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Target Tracking and Navigation for Intelligent Autonomous Unmanned Systems Application
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Target Tracking and Navigation for Intelligent Autonomous Unmanned Systems Application

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

Deadline for manuscript submissions: 15 September 2024 | Viewed by 1022

Special Issue Editors

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 Issue Information

Dear Colleagues,

An autonomous unmanned system (AUS) is a kind of electromechanical system that can exert its power to perform a specified task during unmanned operations, such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs) and unmanned underwater vehicles (UUVs), etc. The development of artificial intelligence technology can enhance the capability of autonomous unmanned systems and form intelligent autonomous unmanned systems (iAUSs). An intelligent autonomous unmanned system is an interdisciplinary field that relies on advances in big data, artificial intelligence, and other science and technology to create autonomous unmanned systems with integrated tasks, motion planning, decision making and reasoning capabilities, featuring intelligence, autonomy and collaboration.

Target detection, tracking, localization and navigation technology are the most basic technologies of iAUS. At present, this kind of technology also presents a variety of intelligent development characteristics. This Special Issue hopes to discuss the technologies involved in iAUS and outline the latest research results to facilitate everyone’s communication.

We invite scholars in the field of unmanned system perception and control to show their research results, exchange scientific research experience and lead research on iUAS technology for better development.

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

  • target recognition
  • target tracking
  • point cloud processing
  • information fusion
  • event camera
  • SLAM
  • reactive control
  • perception-aware control
  • image processing

Published Papers (3 papers)

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Research

22 pages, 35068 KiB  
Article
Infrared and Visible Image Fusion Algorithm Based on Double-Domain Transform Filter and Contrast Transform Feature Extraction
by Xu Ma, Tianqi Li, Jun Deng, Tong Li, Jiahao Li, Chi Chang, Rui Wang, Guoliang Li, Tianrui Qi and Shuai Hao
Sensors 2024, 24(12), 3949; https://doi.org/10.3390/s24123949 - 18 Jun 2024
Viewed by 205
Abstract
Current challenges in visible and infrared image fusion include color information distortion, texture detail loss, and target edge blur. To address these issues, a fusion algorithm based on double-domain transform filter and nonlinear contrast transform feature extraction (DDCTFuse) is proposed. First, for the [...] Read more.
Current challenges in visible and infrared image fusion include color information distortion, texture detail loss, and target edge blur. To address these issues, a fusion algorithm based on double-domain transform filter and nonlinear contrast transform feature extraction (DDCTFuse) is proposed. First, for the problem of incomplete detail extraction that exists in the traditional transform domain image decomposition, an adaptive high-pass filter is proposed to decompose images into high-frequency and low-frequency portions. Second, in order to address the issue of fuzzy fusion target caused by contrast loss during the fusion process, a novel feature extraction algorithm is devised based on a novel nonlinear transform function. Finally, the fusion results are optimized and color-corrected by our proposed spatial-domain logical filter, in order to solve the color loss and edge blur generated in the fusion process. To validate the benefits of the proposed algorithm, nine classical algorithms are compared on the LLVIP, MSRS, INO, and Roadscene datasets. The results of these experiments indicate that the proposed fusion algorithm exhibits distinct targets, provides comprehensive scene information, and offers significant image contrast. Full article
(This article belongs to the Special Issue Target Tracking and Navigation for Intelligent Autonomous Unmanned Systems Application)
14 pages, 5016 KiB  
Article
Real-Time Trajectory Smoothing and Obstacle Avoidance: A Method Based on Virtual Force Guidance
by Yongbin Su, Chenying Lin and Tundong Liu
Sensors 2024, 24(12), 3935; https://doi.org/10.3390/s24123935 - 18 Jun 2024
Viewed by 230
Abstract
In dynamic environments, real-time trajectory planners are required to generate smooth trajectories. However, trajectory planners based on real-time sampling often produce jerky trajectories that necessitate post-processing steps for smoothing. Existing local smoothing methods may result in trajectories that collide with obstacles due to [...] Read more.
In dynamic environments, real-time trajectory planners are required to generate smooth trajectories. However, trajectory planners based on real-time sampling often produce jerky trajectories that necessitate post-processing steps for smoothing. Existing local smoothing methods may result in trajectories that collide with obstacles due to the lack of a direct connection between the smoothing process and trajectory optimization. To address this limitation, this paper proposes a novel trajectory-smoothing method that considers obstacle constraints in real time. By introducing virtual attractive forces from original trajectory points and virtual repulsive forces from obstacles, the resultant force guides the generation of smooth trajectories. This approach enables parallel execution with the trajectory-planning process and requires low computational overhead. Experimental validation in different scenarios demonstrates that the proposed method not only achieves real-time trajectory smoothing but also effectively avoids obstacles. Full article
(This article belongs to the Special Issue Target Tracking and Navigation for Intelligent Autonomous Unmanned Systems Application)
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16 pages, 5742 KiB  
Article
RSDNet: A New Multiscale Rail Surface Defect Detection Model
by Jingyi Du, Ruibo Zhang, Rui Gao, Lei Nan and Yifan Bao
Sensors 2024, 24(11), 3579; https://doi.org/10.3390/s24113579 - 1 Jun 2024
Viewed by 281
Abstract
The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, [...] Read more.
The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network’s attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications. Full article
(This article belongs to the Special Issue Target Tracking and Navigation for Intelligent Autonomous Unmanned Systems Application)
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