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Machines
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Autonomous Navigation of Mobile Robots and UAV
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Autonomous Navigation of Mobile Robots and UAV

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 3287

Special Issue Editors

Dr. Changwon Kim

E-Mail Website
Guest Editor
Intelligent Mobility Systems Laboratory, Pukyong National University, Busan, Republic of Korea
Interests: autonomous mobile robot navigation; autonomous system-based services; path planning; smart mechanism; intelligent control; intention-based control
Dr. Seong-Jin Kwon

E-Mail Website
Guest Editor
Department of Automotive Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan-si, Gyeongsangbuk-do, Republic of Korea
Interests: vehicle dynamics and control; autonomous driving fusion systems; field operational test for autonomous vehicles

Special Issue Information

Dear Colleagues,

In the era of the 4th 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, 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 research using robot hardware to simulation-based research using simulation tools.

Dr. Changwon Kim
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 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

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

Published Papers (3 papers)

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Research

20 pages, 7249 KiB  
Article
Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
by Yujin Shin, Chanhee Lee and Euiho Kim
Machines 2024, 12(3), 202; https://0-doi-org.brum.beds.ac.uk/10.3390/machines12030202 - 19 Mar 2024
Viewed by 753
Abstract
Real-time kinematic (RTK) positioning of the global navigation satellite systems (GNSS) is used to provide centimeter-level positioning accuracy. There are several ways to implement RTK but a Kalman filter-based RTK is preferred because of its superior capability to resolve GNSS carrier phase integer [...] Read more.
Real-time kinematic (RTK) positioning of the global navigation satellite systems (GNSS) is used to provide centimeter-level positioning accuracy. There are several ways to implement RTK but a Kalman filter-based RTK is preferred because of its superior capability to resolve GNSS carrier phase integer ambiguities. However, the positioning performance of the Kalman filter-based RTK is often compromised by various factors when it comes to determining a precise relative position vector between moving unmanned aerial vehicles (UAVs) equipped with low-cost GNSS receivers and antennas, where the locations of both GNSS antennas are not accurately known and change over time. Some of the critical factors that lead to a high rate of incorrect resolutions of carrier phase integer ambiguities are measurement time differences between GNSS receivers, frequent cycle slips with high noise in code and carrier phase measurements, and an improper Kalman filter gain due to a newly risen satellite. In this paper, effective methods to deal with those factors to achieve a seamless Kalman filter-based RTK performance in moving UAVs are presented. Using our extensive 45 flight tests data sets, conducted over a duration of 3 to 12 min, the RTK positioning results showed that the root-mean-square position error (RMSE) decreased by up to 95.13%, with an average of 65.31%, and that the percentage of epochs that passed the ratio test, which is the most common method for validating double differenced carrier phase integer ambiguity resolution, increased by up to 130%, with an average of 23.54%. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAV)
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18 pages, 1280 KiB  
Article
Sampled-Data Cooperative Adaptive Cruise Control for String-Stable Vehicle Platooning with Communication Delays: A Linear Matrix Inequality Approach
by Yong Hoon Jang and Han Sol Kim
Machines 2024, 12(3), 165; https://0-doi-org.brum.beds.ac.uk/10.3390/machines12030165 - 28 Feb 2024
Viewed by 872
Abstract
This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines [...] Read more.
This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAV)
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19 pages, 9185 KiB  
Article
Efficient Navigation and Motion Control for Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC Implementation
by Konchanok Vorasawad, Myoungkuk Park and Changwon Kim
Machines 2023, 11(12), 1050; https://0-doi-org.brum.beds.ac.uk/10.3390/machines11121050 - 25 Nov 2023
Viewed by 1058
Abstract
The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that [...] Read more.
The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAV)
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