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Machines
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Autonomous Navigation of Mobile Robots and UAVs, 2nd Edition
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Autonomous Navigation of Mobile Robots and UAVs, 2nd Edition

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

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 5197

Special Issue Editors

Dr. Changwon Kim

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Guest Editor
School of Mechanical Engineering, Pukyong National University, Busan 48513, Republic of Korea
Interests: autonomous mobile robot navigation; autonomous system-based services; path planning; smart mechanism; intelligent control; intention-based control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Seong-Jin Kwon

E-Mail
Guest Editor
Department of Automotive Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan-si, Republic of Korea
Interests: vehicle dynamics and control; autonomous driving fusion systems; field operational test for autonomous vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of the fourth industrial revolution, robots with autonomous driving technology are receiving significant attention across various fields. Mobile robots are not limited to the ground but are expanding their domain to underwater regions, water (surface), and air. In this Special Issue, we include excellent studies across all areas of recognition, decision-making, and the control of autonomous driving robots applied to diverse areas. It is expected that various research results will be shared, from experimental studies using robot hardware to simulation-based research using simulation tools.

Dr. Changwon Kim
Prof. Dr. Seong-Jin Kwon
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 250 words) can be sent to the Editorial Office for assessment.

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

  • path planning for autonomous agents
  • enhancing the localization of a mobile robot
  • motion control of a mobile robot
  • application of mobile robots to specific areas
  • multi-agent management system

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Related Special Issue

Published Papers (3 papers)

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Research

30 pages, 470 KB  
Article
Clustered Reverse Resumable A* Algorithm for Warehouse Robot Pathfinding
by Gábor Csányi and László Z. Varga
Machines 2025, 13(12), 1127; https://doi.org/10.3390/machines13121127 - 8 Dec 2025
Viewed by 790
Abstract
Robots are widely used to carry goods in automated warehouses. Planning collision-free paths for multiple robots which are continuously given new goals is called Lifelong Multi-Agent Pathfinding. In a lifelong environment, conflicts may emerge among the robots, and continuous replanning is needed. We [...] Read more.
Robots are widely used to carry goods in automated warehouses. Planning collision-free paths for multiple robots which are continuously given new goals is called Lifelong Multi-Agent Pathfinding. In a lifelong environment, conflicts may emerge among the robots, and continuous replanning is needed. We propose, develop, implement, and evaluate the novel approach called the Clustered Reverse Resumable A* (CRRA*) algorithm to enhance the continuous computation of the shortest path from the changing position of a robot to its goal. The Priority Inheritance with Backtracking (PIBT) algorithm is the currently known most efficient algorithm to handle the pathfinding of thousands of robots in a warehouse. The PIBT algorithm requires that in each step each robot evaluates the distances from its surrounding positions to its goal; therefore, we integrate the CRRA* algorithm with the PIBT algorithm to evaluate CRRA*. The evaluation results show that the CRRA* leads to a significant reduction in computation time, especially in larger warehouses where the obstacles form well-separated spaces. At the same time, the degradation in solution quality is minimal. The CRRA* algorithm is more efficient in larger warehouses than the plain Reverse Resumable A* (RRA*) algorithm. The faster computation of slightly suboptimal paths can be useful in many practical applications, especially in situations where real-time planning is more important than finding the optimal paths. CRRA* can also be used as a heuristic in any multi-agent pathfinding solution to obtain a faster, nearly accurate heuristic. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAVs, 2nd Edition)
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31 pages, 9881 KB  
Article
Guide Robot Based on Image Processing and Path Planning
by Chen-Hsien Yang and Jih-Gau Juang
Machines 2025, 13(7), 560; https://doi.org/10.3390/machines13070560 - 27 Jun 2025
Viewed by 1236
Abstract
While guide dogs remain the primary aid for visually impaired individuals, robotic guides continue to be an important area of research. This study introduces an indoor guide robot designed to physically assist a blind person by holding their hand with a robotic arm [...] Read more.
While guide dogs remain the primary aid for visually impaired individuals, robotic guides continue to be an important area of research. This study introduces an indoor guide robot designed to physically assist a blind person by holding their hand with a robotic arm and guiding them to a specified destination. To enable hand-holding, we employed a camera combined with object detection to identify the human hand and a closed-loop control system to manage the robotic arm’s movements. For path planning, we implemented a Dueling Double Deep Q Network (D3QN) enhanced with a genetic algorithm. To address dynamic obstacles, the robot utilizes a depth camera alongside fuzzy logic to control its wheels and navigate around them. A 3D point cloud map is generated to determine the start and end points accurately. The D3QN algorithm, supplemented by variables defined using the genetic algorithm, is then used to plan the robot’s path. As a result, the robot can safely guide blind individuals to their destinations without collisions. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAVs, 2nd Edition)
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14 pages, 6946 KB  
Article
Microcontroller Unit-Based Gesture Recognition System
by Jakub Grabarczyk and Agnieszka Lazarowska
Machines 2025, 13(2), 90; https://doi.org/10.3390/machines13020090 - 23 Jan 2025
Cited by 3 | Viewed by 2497
Abstract
This article describes the design, construction, and programming of a microcontroller-based system, which uses hand gestures with machine learning algorithms to control an unmanned aerial vehicle (UAV). A neural network is used as a model, and an IMU sensor detects the gestures. The [...] Read more.
This article describes the design, construction, and programming of a microcontroller-based system, which uses hand gestures with machine learning algorithms to control an unmanned aerial vehicle (UAV). A neural network is used as a model, and an IMU sensor detects the gestures. The developed gesture recognition system, besides the IMU sensor, is composed of a Raspberry Pi Pico and radio communication module. The benefits and drawbacks of deploying machine learning models on microcontrollers, as opposed to units superior in terms of clocking are also discussed. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAVs, 2nd Edition)
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