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Designs
Special Issues
Advancements in Robotic Design, Manufacturing, and the Action-Perception Loop
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Advancements in Robotic Design, Manufacturing, and the Action-Perception Loop

A special issue of Designs (ISSN 2411-9660).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3032

Special Issue Editors

Dr. Roveda Loris

E-Mail Website
Guest Editor
Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI) IDSIA-SUPSI, Manno, Switzerland
Interests: industrial robots; collaborative robots; control theory; wearable robotics; interaction control; human-robot collaboration; AI; ML
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Meattini

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
Interests: robotic manipulation; human-robot interaction; assistive robotics; programming by demonstration; machine learning

Special Issue Information

Dear Colleagues,

Recent advancements in robotics research have underscored the importance of exploiting the link between robot perception and action, which, if considered as interconnected entities of a loop instead of separate processes, could enhance the geometric interpretation of perceptual information, the estimation of object models, the integration of grasp planning with machine learning, and long-horizon manipulation task sequences in industrial settings. Investigating the crucial relations between robot perception and action may contribute to overcoming current limitations and enable a new era of industrial automation to be unlocked.

This Special Issue aims to connect scientists who are actively working at the intersection of robotic manipulation and perception. Contributions to this Special Issue should present recent advancements and perspectives concerning robotic manipulation and perception for a diverse range of topics, including, but not limited to, deformable object manipulation, grasp stability, dexterous manipulation, active and interactive perception, robot learning, computer vision, tactile sensing, and learning from demonstration.

Dr. Roveda Loris
Dr. Roberto Meattini
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. Designs 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 1600 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

  • robotics
  • robotic manipulation
  • robotic perception
  • automation
  • machine learning
  • human–robot collaboration
  • deformable objects

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Published Papers (3 papers)

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Research

17 pages, 4182 KiB  
Article
Comparative Analysis of Topology Optimization Platforms for Additive Manufacturing of Robot Arms
by Petar Curkovic
Designs 2024, 8(5), 98; https://doi.org/10.3390/designs8050098 - 30 Sep 2024
Viewed by 404
Abstract
Recently, CAD environments have integrated topology optimization (TO) tools, enabling rapid development and manufacturing of parts with optimized mechanical properties. However, different CAD platforms incorporate TO differently, making a comparative analysis necessary. This study aims to systematically compare the efficiency, material usage, and [...] Read more.
Recently, CAD environments have integrated topology optimization (TO) tools, enabling rapid development and manufacturing of parts with optimized mechanical properties. However, different CAD platforms incorporate TO differently, making a comparative analysis necessary. This study aims to systematically compare the efficiency, material usage, and design quality of five commonly used CAD/TO platforms when applied to the topology optimization of a six degrees of freedom robotic arm. The objective is to identify the key differences in how these platforms influence the final design and manufacturing outcomes. Practical validation of results is provided by printing and assembling optimized components into a fully functional robotic arm. Our findings indicate differences in optimization efficiency, material usage, and print time between analyzed platforms. Strengths and weaknesses of each platform are indicated and recommendations for optimization parameters are provided. Full article
(This article belongs to the Special Issue Advancements in Robotic Design, Manufacturing, and the Action-Perception Loop)
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31 pages, 12058 KiB  
Article
Design and Operational Assessment of a Railroad Track Robot for Railcar Undercarriage Condition Inspection
by James Kasch and Mehdi Ahmadian
Designs 2024, 8(4), 70; https://doi.org/10.3390/designs8040070 - 10 Jul 2024
Viewed by 809
Abstract
The operational effectiveness of a railroad track robot that is designed for railcar undercarriage inspection is provided. Beyond describing the robot’s design details and onboard imaging system, the paper analyzes the recorded video images and offers design improvements to increase their clarity. The [...] Read more.
The operational effectiveness of a railroad track robot that is designed for railcar undercarriage inspection is provided. Beyond describing the robot’s design details and onboard imaging system, the paper analyzes the recorded video images and offers design improvements to increase their clarity. The robot is designed to be deployed trackside, traverse over the rails, and then maneuver in between the rails beneath a stopped train in a siding or a railyard. The under-carriage conditions are documented by onboard video cameras for automated or manual postprocessing. The intent is to inspect the components that are not visible to the conductor or train inspector during a walk-along inspection of a stationary train. An assessment of the existing design, followed by modification and validation, is presented. The results from a prototype unit developed by the Railway Technologies Laboratory at Virginia Tech (RTL) indicate that with proper positioning of off-the-shelf imaging systems such as cameras manufactured by GoPro® in San Mateo, CA, USA and appropriate lighting, it is possible to capture videos that are sufficiently clear for manual (by a railroad engineer), semi-automated, or fully automated (using Artificial Intelligence or Machine Learning methods) inspections of rolling stock undercarriages. Additionally, improvements to the control, mobility, and reliability of the system are documented, although reliability throughout operation and the ability to consistently climb out of the track bed remain points of future investigation. Full article
(This article belongs to the Special Issue Advancements in Robotic Design, Manufacturing, and the Action-Perception Loop)
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30 pages, 3617 KiB  
Article
Energy Requirement Modeling for Automated Guided Vehicles Considering Material Flow and Layout Data
by Marvin Sperling and Kai Furmans
Designs 2024, 8(3), 48; https://doi.org/10.3390/designs8030048 - 21 May 2024
Viewed by 1348
Abstract
Saving energy and resources has become increasingly important for industrial applications. Foremost, this requires knowledge about the energy requirement. For this purpose, this paper presents a state-based energy requirement model for mobile robots, e.g., automated guided vehicles or autonomous mobile robots, that determines [...] Read more.
Saving energy and resources has become increasingly important for industrial applications. Foremost, this requires knowledge about the energy requirement. For this purpose, this paper presents a state-based energy requirement model for mobile robots, e.g., automated guided vehicles or autonomous mobile robots, that determines the energy requirement by integrating the linearized power requirement parameters within each system state of the vehicle. The model and their respective system states were verified using a qualitative process analysis of 25 mobile robots from different manufacturers and validated by comparing simulated data with experimental data. For this purpose, power consumption measurements over 461 operating hours were performed in experiments with two different industrial mobile robots. System components of a mobile robot, which require energy, were classified and their power consumptions were measured individually. The parameters in the study consist of vehicle speed, load-handling duration, load, utilization, material flow and layout data, and charging infrastructure system frequency, yet these varied throughout the experiments. Validation of the model through real experiments shows that, in a 99% confidence interval, the relative deviation in the modeled power requirement for a small-scale vehicle is [−1.86%,−1.14%], whereas, for a mid-scale vehicle, it is [−0.73%,−0.31%]. This sets a benchmark for modeling the energy requirement of mobile robots with multiple influencing factors, allowing for an accurate estimation of the energy requirement of mobile robots. Full article
(This article belongs to the Special Issue Advancements in Robotic Design, Manufacturing, and the Action-Perception Loop)
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