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Machines
Special Issues
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 May 2024 | Viewed by 541

Special Issue Editors

Dr. Wilfried Yves Hamilton Adoni

E-Mail Website
Guest Editor
Helmholtz-Zentrum Dresden-Rossendorf, Center for Advanced Systems Understanding, Untermarkt 20, 02826 Görlitz, Germany
Interests: unmanned aerial vehicle (UAV); drones; big data; machine learning; deep learning; distributed computing; autonomous systems; swarm; ROS; high-performance computing; computer vision; formal verification; multi-agents; distributed storage; parallel computing
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

Published Papers (1 paper)

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Research

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
Viewed by 210
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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