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Electronics
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Unmanned Vehicles and Intelligent Robotic Alike Systems
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Unmanned Vehicles and Intelligent Robotic Alike Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 12767

Special Issue Editors

Prof. Dr. Bhaskar Krishnamachari

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Guest Editor
Department of Electrical Engineering, USC Viterbi School of Engineering, Los Angeles, CA 90089, USA
Interests: design and analysis of algorithms, protocols, and applications for next-generation wireless and mobile networks, including low-power wireless networks (wireless sensor networks), vehicular and robotic networks, cognitive-radio networks, underwater networks, and mobile sensing
Prof. Dr. Chung-Hsien Kuo

E-Mail Website
Guest Editor
Department of Mechanical Engineering, National Taiwan University, Taipei 106319, Taiwan
Interests: intelligent robots; intelligent automation; intelligent welfare technologies; autonomous driving
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Birgit Vogel-Heuser

E-Mail Website1 Website2
Guest Editor
Department of Mechanical Engineering, Technical University of Munich, 80333 München, Germany
Interests: modeling of distributed embedded systems in automation and control regarding dependability and usability; human–machine interactions in process engineering of complex machines and plants
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jen-Yuan (James) Chang

E-Mail Website
Guest Editor
Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
Interests: dynamic systems and control; robotics (AI, soft, bionic, medical, collaborative, and assistive); smart machinery and manufacturing; mechatronics; magnetic recording; data storage systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned vehicles, intelligent robotics, and robot alike systems are among the most recognized critical industries in the world. The development of unmanned vehicles and intelligent robot alike systems is a highly integrated mechatronic process combined with modern control, sensor fusion, computer vision, and artificial intelligence to achieve high performance and robustness.

This Special Issue focuses on theoretical and practical studies dealing with the development of unmanned vehicles and intelligent robot-like systems. We invite high-quality contributions from interested communities in relation to unmanned vehicles and intelligent robot-like systems.

Potential topics of interest include, but are not limited to, the following:

  • Autonomous mobile robots;
  • Unmanned aerial vehicles;
  • Autonomous mobile manipulators;
  • Autonomous underwater vehicles;
  • Autonomous humanoid robot;
  • Robust control of robot-like mobile machines (construction, etc.);
  • Robust control of complex robot-like production machines and plants;
  • Autonomous navigation and obstacle avoidance;
  • Path planning and trajectory tracking;
  • Robotic manipulators;
  • Decentralized robotic and robot-like control systems;
  • Swarm robotic systems;
  • Heterogeneous robotic systems;
  • Human–robot cooperation;
  • Human–robot interaction;
  • Computer vision systems in assisting industrial robotics systems;
  • Sensor fusion technologies for unmanned vehicles and intelligent robotic systems;
  • Practical applications of unmanned vehicles and intelligent robotic systems.

Prof. Dr. Bhaskar Krishnamachari
Prof. Dr. Chung-Hsien Kuo
Prof. Dr. Birgit Vogel-Heuser
Prof. Dr. Jen-Yuan (James) Chang
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. Electronics is an international peer-reviewed open access semimonthly 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

  • autonomous mobile robots
  • unmanned aerial vehicles
  • autonomous mobile manipulators and machines
  • decentralized robot-like control systems
  • swarm robotic systems
  • heterogeneous robotic systems
  • human–robot systems

Published Papers (5 papers)

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Research

12 pages, 3995 KiB  
Article
On the Construction of an Edge-Based Remote Sensing Framework: The Applications on Automated Guided Vehicles and Drones
by Chen-Kun Tsung, Fa-Shian Chang and Xiu-Yu Liu
Electronics 2022, 11(7), 1034; https://doi.org/10.3390/electronics11071034 - 25 Mar 2022
Cited by 3 | Viewed by 2203
Abstract
To monitor the status and mission progress of automated guided vehicles (AGVs), most platforms typically obtained real-time data through a data acquisition system that is deployed on the end vehicles. The data acquired from an end vehicle are generally sparse but are required [...] Read more.
To monitor the status and mission progress of automated guided vehicles (AGVs), most platforms typically obtained real-time data through a data acquisition system that is deployed on the end vehicles. The data acquired from an end vehicle are generally sparse but are required frequently, and an examination process using cloud storage cannot commence until the device’s raw data are received. To reduce communication costs, the proposed edge-based monitoring system (EMS) applies edge computation to move the data examination from the cloud to an end site. The data buffered in the end device could be pre-processed by some detectors. For example, checking the energy is adequate for returning to the base. Thus, buffering data on the end device helps to minimize the time required by the decision maker for abnormal events, e.g., shutdowns caused by exhausted energy. In addition to adopting the common methods of storing, processing, and analyzing data at the data center, the EMS moves some time-sensitive services to the end vehicle. Moreover, after obtaining real-time motion data, the edge computing architecture immediately targets abnormal actions and sends reaction commands to shorten the decision making delay caused by the communication cost between the end vehicles and cloud storage sites, thereby avoiding collisions or accidents. The EMS has been implemented to monitor AGV and unmanned aerial vehicles. The EMS primarily monitored the power and motion of the vehicles. It also combined task-oriented motion commands for monitoring unexpected vehicle motions during tasks. If an abnormal event occurred, immediate warnings were provided through a notification interface and were immediately processed by the EMS to ensure safety during task execution. After checking data consistency between the EMS and the real device, the EMS reveals the corrected status of the device with very little delay. Therefore, the EMS could help with minimizing the time taken to make decisions. Moreover, the EMS has been modified to be deployed on drones to confirm its cross-platform applicability. In the simulations of drones, the EMS also got similar results to the simulations of AGVs. Therefore, the EMS could reduce the time in examining abnormal events and has cross-platform functionality. Full article
(This article belongs to the Special Issue Unmanned Vehicles and Intelligent Robotic Alike Systems)
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15 pages, 8295 KiB  
Article
Output-Based Tracking Control for a Class of Car-Like Mobile Robot Subject to Slipping and Skidding Using Event-Triggered Mechanism
by Changshun Wang, Dan Wang, Weigang Pan and Huang Zhang
Electronics 2021, 10(23), 2886; https://doi.org/10.3390/electronics10232886 - 23 Nov 2021
Cited by 1 | Viewed by 1344
Abstract
This paper presents an output-based tracking controller for a class of car-like mobile robot (CLMR) subject to slipping and skidding. The slipping and skidding are regarded as external disturbances, and an event-triggered extended state observer (ET-ESO) is utilized to recover the velocities as [...] Read more.
This paper presents an output-based tracking controller for a class of car-like mobile robot (CLMR) subject to slipping and skidding. The slipping and skidding are regarded as external disturbances, and an event-triggered extended state observer (ET-ESO) is utilized to recover the velocities as well as to estimate the uncertainties and disturbances. The constrained longitudinal velocity is established, conforming to the traffic flow theory on the kinematic level. The velocity control law and heading angle control law are developed on the dynamic level, respectively. The input to state stability (ISS) of the closed-loop system is analyzed via cascade theory. Simulation results are given to demonstrate the effectiveness of the proposed tracking controller for CLMR subject to slipping and skidding. Full article
(This article belongs to the Special Issue Unmanned Vehicles and Intelligent Robotic Alike Systems)
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21 pages, 4470 KiB  
Article
Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment
by Jui-An Yang and Chung-Hsien Kuo
Electronics 2021, 10(21), 2703; https://doi.org/10.3390/electronics10212703 - 05 Nov 2021
Cited by 7 | Viewed by 2268
Abstract
This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a [...] Read more.
This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters. Full article
(This article belongs to the Special Issue Unmanned Vehicles and Intelligent Robotic Alike Systems)
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18 pages, 496 KiB  
Article
On Optimizing a Multi-Mode Last-Mile Parcel Delivery System with Vans, Truck and Drone
by Chuan Wang, Hongjie Lan, Francisco Saldanha-da-Gama and Youhua Chen
Electronics 2021, 10(20), 2510; https://doi.org/10.3390/electronics10202510 - 15 Oct 2021
Cited by 10 | Viewed by 2882
Abstract
This work focuses on the optimization of a last-mile delivery system with multiple transportation modes. In this scenario, parcels need to be delivered to each customer point. The major feature of the problem is the combination of a fleet of road vehicles (vans) [...] Read more.
This work focuses on the optimization of a last-mile delivery system with multiple transportation modes. In this scenario, parcels need to be delivered to each customer point. The major feature of the problem is the combination of a fleet of road vehicles (vans) with a drone. Each van visits a subset of demand nodes to be determined according to the route of the van. The drone serves the customers not served by vans. At the same time, considering the safety, policy and terrain as well as the need to replace the battery, the drone needs to be transported by truck to the identified station along with the parcel. From each such station, the drone serves a subset of customers according to a direct assignment pattern, i.e., every time the drone is launched, it serves one demand node and returns to the station to collect another parcel. Similarly, the truck is used to transport the drone and cargo between stations. This is somewhat different from the research of other scholars. In terms of the joint distribution of the drone and road vehicle, most scholars will choose the combination of two transportation tools, while we use three. The drone and vans are responsible for distribution services, and the trucks are responsible for transporting the goods and drone to the station. The goal is to optimize the total delivery cost which includes the transportation costs for the vans and the delivery cost for the drone. A fixed cost is also considered for each drone parking site corresponding to the cost of positioning the drone and using the drone station. A discrete optimization model is presented for the problem in addition to a two-phase heuristic algorithm. The results of a series of computational tests performed to assess the applicability of the model and the efficiency of the heuristic are reported. The results obtained show that nearly 10% of the cost can be saved by combining the traditional delivery mode with the use of a drone and drone stations. Full article
(This article belongs to the Special Issue Unmanned Vehicles and Intelligent Robotic Alike Systems)
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18 pages, 8582 KiB  
Article
Hardware-in-the-Loop Simulation of Self-Driving Electric Vehicles by Dynamic Path Planning and Model Predictive Control
by Yi Chung and Yee-Pien Yang
Electronics 2021, 10(19), 2447; https://doi.org/10.3390/electronics10192447 - 08 Oct 2021
Cited by 3 | Viewed by 2835
Abstract
This paper applies a dynamic path planning and model predictive control (MPC) to simulate self-driving and parking for an electric van on a hardware-in-the-loop (HiL) platform. The hardware platform is a simulator which consists of an electric power steering system, accelerator and brake [...] Read more.
This paper applies a dynamic path planning and model predictive control (MPC) to simulate self-driving and parking for an electric van on a hardware-in-the-loop (HiL) platform. The hardware platform is a simulator which consists of an electric power steering system, accelerator and brake pedals, and an Nvidia drive PX2 with a robot operating system (ROS). The vehicle dynamics model, sensors, controller, and test field map are virtually built with the PreScan simulation platform. Both manual and autonomous driving modes can be simulated, and a graphic user interface allows a test driver to select a target parking space on a display screen. Three scenarios are demonstrated: forward parking, reverse parking, and obstacle avoidance. When the vehicle perceives an obstacle, the map is updated and the route is adaptively planned. The effectiveness of the proposed MPC is verified in experiments and proved to be superior to a traditional proportional–integral–derivative controller with regards to safety, energy-saving, comfort, and agility. Full article
(This article belongs to the Special Issue Unmanned Vehicles and Intelligent Robotic Alike Systems)
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