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Machines
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Selected Papers from the 12th International Conference on Control, Mechatronics...
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Selected Papers from the 12th International Conference on Control, Mechatronics and Automation (ICCMA 2024)

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 1426

Special Issue Editor

Dr. Xinli Du

E-Mail Website
Guest Editor
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
Interests: smart surgical tools and multiple sensing technology

Special Issue Information

Dear Colleagues,

Control, mechatronics and automation is an interdisciplinary field that combines control engineering, mechanical engineering, and electronic engineering. In recent years, advancements in artificial intelligence, the internet of things (IoT) and big data analytics have been driving the development of this field. Control, mechatronics and automation has become increasingly important in modern manufacturing and production systems, as well as in robotics, aerospace, transportation, and medical industries. This field is rapidly evolving with new advances in sensors, actuators, control algorithms and communication technologies. 

The 12th International Conference on Control, Mechatronics and Automation (ICCMA 2024, https://www.iccma.org/topics.html) will be held at Brunel University London, UK, from November 11 to 13, 2024. This conference, sponsored by Brunel University London, UK, provides a platform for researchers, scholars, engineers and students from around the world to present their research, share ideas and foster research collaborations between academia and industry. 

Papers selected by the conference review chairs and cochairs will be recommended for this Special Issue. However, non-conference articles that are related to the theme of this Special Issue are also welcome for submission.

Dr. Xinli Du
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. 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

  • control theory and applications
  • mechatronics and robotics
  • industrial automation and process control
  • intelligent systems and machine learning

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

Published Papers (1 paper)

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Research

29 pages, 4682 KiB  
Article
LSAF-LSTM-Based Self-Adaptive Multi-Sensor Fusion for Robust UAV State Estimation in Challenging Environments
by Mahammad Irfan, Sagar Dalai, Petar Trslic, James Riordan and Gerard Dooly
Machines 2025, 13(2), 130; https://doi.org/10.3390/machines13020130 - 9 Feb 2025
Viewed by 1130
Abstract
Unmanned aerial vehicle (UAV) state estimation is fundamental across applications like robot navigation, autonomous driving, virtual reality (VR), and augmented reality (AR). This research highlights the critical role of robust state estimation in ensuring safe and efficient autonomous UAV navigation, particularly in challenging [...] Read more.
Unmanned aerial vehicle (UAV) state estimation is fundamental across applications like robot navigation, autonomous driving, virtual reality (VR), and augmented reality (AR). This research highlights the critical role of robust state estimation in ensuring safe and efficient autonomous UAV navigation, particularly in challenging environments. We propose a deep learning-based adaptive sensor fusion framework for UAV state estimation, integrating multi-sensor data from stereo cameras, an IMU, two 3D LiDAR’s, and GPS. The framework dynamically adjusts fusion weights in real time using a long short-term memory (LSTM) model, enhancing robustness under diverse conditions such as illumination changes, structureless environments, degraded GPS signals, or complete signal loss where traditional single-sensor SLAM methods often fail. Validated on an in-house integrated UAV platform and evaluated against high-precision RTK ground truth, the algorithm incorporates deep learning-predicted fusion weights into an optimization-based odometry pipeline. The system delivers robust, consistent, and accurate state estimation, outperforming state-of-the-art techniques. Experimental results demonstrate its adaptability and effectiveness across challenging scenarios, showcasing significant advancements in UAV autonomy and reliability through the synergistic integration of deep learning and sensor fusion. Full article
(This article belongs to the Special Issue Selected Papers from the 12th International Conference on Control, Mechatronics and Automation (ICCMA 2024))
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