Intelligent Perception, Planning and Control Technology for Autonomous Unmanned Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 February 2026 | Viewed by 1962

Special Issue Editors


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Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: intelligent systems; robot technology; nonlinear control systems; renewable energy systems
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Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Weihai 264209, China
Interests: renewable energy systems; motor drive; grid-connected inverters control; PMSMs; nonlinear control systems; intelligent systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: pattern recognition; artificial intelligence and deep learning networks; intelligent systems

E-Mail Website
Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: multi-agent systems; path planning and decision; state estimation; intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the breakthrough development of automation and artificial intelligence technology, the application value of unmanned systems in manufacturing, agriculture, security, and medical care has become increasingly prominent. Mission planning, perception, and control are key technologies that improve autonomous decision-making and the intelligent operation of unmanned systems. On the one hand, while a series of developments have been made in the control and decision-making technologies of typical unmanned systems such as unmanned vehicles and unmanned aircraft, new theoretical and technological innovations are urgently needed for the reliable control and decision-making of extremely complex industrial systems represented by robots and power systems. On the other hand, the cluster intelligence of multi-modal heterogeneous unmanned systems, autonomous decision-making, and collaboration in wide-area complex and variable task scenarios pose new challenges.

By integrating both conference proceedings and external submissions, this Special Issue will enrich the existing dialogue within the technology community. It will expand on the discussions initiated at the 44th Chinese Control Conference (CCC 2025), providing a broader, more comprehensive view that bridges current research with emerging trends.

This invited paper invites original papers including innovative ideas, concepts, discoveries, improvements, and applications related to “Intelligent perception, planning, and control technology for autonomous unmanned systems”. We hope researchers in relevant fields take advantage of this opportunity to report the recent results to the scientific community. The list of topics includes, but is not limited to:

  • Intelligent control and trajectory planning of robots;
  • Structural design, modeling, and high dynamic control of soft robots;
  • Cooperative perception and mapping of heterogeneous unmanned systems;
  • Dynamics and intelligent control of space unmanned systems;
  • Multi-task decision-making and game confrontation for unmanned systems in wide-area environments;
  • High-precision control of robot joint motors;
  • Multi-motor coordination and control;
  • Power system scheduling and energy management;
  • Optimization and control of smart grids.

Dr. Zhuang Liu
Dr. Xinpo Lin
Prof. Dr. Yue Zhao
Dr. Yabin Gao
Prof. Dr. Jianxing Liu
Guest Editors

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Keywords

  • unmanned systems
  • industrial systems
  • task planning
  • intelligent perception
  • advanced control

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Published Papers (2 papers)

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Research

22 pages, 17573 KB  
Article
Robust UAV Path Planning Using RSS in GPS-Denied and Dense Environments Based on Deep Reinforcement Learning
by Kyounghun Kim, Joonho Seon, Jinwook Kim, Jeongho Kim, Youngghyu Sun, Seongwoo Lee, Soohyun Kim, Byungsun Hwang, Mingyu Lee and Jinyoung Kim
Electronics 2025, 14(19), 3844; https://doi.org/10.3390/electronics14193844 - 28 Sep 2025
Viewed by 836
Abstract
A wide range of research has been conducted on path planning and collision avoidance to enhance the operational efficiency of unmanned aerial vehicles (UAVs). The existing works have mainly assumed an environment with static obstacles and global positioning system (GPS) signals. However, practical [...] Read more.
A wide range of research has been conducted on path planning and collision avoidance to enhance the operational efficiency of unmanned aerial vehicles (UAVs). The existing works have mainly assumed an environment with static obstacles and global positioning system (GPS) signals. However, practical environments have often been involved with dynamic obstacles, dense areas with numerous obstacles in confined spaces, and blocked GPS signals. In order to consider these issues for practical implementation, a deep reinforcement learning (DRL)-based method is proposed for path planning and collision avoidance in GPS-denied and dense environments. In the proposed method, robust path planning and collision avoidance can be conducted by using the received signal strength (RSS) value with the extended Kalman filter (EKF). Additionally, the attitude of the UAV is adopted as part of the action space to enable the generation of smooth trajectories. Performance was evaluated under single- and multi-target scenarios with numerous dynamic obstacles. Simulation results demonstrated that the proposed method can generate smoother trajectories and shorter path lengths while consistently maintaining a lower collision rate compared to conventional methods. Full article
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15 pages, 1900 KB  
Article
RRT-GPMP2: A Motion Planner for Mobile Robots in Complex Maze Environments
by Jiawei Meng, Yuanchang Liu, Richard Bucknall and Danail Stoyanov
Electronics 2025, 14(14), 2888; https://doi.org/10.3390/electronics14142888 - 18 Jul 2025
Cited by 1 | Viewed by 652
Abstract
With the development of science and technology, mobile robots are playing a significant role in the new round of world revolution. Mobile robots could serve as assistants or substitutes for humans across a wide range of applications. To enhance mobile robot automation, advanced [...] Read more.
With the development of science and technology, mobile robots are playing a significant role in the new round of world revolution. Mobile robots could serve as assistants or substitutes for humans across a wide range of applications. To enhance mobile robot automation, advanced motion planners must be integrated to handle diverse environments. Navigating complex maze environments is a key challenge for mobile robots in various practical scenarios. Therefore, this article proposes a novel hierarchical motion planner named the rapidly exploring random tree-based Gaussian process motion planner 2, which aims to tackle the motion planning problem for mobile robots in complex maze environments. Specifically, the proposed motion planner successfully combines the advantages of the trajectory optimisation motion planning method and sampling-based motion planning method. To validate the performance and practicability of the proposed motion planner, we tested it in a series of self-constructed maze simulations and applied it on a surface marine robot in a high-fidelity maritime simulation environment in the Robot operating system. Full article
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