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Electronics
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Control of Mobile Robots
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Control of Mobile Robots

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 13061

Special Issue Editor

Prof. Dr. Vladan Papic

E-Mail Website
Guest Editor
Department of Electronics and Computer Science, University of Split, 21 000 Split, Croatia
Interests: computer vision; expert systems; robotics; motion analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Controlling robots in order to make their behavior purposeful, safe, and effective is an intensive area of research today. Robots are assigned various tasks and must be adapted to changing environments and conditions. The goal of autonomous and intelligent robot performance based on sensor data and advanced signal processing makes this area interesting for many researchers. Robots are present in the air, on the ground, and under water. They are becoming part of our everyday lives. In addition to artificial intelligence and autonomous driving, which are the most exploited tech news themes, other areas related to robotics and robot control, such as communication, cooperative work, nanorobotics, human–robot interfaces and interaction, and emotion expression and perception, are worthy of our attention.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Robot perception   
  • Mapping
  • Navigation
  • Motion planning
  • Task planning
  • Estimation and learning for robotics systems
  • Human–robot interaction
  • Multi-robot control.

Prof. Dr. Vladan Papic
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. 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

  • Object detection and categorization
  • Motion control
  • Motion and path planning
  • Visual servoing
  • Visual-based navigation
  • Sensor-based control
  • Human–robot interaction
  • Optimization and optimal control
  • Deep learning in robotics.

Published Papers (3 papers)

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Research

20 pages, 15876 KiB  
Article
Estimation of the Energy Consumption of an All-Terrain Mobile Manipulator for Operations in Steep Vineyards
by Ivan Hrabar, Goran Vasiljević and Zdenko Kovačić
Electronics 2022, 11(2), 217; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11020217 - 11 Jan 2022
Cited by 4 | Viewed by 2242
Abstract
A heterogeneous robotic system that can perform various tasks in the steep vineyards of the Mediterranean region was developed and tested as part of the HEKTOR—Heterogeneous Autonomous Robotic System in Viticulture and Mariculture—project. This article describes the design of hardware and an easy-to-use [...] Read more.
A heterogeneous robotic system that can perform various tasks in the steep vineyards of the Mediterranean region was developed and tested as part of the HEKTOR—Heterogeneous Autonomous Robotic System in Viticulture and Mariculture—project. This article describes the design of hardware and an easy-to-use method for evaluating the energy consumption of the system, as well as, indirectly, its deployment readiness level. The heterogeneous robotic system itself consisted of a flying robot—a light autonomous aerial robot (LAAR)—and a ground robot—an all-terrain mobile manipulator (ATMM), composed of an all-terrain mobile robot (ATMR) platform and a seven-degree-of-freedom (DoF) torque-controlled robotic arm. A formal approach to describe the topology and parameters of selected vineyards is presented. It is shown how Google Earth data can be used to make an initial estimation of energy consumption for a selected vineyard. On this basis, estimates of energy consumption were made for the tasks of protective spraying and bud rubbing. The experiments were conducted in two different vineyards, one with a moderate slope and the other with a much steeper slope, to evaluate the proposed estimation method. Full article
(This article belongs to the Special Issue Control of Mobile Robots)
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18 pages, 11115 KiB  
Article
End-Effector Force and Joint Torque Estimation of a 7-DoF Robotic Manipulator Using Deep Learning
by Stanko Kružić, Josip Musić, Roman Kamnik and Vladan Papić
Electronics 2021, 10(23), 2963; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10232963 - 28 Nov 2021
Cited by 7 | Viewed by 4770
Abstract
When a mobile robotic manipulator interacts with other robots, people, or the environment in general, the end-effector forces need to be measured to assess if a task has been completed successfully. Traditionally used force or torque estimation methods are usually based on observers, [...] Read more.
When a mobile robotic manipulator interacts with other robots, people, or the environment in general, the end-effector forces need to be measured to assess if a task has been completed successfully. Traditionally used force or torque estimation methods are usually based on observers, which require knowledge of the robot dynamics. Contrary to this, our approach involves two methods based on deep neural networks: robot end-effector force estimation and joint torque estimation. These methods require no knowledge of robot dynamics and are computationally effective but require a force sensor under the robot base. Several different architectures were considered for the tasks, and the best ones were identified among those tested. First, the data for training the networks were obtained in simulation. The trained networks showed reasonably good performance, especially using the LSTM architecture (with a root mean squared error (RMSE) of 0.1533 N for end-effector force estimation and 0.5115 Nm for joint torque estimation). Afterward, data were collected on a real Franka Emika Panda robot and then used to train the same networks for joint torque estimation. The obtained results are slightly worse than in simulation (0.5115 Nm vs. 0.6189 Nm, according to the RMSE metric) but still reasonably good, showing the validity of the proposed approach. Full article
(This article belongs to the Special Issue Control of Mobile Robots)
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22 pages, 5068 KiB  
Article
An Autonomous Grape-Harvester Robot: Integrated System Architecture
by Eleni Vrochidou, Konstantinos Tziridis, Alexandros Nikolaou, Theofanis Kalampokas, George A. Papakostas, Theodore P. Pachidis, Spyridon Mamalis, Stefanos Koundouras and Vassilis G. Kaburlasos
Electronics 2021, 10(9), 1056; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10091056 - 29 Apr 2021
Cited by 30 | Viewed by 5096
Abstract
This work pursues the potential of extending “Industry 4.0” practices to farming toward achieving “Agriculture 4.0”. Our interest is in fruit harvesting, motivated by the problem of addressing the shortage of seasonal labor. In particular, here we present an integrated system architecture of [...] Read more.
This work pursues the potential of extending “Industry 4.0” practices to farming toward achieving “Agriculture 4.0”. Our interest is in fruit harvesting, motivated by the problem of addressing the shortage of seasonal labor. In particular, here we present an integrated system architecture of an Autonomous Robot for Grape harvesting (ARG). The overall system consists of three interdependent units: (1) an aerial unit, (2) a remote-control unit and (3) the ARG ground unit. Special attention is paid to the ARG; the latter is designed and built to carry out three viticultural operations, namely harvest, green harvest and defoliation. We present an overview of the multi-purpose overall system, the specific design of each unit of the system and the integration of all subsystems. In addition, the fully sensory-based sensing system architecture and the underlying vision system are analyzed. Due to its modular design, the proposed system can be extended to a variety of different crops and/or orchards. Full article
(This article belongs to the Special Issue Control of Mobile Robots)
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