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Intelligent Sensing and Control Technology for Unmanned Vehicles
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Intelligent Sensing and Control Technology for Unmanned Vehicles

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 5591

Special Issue Editors

Dr. Xiaojie Sun

E-Mail Website
Guest Editor
College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China
Interests: decision-making; autonomous control systems; multi-agent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Dongdong Mu

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: unmanned surface vehicle; intelligent decision; nonlinear control
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Wu

E-Mail Website
Guest Editor
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: nonlinear control; fault diagnosis; fault tolerant control of unmanned systems

Special Issue Information

Dear Colleagues,

In recent years, intelligent sensing and control technology for unmanned vehicles has driven profound transformations in the transportation field at an unprecedented speed. The core of this technology lies in the integration of various high-precision and high-reliability sensors, such as LiDAR, millimeter wave radar, high-definition cameras, ultrasonic sensors, and inertial navigation units (IMU), which together provide vehicles with a comprehensive perception of the surrounding environment. By integrating and processing these sensor data through advanced algorithms, unmanned vehicles can understand traffic conditions in real time, identify vehicles and other obstacles, and predict their motion trajectories.

In terms of control technology, autonomous driving technology relies on complex control algorithms and artificial intelligence systems to achieve precise control of vehicle acceleration, braking, and steering, ensuring safety and stability during driving. In addition, this technology also integrates advanced functions such as path planning, decision-making, and behavior prediction, enabling vehicles to autonomously navigate to their destinations while strictly adhering to traffic rules and adapting to various complex traffic scenarios.

The unmanned vehicles in this Special Issue include various unmanned carriers in the fields of land, sea, and air, including unmanned ground vehicles, unmanned ships, unmanned aerial vehicles, and so on. The development of intelligent sensing and control technology for unmanned vehicles will greatly enhance the safety and efficiency of the transportation industry and have profound impacts on multiple fields such as infrastructure planning, logistics transportation, and personal travel. With the continuous deepening of research and the increasing maturity of technology, unmanned vehicles are gradually moving from laboratories to practical environments, leading us into a new era of intelligent transportation. This Special Issue, ”Intelligent Sensing and Control Technology for Unmanned Vehicles“, will focus on the latest developments in intelligent sensing and control technology in this field, exploring technological innovation, application challenges, and future development trends, thereby contributing to the further development of unmanned vehicles technology.

Dr. Xiaojie Sun
Dr. Dongdong Mu
Dr. Yue Wu
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 250 words) can be sent to the Editorial Office for assessment.

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 vehicles
  • intelligent sensing
  • sensor fusion
  • motion-planning
  • AI-driven control
  • decision-making
  • behavior prediction
  • collision avoidance
  • safety control
  • autonomous driving

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Published Papers (4 papers)

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Research

16 pages, 34530 KB  
Article
A Hybrid θ*-APF-Q Framework for Energy-Aware Path Planning of Unmanned Surface Vehicles Under Wind and Current
by Xiaojie Sun, Zhanhong Dong, Xinbo Chen, Lifan Sun and Yanheng An
Sensors 2026, 26(7), 2116; https://doi.org/10.3390/s26072116 - 29 Mar 2026
Viewed by 457
Abstract
Safe and energy-aware navigation is still difficult for unmanned surface vehicles (USVs), especially in cluttered waters where obstacles, smooth motion, and wind or current effects must be considered at the same time. If these issues are handled separately, the path may become longer [...] Read more.
Safe and energy-aware navigation is still difficult for unmanned surface vehicles (USVs), especially in cluttered waters where obstacles, smooth motion, and wind or current effects must be considered at the same time. If these issues are handled separately, the path may become longer and the vehicle may turn more often, which raises propulsion effort and hurts stability. To reduce these problems, a hybrid path planning method called θ∗-APF-Q is proposed, and it combines global planning, learning-based decisions, and local adjustment in a three-layer structure. First, an any-angle θ∗ global planner is employed to generate a near-optimal backbone trajectory by line-of-sight pruning, thereby reducing redundant waypoints and limiting detours. Second, an enhanced tabular Q-learning model is executed in an expanded eight-direction action space, and policy learning is guided by a multi-objective reward that jointly encourages distance reduction, alignment with ocean current and wind-induced forces for energy saving, smooth heading variation to suppress excessive steering, and maintenance of a safety margin near obstacles. Third, an adaptive artificial potential field (APF) module is used for real-time local correction, providing repulsion in high-risk regions and assisting trajectory smoothing to reduce unnecessary turning operations. A decision bias strategy further couples instantaneous APF forces with long-term state–action values, while the influence weight is adaptively adjusted according to environmental complexity. The algorithm is validated on the randomly generated marine grid maps and on the real-world satellite map scenario, with comparisons against a conventional four-direction Q-learning baseline. Across randomized tests, average path length, turning frequency, and the composite energy indicator are reduced by 22.3%, 55.6%, and 26.4%, respectively, and the success rate increases by 16%. The results indicate that integrating global guidance, adaptive learning, and local reactive decision making supports practical, energy-aware USV navigation. Full article
(This article belongs to the Special Issue Intelligent Sensing and Control Technology for Unmanned Vehicles)
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35 pages, 6720 KB  
Article
Vision-Based Vehicle State and Behavior Analysis for Aircraft Stand Safety
by Ke Tang, Liang Zeng, Tianxiong Zhang, Di Zhu, Wenjie Liu and Xinping Zhu
Sensors 2026, 26(6), 1821; https://doi.org/10.3390/s26061821 - 13 Mar 2026
Viewed by 505
Abstract
With the continuous elevation of aviation safety standards, accurate monitoring of ground support vehicles in aircraft stand areas has become a critical task for enhancing overall aircraft stand operational safety. Given the limitations of existing surface movement radar and multi-camera surveillance systems in [...] Read more.
With the continuous elevation of aviation safety standards, accurate monitoring of ground support vehicles in aircraft stand areas has become a critical task for enhancing overall aircraft stand operational safety. Given the limitations of existing surface movement radar and multi-camera surveillance systems in terms of cost, deployment complexity, and coverage, this paper proposes a lightweight vision-based framework for vehicle state perception and spatiotemporal behavior analysis oriented toward aircraft stand safety. Leveraging existing fixed monocular monitoring resources in the stand area, the framework first establishes a precise mapping from image pixel coordinates to the physical plane through self-calibration and homography transformation utilizing scene line features, thereby achieving unified spatial measurement of vehicle targets. Subsequently, it integrates an improved lightweight YOLO detector (incorporating Ghost modules and CBAM for noise suppression) with the ByteTrack tracking algorithm to enable stable extraction of vehicle trajectories under complex occlusion conditions. Finally, by combining functional zone division within the stand, a semantic map is constructed, and a behavior analysis method based on a spatiotemporal finite state machine is proposed. This method performs joint reasoning by fusing multi-dimensional constraints including position, zone, and time, enabling automatic detection of abnormal behaviors such as “intrusion into restricted areas” and “abnormal stop.” Quantitative evaluations demonstrate the framework’s efficacy: it achieves an average physical localization error (RMSE) of 0.32 m, and the improved detection model reaches an accuracy (mAP@50) of 90.4% for ground support vehicles. In tests simulating typical violation scenarios, the system achieved high recall (96.0%) and precision (95.8%) rates in detecting ‘area intrusion’ and ‘abnormal stop’ violations, respectively. These results, achieved using only existing surveillance cameras, validate its potential as a cost-effective and easily deployable tool to augment existing safety monitoring systems for airport ground operations. Full article
(This article belongs to the Special Issue Intelligent Sensing and Control Technology for Unmanned Vehicles)
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23 pages, 1351 KB  
Article
Multi-Observer Fusion Based Minimal-Sensor Adaptive Control for Ship Dynamic Positioning Systems
by Yanbin Wu, Xiaomeng He, Linlong Shi and Shengli Dong
Sensors 2025, 25(3), 679; https://doi.org/10.3390/s25030679 - 23 Jan 2025
Cited by 5 | Viewed by 1438
Abstract
This paper proposes an adaptive dynamic positioning (DP) control method based on a multi-observer fusion architecture with minimal sensor requirements. A sliding mode observer is designed based on a high- and low-frequency superposition model to filter high-frequency state variables, while a finite-time convergence [...] Read more.
This paper proposes an adaptive dynamic positioning (DP) control method based on a multi-observer fusion architecture with minimal sensor requirements. A sliding mode observer is designed based on a high- and low-frequency superposition model to filter high-frequency state variables, while a finite-time convergence disturbance observer estimates unknown time-varying low-frequency disturbances online. For efficient handling of model uncertainties, a single-parameter learning neural network is implemented that requires only one parameter to be estimated online. The control system employs auxiliary dynamic systems to handle input saturation constraints and considers thruster system dynamics. Theoretical analysis demonstrates the stability of the observer-fusion control strategy, while simulation results based on the SimuNPS platform validate its effectiveness in state estimation and disturbance rejection compared to traditional sensor-dependent methods. Full article
(This article belongs to the Special Issue Intelligent Sensing and Control Technology for Unmanned Vehicles)
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14 pages, 5395 KB  
Article
Energy-Efficient Route Planning Method for Ships Based on Level Set
by Jiejian Zhu, Haiqing Shen, Qiangrong Tang, Zhong Qin and Yalei Yu
Sensors 2025, 25(2), 381; https://doi.org/10.3390/s25020381 - 10 Jan 2025
Cited by 5 | Viewed by 2095
Abstract
To reduce the fuel consumption of ships’ oceanic voyages, this study incorporates the influence of ocean currents into the traditional level set algorithm and proposes a route planning algorithm capable of identifying energy-efficient routes in complex and variable sea conditions. The approach introduces [...] Read more.
To reduce the fuel consumption of ships’ oceanic voyages, this study incorporates the influence of ocean currents into the traditional level set algorithm and proposes a route planning algorithm capable of identifying energy-efficient routes in complex and variable sea conditions. The approach introduces the influence factor of ocean current to optimize routing in dynamically changing marie environments. First, models for the energy consumption of ships and flow fields are established. The level set curve is then evolved by integrating the flow environment and energy consumption gradient, solving the Hamilton–Jacobi equation with energy consumption parameters. The optimal path is subsequently determined through backtracking along the energy consumption gradient, enabling energy-efficient route planning from the starting point to the endpoint in complex ocean conditions. To verify the effectiveness of the proposed algorithm, its performance is evaluated through two case studies, comparing energy consumption under different environmental conditions. The experimental results demonstrate that, compared to the shortest path method based on the level set algorithm, the proposed approach achieves an energy saving rate of approximately 2.1% in obstacle-free environments and 1.4% in environments with obstacles. Full article
(This article belongs to the Special Issue Intelligent Sensing and Control Technology for Unmanned Vehicles)
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