as system navigation and guidance (and other control technologies), are the most basic technologies of iAUSs. At present, these kinds of technologies also show a variety of intelligent development characteristics. This Special Issue aims to discuss the technologies involved in iAUSs, present the latest research advancements and facilitate the exchange of information.

Scholars in the field of unmanned system perception and control are invited to present research results, exchange scientific research experience and contribute to the development of unmanned system technology research.

Prof. Dr. Chunhui Zhao
Dr. Shuai Hao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • environment perception
  • intelligent network
  • target detection
  • target recognition
  • target tracking
  • reactive control
  • perception-aware control
  • SLAM
  • robot
  • event camera
  • point cloud processing
  • image processing
  • information fusion

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Related Special Issue

Published Papers (3 papers)

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Research

28 pages, 1768 KB  
Article
Research on a Cooperative Grasping Method for Heterogeneous Objects in Unstructured Scenarios of Mine Conveyor Belts Based on an Improved MATD3
by Rui Gao, Mengcong Liu, Jingyi Du, Yifan Bao, Xudong Wu and Jiahui Liu
Sensors 2025, 25(22), 6824; https://doi.org/10.3390/s25226824 - 7 Nov 2025
Abstract
Underground coal mine conveying systems operate in unstructured environments. Influenced by geological and operational factors, coal conveyors are frequently contaminated by foreign objects such as coal gangue and anchor bolts. These contaminants disrupt conveying stability and pose challenges to safe mining operations, making [...] Read more.
Underground coal mine conveying systems operate in unstructured environments. Influenced by geological and operational factors, coal conveyors are frequently contaminated by foreign objects such as coal gangue and anchor bolts. These contaminants disrupt conveying stability and pose challenges to safe mining operations, making their effective removal critical. Given the significant heterogeneity and unpredictability of these objects in shape, size, and orientation, precise manipulation requires dual-arm cooperative control. Traditional control algorithms rely on precise dynamic models and fixed parameters, lacking robustness in such unstructured environments. To address these challenges, this paper proposes a cooperative grasping method tailored for heterogeneous objects in unstructured environments. The MATD3 algorithm is employed to cooperatively perform dual-arm trajectory planning and grasping tasks. A multi-factor reward function is designed to accelerate convergence in continuous action spaces, optimize real-time grasping trajectories for foreign objects, and ensure stable robotic arm positioning. Furthermore, priority experience replay (PER) is integrated into the MATD3 framework to enhance experience utilization and accelerate convergence toward optimal policies. For slender objects, a sequential cooperative optimization strategy is developed to improve the stability and reliability of grasping and placement. Experimental results demonstrate that the P-MATD3 algorithm significantly improves grasping success rates and efficiency in unstructured environments. In single-arm tasks, compared to MATD3 and MADDPG, P-MATD3 increases grasping success rates by 7.1% and 9.94%, respectively, while reducing the number of steps required to reach the pre-grasping point by 11.44% and 12.77%. In dual-arm tasks, success rates increased by 5.58% and 9.84%, respectively, while step counts decreased by 11.6% and 18.92%. Robustness testing under Gaussian noise demonstrated that P-MATD3 maintains high stability even with varying noise intensities. Finally, ablation and comparative experiments comprehensively validated the proposed method’s effectiveness in simulated environments. Full article
(This article belongs to the Special Issue Perception and Control Technology for Intelligent Autonomous Unmanned Systems)
20 pages, 7975 KB  
Article
Trunk Detection in Complex Forest Environments Using a Lightweight YOLOv11-TrunkLight Algorithm
by Siqi Zhang, Yubi Zheng, Rengui Bi, Yu Chen, Cong Chen, Xiaowen Tian and Bolin Liao
Sensors 2025, 25(19), 6170; https://doi.org/10.3390/s25196170 - 5 Oct 2025
Viewed by 523
Abstract
The autonomous navigation of inspection robots in complex forest environments heavily relies on accurate trunk detection. However, existing detection models struggle to achieve both high accuracy and real-time performance on resource-constrained edge devices. To address this challenge, this study proposes a lightweight algorithm [...] Read more.
The autonomous navigation of inspection robots in complex forest environments heavily relies on accurate trunk detection. However, existing detection models struggle to achieve both high accuracy and real-time performance on resource-constrained edge devices. To address this challenge, this study proposes a lightweight algorithm named YOLOv11-TrunkLight. The core innovations of the algorithm include (1) a novel StarNet_Trunk backbone network, which replaces traditional residual connections with element-wise multiplication and incorporates depthwise separable convolutions, significantly reducing computational complexity while maintaining a large receptive field; (2) the C2DA deformable attention module, which effectively handles the geometric deformation of tree trunks through dynamic relative position bias encoding; and (3) the EffiDet detection head, which improves detection speed and reduces the number of parameters through dual-path feature decoupling and a dynamic anchor mechanism. Experimental results demonstrate that compared to the baseline YOLOv11 model, our method improves detection speed by 13.5%, reduces the number of parameters by 34.6%, and decreases computational load (FLOPs) by 39.7%, while the average precision (mAP) is only marginally reduced by 0.1%. These advancements make the algorithm particularly suitable for deployment on resource-constrained edge devices of inspection robots, providing reliable technical support for intelligent forestry management. Full article
(This article belongs to the Special Issue Perception and Control Technology for Intelligent Autonomous Unmanned Systems)
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16 pages, 5727 KB  
Article
ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images
by Jing Zhang, Hao Zhou, Kunyu Liu and Yuguang Xu
Sensors 2025, 25(8), 2432; https://doi.org/10.3390/s25082432 - 12 Apr 2025
Cited by 3 | Viewed by 1085
Abstract
The outbreak of cassava diseases poses a serious threat to agricultural economic security and food production systems in tropical regions. Traditional manual monitoring methods are limited by efficiency bottlenecks and insufficient spatial coverage. Although low-altitude drone technology offers advantages such as high resolution [...] Read more.
The outbreak of cassava diseases poses a serious threat to agricultural economic security and food production systems in tropical regions. Traditional manual monitoring methods are limited by efficiency bottlenecks and insufficient spatial coverage. Although low-altitude drone technology offers advantages such as high resolution and strong timeliness, it faces dual challenges in the field of disease identification, such as complex background interference and irregular disease morphology. To address these issues, this study proposes an intelligent classification method for cassava diseases based on drone imagery and an ED-Swin Transformer. Firstly, we introduced the EMAGE (Efficient Multi-Scale Attention with Grouping and Expansion) module, which integrates the global distribution features and local texture details of diseased leaves in drone imagery through a multi-scale grouped attention mechanism, effectively mitigating the interference of complex background noise on feature extraction. Secondly, the DASPP (Deformable Atrous Spatial Pyramid Pooling) module was designed to use deformable atrous convolution to adaptively match the irregular boundaries of diseased areas, enhancing the model’s robustness to morphological variations caused by angles and occlusions in low-altitude drone photography. The results show that the ED-Swin Transformer model achieved excellent performance across five evaluation metrics, with scores of 94.32%, 94.56%, 98.56%, 89.22%, and 96.52%, representing improvements of 1.28%, 2.32%, 0.38%, 3.12%, and 1.4%, respectively. These experiments demonstrate the superior performance of the ED-Swin Transformer model in cassava classification networks. Full article
(This article belongs to the Special Issue Perception and Control Technology for Intelligent Autonomous Unmanned Systems)
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