sensors-logo

Journal Browser

Journal Browser

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: 30 April 2025 | Viewed by 1659

Special Issue Editors


E-Mail Website
Guest Editor
College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
Interests: autonomous collision avoidance of unmanned vehicles
Special Issues, Collections and Topics in MDPI journals

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

23 pages, 1351 KiB  
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
Viewed by 518
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)
Show Figures

Figure 1

14 pages, 5395 KiB  
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 2 | Viewed by 689
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)
Show Figures

Figure 1

Back to TopTop