Advances in the Unmanned System: Control and Autonomous Applications

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 241

Special Issue Editors


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Guest Editor
School of Aerospace Engineering, North University of China, 3 Xueyuan Road, Taiyuan, China
Interests: unmanned systems; motion control; terrain-aided navigation; path planning

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Guest Editor
School of Aerospace Engineering, North University of China, 3 Xueyuan Road, Taiyuan, China
Interests: navigation guidance and control; cluster UAV cooperative control technology; multi-source information fusion cooperative positioning technology; anti-swarm fire coordination technology and autonomy

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Guest Editor
College of Harbor, Coastal and Offshore Engineering, Hohai University, Nanjing 210024, China
Interests: autonomous underwater vehicles; marine robotic dynamics and control; marine environmental sensing
Special Issues, Collections and Topics in MDPI journals
National Key Laboratory of Autonomous Marine Vehicle Technology, Harbin Engineering University, Harbin 150001, China
Interests: autonomous underwater vehicles; underwater navigation; marine environmental sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of artificial intelligence, sensor fusion, and autonomous control technologies, unmanned systems—including both individual platforms and unmanned swarms—have emerged as a transformative force across diverse fields such as aerospace, maritime navigation, smart logistics, and industrial inspection. This Special Issue focuses on recent advances in the control and autonomous applications of unmanned systems, with particular attention given to unmanned swarm control and its practical deployments. It aims to provide a platform for disseminating cutting-edge research, covering innovative control strategies, autonomous decision-making mechanisms, environmental perception technologies, swarm collaboration, and practical explorations involving both individual- and swarm-based unmanned systems. By showcasing high-quality research, this Special Issue aims to promote technological breakthroughs and interdisciplinary integration, addressing key challenges in the reliability, adaptability, intelligence, and swarm coordination of unmanned systems to facilitate their broader and more efficient deployment in real-world scenarios.

Dr. Pengyun Chen
Dr. Pengfei Zhang
Dr. Rupeng Wang
Dr. Teng Ma
Guest Editors

Manuscript Submission Information

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Keywords

  • unmanned system
  • autonomous control
  • autonomous decision-making
  • unmanned system applications
  • intelligent navigation
  • AI-driven unmanned systems
  • swarm collaboration
  • cooperative control
  • path planning
  • intelligent algorithms

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Published Papers (1 paper)

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Research

18 pages, 3822 KB  
Article
An Efficient Odor Source Localization Method for Wheeled Mobile Robots in Indoor Ventilated Environments
by Xutong Ye, Boxuan Guo, Yujiao Gu, Haifeng Jiu and Shuo Pang
Technologies 2026, 14(5), 279; https://doi.org/10.3390/technologies14050279 - 4 May 2026
Abstract
Odor source localization (OSL) using mobile robots in indoor ventilated environments remains challenging due to turbulent dispersion, uneven concentration distribution, and weak robustness in conventional algorithms. This paper proposes an efficient OSL strategy for wheeled mobile robots by integrating time-varying smoke plume modeling, [...] Read more.
Odor source localization (OSL) using mobile robots in indoor ventilated environments remains challenging due to turbulent dispersion, uneven concentration distribution, and weak robustness in conventional algorithms. This paper proposes an efficient OSL strategy for wheeled mobile robots by integrating time-varying smoke plume modeling, particle filtering (PF), and information entropy. A multi-sensor fusion perception system is developed, including an LDS-02 LiDAR, ultrasonic anemometer, and PMS5003 particle sensor. The proposed method employs a plume model to characterize odor particle propagation, uses particle filtering to estimate the posterior distribution of the source location, and introduces information entropy to quantify perceptual uncertainty and optimize robot path planning. Comparative simulations and real-world experiments are conducted in a 5 m × 3 m indoor ventilated environment against the traditional gradient–bionic hybrid algorithm. Results demonstrate that the proposed algorithm significantly reduces the average search time and improves the localization success rate. The long-distance localization success rate exceeds 90%, and the positioning error is controlled within 0.5 m. The proposed strategy provides a reliable and practical solution for OSL in indoor ventilation environments. Full article
(This article belongs to the Special Issue Advances in the Unmanned System: Control and Autonomous Applications)
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