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Unmanned and Autonomous Surface Vehicles: Localization, Control, and Navigation
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Unmanned and Autonomous Surface Vehicles: Localization, Control, and Navigation

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2992

Special Issue Editor

Dr. Karim Ismail

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON, Canada
Interests: road safety analysis; highway design

Special Issue Information

Dear Colleagues,

We are seeing rapid advances in vehicle control and autonomous surface systems. Artificial-intelligence-powered systems are gaining mainstream acceptance due to the unique capabilities that they offer. Surface vehicles can be utilized in a wide range of contexts, including, but not limited to, transportation, industry, and warehouses. The unmanned or autonomous operation of such systems can offer tremendous functional benefits, such as higher safety and efficiency expectations in the deployment of connected and autonomous vehicles and improved traffic flow.

This Special Issue will cover topics including autonomous and unmanned vehicle control, safety countermeasures, safe interactions with objects and other road users, navigation, spatial awareness of surrounding environments, and key advancements in the sensors used in the control and navigation of unmanned and autonomous vehicles.

This Special Issue aims to disseminate cutting-edge research on several topics related to unmanned and autonomous surface vehicles, with a particular focus on localization, control, and navigation.

Dr. Karim Ismail
Guest Editor

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

  • unmanned surface vehicles
  • autonomous surface vehicles
  • safe interactions
  • outdoor navigation
  • localization
  • control

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Published Papers (2 papers)

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Research

28 pages, 2592 KiB  
Article
Output Feedback Integrated Guidance and Control Design for Autonomous Underwater Vehicles Against Maneuvering Targets
by Rui Wang, Jingwei Lu, Shuke Lyu, Yongtao Liu and Yuchen Cui
Sensors 2025, 25(10), 3088; https://doi.org/10.3390/s25103088 - 13 May 2025
Viewed by 217
Abstract
Traditional guidance and control systems often treat guidance and control systems separately, leading to reduced interception accuracy and responsiveness, especially during high-speed terminal trajectories. These limitations are further exacerbated in autonomous underwater vehicles (AUVs) due to unknown wave/current disturbances, harsh underwater acoustic conditions, [...] Read more.
Traditional guidance and control systems often treat guidance and control systems separately, leading to reduced interception accuracy and responsiveness, especially during high-speed terminal trajectories. These limitations are further exacerbated in autonomous underwater vehicles (AUVs) due to unknown wave/current disturbances, harsh underwater acoustic conditions, and limited sensor capabilities. To address these challenges, this paper studies an integrated guidance and control (IGC) design for AUVs intercepting maneuvering targets with unknown disturbances and unmeasurable system states. The IGC model is derived based on the relative motion equations between the AUV and the target, incorporating the lateral dynamics of the AUV. A model transformation is introduced to synthesize external disturbances with unmeasurable states, extending the resultant disturbance to a new system state. A finite-time convergent extended state observer (ESO) is thus designed for the transformed system to estimate the unknown signals. Using these estimates from the observer, a finite-time event-triggered sliding mode controller is developed, ensuring finite-time convergence of system errors to an adjustable residual set, as rigorously proven through Lyapunov stability analysis. Simulation results demonstrate the superiority of the proposed method in achieving higher interception accuracy and faster response compared to traditional guidance and control approaches with unknown disturbances and unmeasurable states. Full article
(This article belongs to the Special Issue Unmanned and Autonomous Surface Vehicles: Localization, Control, and Navigation)
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29 pages, 234471 KiB  
Article
Optimizing Camera Exposure Time for Automotive Applications
by Hao Lin, Darragh Mullins, Dara Molloy, Enda Ward, Fiachra Collins, Patrick Denny, Martin Glavin, Brian Deegan and Edward Jones
Sensors 2024, 24(16), 5135; https://doi.org/10.3390/s24165135 - 8 Aug 2024
Cited by 1 | Viewed by 2361
Abstract
Camera-based object detection is integral to advanced driver assistance systems (ADAS) and autonomous vehicle research, and RGB cameras remain indispensable for their spatial resolution and color information. This study investigates exposure time optimization for such cameras, considering image quality in dynamic ADAS scenarios. [...] Read more.
Camera-based object detection is integral to advanced driver assistance systems (ADAS) and autonomous vehicle research, and RGB cameras remain indispensable for their spatial resolution and color information. This study investigates exposure time optimization for such cameras, considering image quality in dynamic ADAS scenarios. Exposure time, the period during which the camera sensor is exposed to light, directly influences the amount of information captured. In dynamic scenarios, such as those encountered in typical driving scenarios, optimizing exposure time becomes challenging due to the inherent trade-off between Signal-to-Noise Ratio (SNR) and motion blur, i.e., extending exposure time to maximize information capture increases SNR, but also increases the risk of motion blur and overexposure, particularly in low-light conditions where objects may not be fully illuminated. The study introduces a comprehensive methodology for exposure time optimization under various lighting conditions, examining its impact on image quality and computer vision performance. Traditional image quality metrics show a poor correlation with computer vision performance, highlighting the need for newer metrics that demonstrate improved correlation. The research presented in this paper offers guidance into the enhancement of single-exposure camera-based systems for automotive applications. By addressing the balance between exposure time, image quality, and computer vision performance, the findings provide a road map for optimizing camera settings for ADAS and autonomous driving technologies, contributing to safety and performance advancements in the automotive landscape. Full article
(This article belongs to the Special Issue Unmanned and Autonomous Surface Vehicles: Localization, Control, and Navigation)
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