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 6051

Special Issue Editors


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

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

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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

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Published Papers (6 papers)

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Research

23 pages, 1613 KiB  
Article
Classification Scheme for the Three-Point Dubins Problem
by Daniela De Palma and Gianfranco Parlangeli
Machines 2024, 12(9), 659; https://doi.org/10.3390/machines12090659 - 20 Sep 2024
Abstract
This paper proposes an optimal path type classification scheme for the three-point Dubins problem. It allows us to directly extract the shortest path type from a Dubins set, evaluating only the relative initial and final configurations with the via point position using a [...] Read more.
This paper proposes an optimal path type classification scheme for the three-point Dubins problem. It allows us to directly extract the shortest path type from a Dubins set, evaluating only the relative initial and final configurations with the via point position using a suitable partition of the Cartesian plane. Two alternative approaches are proposed to address the problem: an analytical approach and a heuristic one. The latter is revealed to be much faster from a computational point of view. The proposed classification logic makes the path planning for the three-point Dubins problem much more effective and suitable for real-time applications. Numerical examples are provided to show the efficiency of the proposed strategy. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAV)
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14 pages, 2244 KiB  
Article
Abnormal Driving Area Detection Using Multiple Vehicle Dynamic Model-Based Filter: Design and Experimental Validation
by Changmook Kang, Taehyung Lee and Jongho Shin
Machines 2024, 12(8), 564; https://doi.org/10.3390/machines12080564 - 17 Aug 2024
Viewed by 276
Abstract
The main concern of remote control systems for autonomous ground vehicles (AGVs) is to perform the given mission according to the purpose of the operator. Although most remote systems are composed of a screen-based architecture, they are insufficient to transfer sufficient information to [...] Read more.
The main concern of remote control systems for autonomous ground vehicles (AGVs) is to perform the given mission according to the purpose of the operator. Although most remote systems are composed of a screen-based architecture, they are insufficient to transfer sufficient information to the remote operator. Therefore, in this paper, we present and experimentally validate an abnormal driving area detection system using an interacting multiple model (IMM) filter for the remote control system. In the proposed IMM filter, the unknown dynamic behavior of the vehicle, which changes according to changes in the driving environment, was lumped into a parameter change of the system model. As a result, we can obtain the probability of each model representing the reliability of each model, but an index can be used to infer the current status of the AGV and the driving environment. The index can help us detect both unusual behavior of the AGV such as skidding or sliding, and areas with low-friction road conditions that are not confirmed by images from the camera sensor. Thus, the remote operator can directly decide whether to continue operating or not. The proposed method is simple but useful and meaningful for the remote operator compared to the image-only method. The overall procedure of the proposed method was experimentally validated via a multi-purpose AGV on rough unpaved proving ground. Nine abnormal driving areas were discovered on the ground. In five of these areas, vehicles consistently exhibited abnormal driving behavior. The remaining four areas were confirmed to be affected by variables such as weather conditions and vehicle tire wear. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAV)
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21 pages, 6716 KiB  
Article
A Velocity-Adaptive MPC-Based Path Tracking Method for Heavy-Duty Forklift AGVs
by Yajun Wang, Kezheng Sun, Wei Zhang and Xiaojun Jin
Machines 2024, 12(8), 558; https://doi.org/10.3390/machines12080558 - 15 Aug 2024
Viewed by 352
Abstract
In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid [...] Read more.
In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s2 for acceleration and −0.2 m/s2 for deceleration in straight paths and 0.3 m/s2 for acceleration with −0.15 m/s2 for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles. Full article
(This article belongs to the Special Issue Autonomous Navigation of Mobile Robots and UAV)
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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://doi.org/10.3390/machines12030202 - 19 Mar 2024
Cited by 1 | Viewed by 1098
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://doi.org/10.3390/machines12030165 - 28 Feb 2024
Viewed by 1143
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://doi.org/10.3390/machines11121050 - 25 Nov 2023
Cited by 1 | Viewed by 1393
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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