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Applied Sciences
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AI Technologies for Collaborative and Service Robots
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AI Technologies for Collaborative and Service Robots

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 2122

Special Issue Editors

Prof. Dr. Giovanni Boschetti

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
Interests: industrial and collaborative robotics; robot and mechanism design; performance evaluation; cable-driven robots
Special Issues, Collections and Topics in MDPI journals
Dr. Matteo Bottin

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
Interests: robotics; redundant robots; trajectory optimization; collaborative robots; rehabilitative robotics; dynamic models
Special Issues, Collections and Topics in MDPI journals
Dr. Riccardo Minto

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
Interests: collaborative robots; cable-driven robots; rehabilitative robotics; teleoperation; haptics; assembly systems

Special Issue Information

Dear Colleagues,

Nowadays, new technologies are improving safe human-robot interactions, and robots are becoming increasingly common both inside and outside of industrial scenarios. Not only are collaborative robots improving production plants by shifting the paradigm of industrial robotics from servant devices to intelligent coworkers, but also service, surgical, and rehabilitation robotics are becoming more and more significant, both from a technological and economic point of view, but also for their social implications. In this context, new intelligent technologies are required for controlling robots in unpredictable and cluttered environments. Moreover, for collaborative and service robots to be effective, they need to become more intelligent, adaptable, and capable of predicting human behaviors in human-centered environments.

For these reasons, we are pleased to invite you to submit a paper for a Special Issue titled “AI Technologies for Collaborative and Service Robots”. This Special Issue aims to provide an opportunity for researchers to publish technological studies and advancements addressing the design and development of applications for collaborative and service robots.

This Special Issue will publish high-quality, original research papers that are related to theory, design, practice, and applications of collaborative and service robots, including, but not limited to, fields of: 

  • Indoor localization and navigation;
  • Control of robotic systems;
  • Innovative robots and applications;
  • Service, medical, and assistive robotics;
  • Haptics;
  • Artificial Intelligence;
  • Machine learning approaches;
  • Dynamics of robots and multi-body systems;
  • Human-robot communication;
  • Autonomous mobile robotics.

Prof. Dr. Giovanni Boschetti
Dr. Matteo Bottin
Dr. Riccardo Minto
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. Applied Sciences 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

  • collaborative robots
  • human-robot interaction
  • autonomous service robots
  • virtual assistants
  • artificial intelligence, machine learning and deep learning
  • prediction models
  • augmented and virtual reality

Published Papers (4 papers)

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Research

23 pages, 12065 KiB  
Article
Trajectory Planning and Singularity Avoidance Algorithm for Robotic Arm Obstacle Avoidance Based on an Improved Fast Marching Tree
by Baoju Wu, Xiaohui Wu, Nanmu Hui and Xiaowei Han
Appl. Sci. 2024, 14(8), 3241; https://doi.org/10.3390/app14083241 - 11 Apr 2024
Viewed by 415
Abstract
The quest for efficient and safe trajectory planning in robotic manipulation poses significant challenges, particularly in complex obstacle environments where the risk of encountering singularities and obstacles is high. Addressing this critical issue, our study presents a novel enhancement of the Fast Marching [...] Read more.
The quest for efficient and safe trajectory planning in robotic manipulation poses significant challenges, particularly in complex obstacle environments where the risk of encountering singularities and obstacles is high. Addressing this critical issue, our study presents a novel enhancement of the Fast Marching Tree (FMT) algorithm, ingeniously designed to navigate the complex terrain of Cartesian space with an unprecedented level of finesse. At the heart of our approach lies a sophisticated two-stage path point sampling strategy, ingeniously coupled with a singularity avoidance mechanism that leverages geometric perception to assess and mitigate the risk of encountering problematic configurations. This innovative method not only facilitates seamless obstacle navigation but also adeptly circumvents the perilous zones of singularity, ensuring a smooth and uninterrupted path for the robotic arm. To further refine the trajectory, we incorporate a quasi-uniform cubic B-spline curve, optimizing the path for both efficiency and smoothness. Our comprehensive simulation experiments underscore the superiority of our algorithm, showcasing its ability to consistently achieve shorter, more efficient paths while steadfastly avoiding obstacles and singularities. The practical applicability of our method is further corroborated through successful implementation in real-world robotic arm trajectory planning scenarios, highlighting its potential to revolutionize the field with its robustness and adaptability. Full article
(This article belongs to the Special Issue AI Technologies for Collaborative and Service Robots)
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15 pages, 4518 KiB  
Article
Robust Learning from Demonstration Based on GANs and Affine Transformation
by Kang An, Zhiyang Wu, Qianqian Shangguan, Yaqing Song and Xiaonong Xu
Appl. Sci. 2024, 14(7), 2902; https://doi.org/10.3390/app14072902 - 29 Mar 2024
Viewed by 340
Abstract
Collaborative robots face barriers to widespread adoption due to the complexity of programming them to achieve human-like movement. Learning from demonstration (LfD) has emerged as a crucial solution, allowing robots to learn tasks directly from expert demonstrations, offering versatility and an intuitive programming [...] Read more.
Collaborative robots face barriers to widespread adoption due to the complexity of programming them to achieve human-like movement. Learning from demonstration (LfD) has emerged as a crucial solution, allowing robots to learn tasks directly from expert demonstrations, offering versatility and an intuitive programming approach. However, many existing LfD methods encounter issues such as convergence failure and lack of generalization ability. In this paper, we propose: (1) a generative adversarial network (GAN)-based model with multilayer perceptron (MLP) architecture, coupled with a novel loss function designed to mitigate convergence issues; (2) an affine transformation-based generalization method aimed at enhancing LfD tasks by improving their generalization performance; (3) a data preprocessing method tailored to facilitate deployment on robotics platforms. We conduct experiments on a UR5 robotic platform tasked with handwritten digit recognition. Our results demonstrate that our proposed method significantly accelerates generation speed, achieving a remarkable processing time of 23 ms, which is five times faster than movement primitives (MPs), while preserving key features from demonstrations. This leads to outstanding convergence and generalization performance. Full article
(This article belongs to the Special Issue AI Technologies for Collaborative and Service Robots)
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20 pages, 5794 KiB  
Article
Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
by Haoyu Lin, Pengkun Quan, Zhuo Liang, Dongbo Wei and Shichun Di
Appl. Sci. 2024, 14(4), 1605; https://doi.org/10.3390/app14041605 - 17 Feb 2024
Viewed by 397
Abstract
With the rise of electric vehicles, autonomous driving, and valet parking technologies, considerable research has been dedicated to automatic charging solutions. While the current focus lies on charging robot design and the visual positioning of charging ports, a notable gap exists in addressing [...] Read more.
With the rise of electric vehicles, autonomous driving, and valet parking technologies, considerable research has been dedicated to automatic charging solutions. While the current focus lies on charging robot design and the visual positioning of charging ports, a notable gap exists in addressing safety aspects during the charging plug-in process. This study aims to bridge this gap by proposing a collision classification scheme for robot manipulators in automatic electric vehicle charging scenarios. In situations with minimal visual positioning deviation, robots employ impedance control for effective insertion. Significant deviations may lead to potential collisions with other vehicle parts, demanding discrimination through a global visual system. For moderate deviations, where a robot’s end-effector encounters difficulty in insertion, existing methods prove inadequate. To address this, we propose a novel data-driven collision classification method, utilizing vibration signals generated during collisions, integrating the robust light gradient boosting machine (LightGBM) algorithm. This approach effectively discerns the acceptability of collision contacts in scenarios involving moderate deviations. Considering the impact of passing vehicles introducing environmental noise, a noise suppression module is introduced into the proposed collision classification method, leveraging empirical mode decomposition (EMD) to enhance its robustness in noisy charging scenarios. This study significantly contributes to the safety of automatic charging processes, offering a practical and applicable collision classification solution tailored to diverse noisy scenarios and potential contact forms encountered by charging robots. The experimental results affirm the effectiveness of the collision classification method, integrating LightGBM and EMD, and highlight its promising prediction accuracy. These findings offer valuable perspectives to steer future research endeavors in the domain of autonomous charging systems. Full article
(This article belongs to the Special Issue AI Technologies for Collaborative and Service Robots)
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31 pages, 8838 KiB  
Article
RTMN 2.0—An Extension of Robot Task Modeling and Notation (RTMN) Focused on Human–Robot Collaboration
by Congyu Zhang Sprenger, Juan Antonio Corrales Ramón and Norman Urs Baier
Appl. Sci. 2024, 14(1), 283; https://doi.org/10.3390/app14010283 - 28 Dec 2023
Viewed by 633
Abstract
This paper describes RTMN 2.0, an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. Intuitive robot programming allows those without programming expertise to plan and control robots through easily understandable predefined modeling notations. These notations achieve no-code programming [...] Read more.
This paper describes RTMN 2.0, an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. Intuitive robot programming allows those without programming expertise to plan and control robots through easily understandable predefined modeling notations. These notations achieve no-code programming and serve as templates for users to create their processes via drag-and-drop functions with graphical representations. The design of the graphical user interface is based on a user survey and gaps identified in the literature We validate our survey through the most influential technology acceptance models, with two major factors: the perceived ease of use and perceived usefulness. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modeling language, RTMN 2.0 concentrates on human–robot collaboration (HRC), which represents the current trend of the industry shift from “mass-production” to “mass-customization”. The biggest contribution that RTMN 2.0 makes is the creation of synergy between HRC modes (based on ISO standards) and HRC task types in the literature. They are modeled as five different HRC task notations: Coexistence Fence, Sequential Cooperation SMS, Teaching HG, Parallel Cooperation SSM, and Collaboration PFL. Both collaboration and safety criteria are defined for each notation. While traditional isolated robot systems in “mass-production” environments provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity in order to be adapted to the variability of customized products. Therefore, human–robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in the new “mass-customization” industrial environments. HRC has made a great impact on the robotic industry: it leads to increased efficiency, reduced costs, and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension of RTMN 2.0 includes the following notations: HRC tasks, requirements, Key Performance Indicators (KPIs), condition checks and decision making, join/split, and data association. With these additional elements, RTMN 2.0 meets the full range of criteria for agile manufacturing—light-out manufacturing is a manufacturing philosophy that does not rely on human labor. Full article
(This article belongs to the Special Issue AI Technologies for Collaborative and Service Robots)
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