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Electronics
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Control and Navigation of Robotics and Unmanned Aerial Vehicles
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Control and Navigation of Robotics and Unmanned Aerial Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 628

Special Issue Editor

Dr. Zhou Zhang

E-Mail Website
Guest Editor
Farmingdale State College, State University of New York, 2350 NY-110, Farmingdale, NY 11735, USA
Interests: robotics; dynamics and control; virtual reality; computer vision; engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in robotics and unmanned aerial vehicles (UAVs) have revolutionized a wide range of sectors, including industry, healthcare, agriculture, defense, gaming, augmented reality, and smart cities. This highly interdisciplinary domain integrates expertise in computer vision, robotics, algorithms, mechanism design, sensors, mechatronics, control systems, simulation, and virtual reality. The ability of these technologies to autonomously navigate complex environments and execute precise tasks represents a cornerstone of their innovation, making control and navigation essential areas of research.

This Special Issue highlights cutting-edge developments, novel methodologies, and practical implementations in the control and navigation of robotics and UAVs. We welcome high-quality contributions that propose innovative solutions to address challenges such as real-time decision-making, path planning, obstacle avoidance, sensor integration, and collaborative autonomous systems. Topics of interest include, but are not limited to, the following:

  • Advanced control algorithms for robotic manipulators and UAVs;
  • AI and machine learning techniques for navigation and control;
  • Path planning and optimization in dynamic environments;
  • Sensor fusion and perception systems for enhanced autonomy;
  • Collaborative navigation in multi-robot and swarm systems;
  • Applications for autonomous vehicles, drones, and industrial robots;
  • Robustness, safety, and resilience in autonomous systems;
  • Simulation frameworks for control and navigation testing;
  • Virtual reality environments for robotics and UAV development;
  • Emerging trends in hybrid and bio-inspired control systems.

This Special Issue aims to provide a platform for researchers, academics, and industry professionals to share groundbreaking insights and foster innovation and cross-disciplinary collaboration in the fields of robotics and UAV navigation.

We look forward to receiving your contributions.

Dr. Zhou Zhang
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. Electronics 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 2400 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

  • robotics control
  • UAV navigation
  • simulation frameworks
  • virtual reality in robotics
  • AI
  • machine learning
  • sensor fusion
  • path planning
  • optimization

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Published Papers (1 paper)

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Research

26 pages, 3726 KiB  
Article
Deep Reinforcement Learning for UAV Target Search and Continuous Tracking in Complex Environments with Gaussian Process Regression and Prior Policy Embedding
by Zhihui Feng, Xitai Na, Shiji Hai, Qingbin Sun and Jinshuo Shi
Electronics 2025, 14(7), 1330; https://doi.org/10.3390/electronics14071330 - 27 Mar 2025
Viewed by 474
Abstract
In recent years, unmanned aerial vehicles (UAVs) have shown substantial application value in continuous target tracking tasks in complex environments. Due to the target’s movement behavior and the complexities of the surrounding environment, the UAV is prone to losing track of the target. [...] Read more.
In recent years, unmanned aerial vehicles (UAVs) have shown substantial application value in continuous target tracking tasks in complex environments. Due to the target’s movement behavior and the complexities of the surrounding environment, the UAV is prone to losing track of the target. To tackle this issue, this paper presents a reinforcement learning (RL) approach that combines UAV target search and tracking. During the target search phase, spatial information entropy is employed to guide the UAV in avoiding redundant searches, thus enhancing information acquisition efficiency. In the event of target loss, Gaussian process regression (GPR) is employed to predict the target trajectory, thereby reducing the time needed for target re-localization. In addition, to address sample efficiency limitations in conventional RL, a Kolmogorov–Arnold networks-based deep deterministic policy gradient (KbDDPG) algorithm with prior policy embedding is proposed for controller training.Simulation results demonstrate that the proposed method outperforms traditional methods in target search and tracking tasks within complex environments. It improves the UAV’s ability to re-locate the target after loss. The proposed KbDDPG efficiently leverages prior policy, leading to accelerated convergence and enhanced performance. Full article
(This article belongs to the Special Issue Control and Navigation of Robotics and Unmanned Aerial Vehicles)
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