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Machines
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Advances in AI-Enabled Applications for Robotics and Autonomous Systems
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Advances in AI-Enabled Applications for Robotics and Autonomous Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3887

Special Issue Editors

Dr. Wilfried Yves Hamilton Adoni

E-Mail Website
Guest Editor
Center for Advanced Systems Understanding (CASUS)-Helmholtz Institute Freiberg for Resource Technology (HIF), Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, 02826 Görlitz, Germany
Interests: drones; uav; swarming; robotic; AI; big data; HPC; smart agriculture and smart cities
Special Issues, Collections and Topics in MDPI journals
Dr. Moez Krichen

E-Mail Website
Guest Editor
FCSIT, Al-Baha University, Al-Baha 65528, Saudi Arabia
Interests: formal verification; drone model checking; IoT; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jamal Berrich

E-Mail Website
Guest Editor
ENSAO, Mohammed First University, Oujda 60000, Morocco
Interests: drone technology; drone systems; drone hardware; unmanned aerial vehicle (UAV); artificial intelligence applied to drone (multi-agents system, computer vision, machine learning, deep learning, reinforcement learning); autonomous drone; drone software; drone building; drone simulation and test; drone mission optimization and estimation; drone obstacle avoidance

Special Issue Information

Dear Colleagues,

With the rapid advancements in artificial intelligence (AI) and drone technologies, there has been a growing interest in exploring their synergistic potential across various domains. AI-powered drones have revolutionized industries such as agriculture, transportation, surveillance, disaster management, and many more. The integration of AI and drones offers unprecedented capabilities, enabling autonomous navigation, intelligent decision making, and enhanced data collection and analysis. To foster the exchange of knowledge and advancements in this exciting field, we are pleased to announce a Special Issue dedicated to the exploration of AI and drones.

Presentation of the Call for Papers:

We invite researchers and experts from academia and industry to contribute their original research findings, case studies, and innovative ideas to this Special Issue. This call for papers aims to gather high-quality research contributions that explore the latest developments, challenges, and opportunities in the use of AI and drones. We encourage submissions that demonstrate novel applications, theoretical advancements, experiments, and practical implementations pertaining to this interdisciplinary field.

Topics Covered:

The Special Issue welcomes submissions covering a wide range of topics related to AI and drones, including, but not limited to, the following:

  • Autonomous drone navigation and control using AI techniques;
  • AI-based object detection, recognition, and tracking for drones;
  • Machine learning and deep learning algorithms for drone data analysis;
  • Swarm intelligence and collective behavior of AI-powered drones;
  • AI-driven decision-making and mission planning in drone applications;
  • Sensor fusion and data integration techniques for drone-based AI systems;
  • AI-enabled drone applications in agriculture, environmental monitoring, and forestry;
  • Drone-based surveillance and security systems utilizing AI algorithms;
  • AI-driven drone imaging, mapping, and 3D reconstruction techniques;
  • Ethical and legal considerations in the integration of AI and drones;
  • Human-drone interaction and collaboration in AI-enabled drone systems;
  • Edge computing and distributed AI for real-time drone applications.

This list is not exhaustive, and we welcome submissions on related topics that contribute to the advancement of AI and drone technologies.

We look forward to receiving your contributions and witnessing the collective knowledge and insights that will shape the future of AI and drones. Join us in this exciting journey to explore the limitless possibilities that arise from the integration of AI and drones.

Sincerely,

Dr. Wilfried Yves Hamilton Adoni
Dr. Moez Krichen
Dr. Jamal Berrich
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. Machines is an international peer-reviewed open access monthly 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
  • drones
  • swarm robotics
  • unmanned aerial vehicle
  • machine learning
  • autonomous systems

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

Published Papers (3 papers)

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Research

18 pages, 1518 KiB  
Article
Nonblocking Modular Supervisory Control of Discrete Event Systems via Reinforcement Learning and K-Means Clustering
by Junjun Yang, Kaige Tan and Lei Feng
Machines 2025, 13(7), 559; https://doi.org/10.3390/machines13070559 (registering DOI) - 27 Jun 2025
Abstract
Traditional supervisory control methods for the nonblocking control of discrete event systems often suffer from exponential computational complexity. Reinforcement learning-based approaches mitigate state explosion by sampling many random sequences instead of computing the synchronous product of multiple modular supervisors, but they struggle with [...] Read more.
Traditional supervisory control methods for the nonblocking control of discrete event systems often suffer from exponential computational complexity. Reinforcement learning-based approaches mitigate state explosion by sampling many random sequences instead of computing the synchronous product of multiple modular supervisors, but they struggle with limited reachable state spaces. A primary novelty of this study is to use the K-means clustering method for online inference with the learned state-action values. The clustering method divides all events at a state into the good group and the bad group. The events in the good group are allowed by the supervisor. The obtained supervisor policy can ensure both system constraints and larger control freedom compared to conventional RL-based supervisors. The proposed framework is validated by two case studies: an industrial transfer line (TL) system and an automated guided vehicle (AGV) system. In the TL case study, nonblocking reachable states increase from 56 to 72, while in the AGV case study, a substantial expansion from 481 to 3558 states is observed. Our new method achieves a balance between computational efficiency and nonblocking supervisory control. Full article
(This article belongs to the Special Issue Advances in AI-Enabled Applications for Robotics and Autonomous Systems)
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18 pages, 3252 KiB  
Article
Improved QT-Opt Algorithm for Robotic Arm Grasping Based on Offline Reinforcement Learning
by Haojun Zhang, Sheng Zeng, Yaokun Hou, Haojie Huang and Zhezhuang Xu
Machines 2025, 13(6), 451; https://doi.org/10.3390/machines13060451 - 24 May 2025
Viewed by 413
Abstract
Reinforcement learning plays a crucial role in the field of robotic arm grasping, providing a promising approach for the development of intelligent and adaptive grasping strategies. Due to distribution shift and local optimum in action, traditional online reinforcement learning is difficult to use [...] Read more.
Reinforcement learning plays a crucial role in the field of robotic arm grasping, providing a promising approach for the development of intelligent and adaptive grasping strategies. Due to distribution shift and local optimum in action, traditional online reinforcement learning is difficult to use existing grasping datasets, leading to low sample efficiency. This study proposes an improved QT-Opt algorithm for robotic arm grasping based on offline reinforcement learning. This improved algorithm proposes the Particle Swarm Optimization (PSO) to identify the action with the highest value within the robotic arm’s action space. Furthermore, a regularization term is proposed during the value iteration process to facilitate the learning of a conservative Q-function, enabling precise estimation of the robotic arm’s action values. Experimental results indicate that the improved QT-Opt algorithm achieves higher average grasping success rates when trained on multiple offline grasping datasets and demonstrates improved stability throughout the training process. Full article
(This article belongs to the Special Issue Advances in AI-Enabled Applications for Robotics and Autonomous Systems)
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16 pages, 4673 KiB  
Article
Highly Self-Adaptive Path-Planning Method for Unmanned Ground Vehicle Based on Transformer Encoder Feature Extraction and Incremental Reinforcement Learning
by Tao Zhang, Jie Fan, Nana Zhou and Zepeng Gao
Machines 2024, 12(5), 289; https://doi.org/10.3390/machines12050289 - 26 Apr 2024
Cited by 1 | Viewed by 2152
Abstract
Path planning is an indispensable component in guiding unmanned ground vehicles (UGVs) from their initial positions to designated destinations, aiming to determine trajectories that are either optimal or near-optimal. While conventional path-planning techniques have been employed for this purpose, planners utilizing reinforcement learning [...] Read more.
Path planning is an indispensable component in guiding unmanned ground vehicles (UGVs) from their initial positions to designated destinations, aiming to determine trajectories that are either optimal or near-optimal. While conventional path-planning techniques have been employed for this purpose, planners utilizing reinforcement learning (RL) exhibit superior adaptability within exceedingly complex and dynamic environments. Nevertheless, existing RL-based path planners encounter several shortcomings, notably, redundant map representations, inadequate feature extraction, and limited adaptiveness across diverse environments. In response to these challenges, this paper proposes an innovative and highly self-adaptive path-planning approach based on Transformer encoder feature extraction coupled with incremental reinforcement learning (IRL). Initially, an autoencoder is utilized to compress redundant map representations, providing the planner with sufficient environmental data while minimizing dimensional complexity. Subsequently, the Transformer encoder, renowned for its capacity to analyze global long-range dependencies, is employed to capture intricate correlations among UGV statuses at continuous intervals. Finally, IRL is harnessed to enhance the path planner’s generalization capabilities, particularly when the trained agent is deployed in environments distinct from its training counterparts. Our empirical findings demonstrate that the proposed method outperforms traditional uniform-sampling-based approaches in terms of execution time, path length, and trajectory smoothness. Furthermore, it exhibits a fivefold increase in adaptivity compared to conventional transfer-learning-based fine-tuning methodologies. Full article
(This article belongs to the Special Issue Advances in AI-Enabled Applications for Robotics and Autonomous Systems)
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