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Robotics
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Collection in Honor of Women's Contribution in Robotics
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Collection in Honor of Women's Contribution in Robotics

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 13246

Special Issue Editors

Dr. Naira Hovakimyan

E-Mail Website
Guest Editor
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Room 223, 1206 West Green Street, Urbana, IL 61801, USA
Interests: control and optimization; autonomous systems; machine learning; neural networks, game theory, and their applications in aerospace, robotics, mechanical, agricultural, electrical, petroleum, biomedical engineering, and elderly care
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kerstin Thurow

E-Mail Website
Guest Editor
Center for Life Science Automation, University Rostock, F.-Barnewitz-Str. 8, 18119 Rostock (D), Germany
Interests: life science automation; robotics; smart sensor systems; mobile robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In honor of women's contribution in robotics, this Special Issue will present research work which collaborate with women scholars. With at least one female author, your submission will have the opportunity to be published free of charge in this Special Issue.

We call on the general population to pay attention to the women workers around, and we're expected to be in honor of women's contribution. We hope that this Special Issue will further encourage and promote the progress of scientific contributions provided by women in this field.

An award of the “Best Collaborator with Women in Robotics” will be launched and granted to the best paper published in this Special Issue. Each award nominee will be assessed on their paper’s originality, quality, and contribution to the field by the Evaluation Committee. The winner will receive a certificate, an award of CHF 500, and an opportunity to publish their next submission in Robotics free of charge.

Prof. Dr. Naira Hovakimyan
Prof. Dr. Kerstin Thurow
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. Robotics is an international peer-reviewed open access monthly 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 1800 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.

Published Papers (7 papers)

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Research

16 pages, 15996 KiB  
Article
Comparison of Machine Learning Approaches for Robust and Timely Detection of PPE in Construction Sites
by Roxana Azizi, Maria Koskinopoulou and Yvan Petillot
Robotics 2024, 13(2), 31; https://doi.org/10.3390/robotics13020031 - 16 Feb 2024
Viewed by 1135
Abstract
Globally, workplace safety is a critical concern, and statistics highlight the widespread impact of occupational hazards. According to the International Labour Organization (ILO), an estimated 2.78 million work-related fatalities occur worldwide each year, with an additional 374 million non-fatal workplace injuries and illnesses. [...] Read more.
Globally, workplace safety is a critical concern, and statistics highlight the widespread impact of occupational hazards. According to the International Labour Organization (ILO), an estimated 2.78 million work-related fatalities occur worldwide each year, with an additional 374 million non-fatal workplace injuries and illnesses. These incidents result in significant economic and social costs, emphasizing the urgent need for effective safety measures across industries. The construction sector in particular faces substantial challenges, contributing a notable share to these statistics due to the nature of its operations. As technology, including machine vision algorithms and robotics, continues to advance, there is a growing opportunity to enhance global workplace safety standards and mitigate the human toll of occupational hazards on a broader scale. This paper explores the development and evaluation of two distinct algorithms designed for the accurate detection of safety equipment on construction sites. The first algorithm leverages the Faster R-CNN architecture, employing ResNet-50 as its backbone for robust object detection. Subsequently, the results obtained from Faster R-CNN are compared with those of the second algorithm, Few-Shot Object Detection (FsDet). The selection of FsDet is motivated by its efficiency in addressing the time-intensive process of compiling datasets for network training in object recognition. The research methodology involves training and fine-tuning both algorithms to assess their performance in safety equipment detection. Comparative analysis aims to evaluate the effectiveness of novel training methods employed in the development of these machine vision algorithms. Full article
(This article belongs to the Special Issue Collection in Honor of Women's Contribution in Robotics)
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13 pages, 1781 KiB  
Article
Evaluation of a Voice-Enabled Autonomous Camera Control System for the da Vinci Surgical Robot
by Reenu Arikkat Paul, Luay Jawad, Abhishek Shankar, Maitreyee Majumdar, Troy Herrick-Thomason and Abhilash Pandya
Robotics 2024, 13(1), 10; https://doi.org/10.3390/robotics13010010 - 01 Jan 2024
Viewed by 1855
Abstract
Robotic surgery involves significant task switching between tool control and camera control, which can be a source of distraction and error. This study evaluated the performance of a voice-enabled autonomous camera control system compared to a human-operated camera for the da Vinci surgical [...] Read more.
Robotic surgery involves significant task switching between tool control and camera control, which can be a source of distraction and error. This study evaluated the performance of a voice-enabled autonomous camera control system compared to a human-operated camera for the da Vinci surgical robot. Twenty subjects performed a series of tasks that required them to instruct the camera to move to specific locations to complete the tasks. The subjects performed the tasks (1) using an automated camera system that could be tailored based on keywords; and (2) directing a human camera operator using voice commands. The data were analyzed using task completion measures and the NASA Task Load Index (TLX) human performance metrics. The human-operated camera control method was able to outperform an automated algorithm in terms of task completion (6.96 vs. 7.71 correct insertions; p-value = 0.044). However, subjective feedback suggests that a voice-enabled autonomous camera control system is comparable to a human-operated camera control system. Based on the subjects’ feedback, thirteen out of the twenty subjects preferred the voice-enabled autonomous camera control system including the surgeon. This study is a step towards a more natural language interface for surgical robotics as these systems become better partners during surgery. Full article
(This article belongs to the Special Issue Collection in Honor of Women's Contribution in Robotics)
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23 pages, 8736 KiB  
Article
Emotional Experience in Human–Robot Collaboration: Suitability of Virtual Reality Scenarios to Study Interactions beyond Safety Restrictions
by Franziska Legler, Jonas Trezl, Dorothea Langer, Max Bernhagen, Andre Dettmann and Angelika C. Bullinger
Robotics 2023, 12(6), 168; https://doi.org/10.3390/robotics12060168 - 08 Dec 2023
Viewed by 1811
Abstract
Today’s research on fenceless human–robot collaboration (HRC) is challenged by a relatively slow development of safety features. Simultaneously, design recommendations for HRC are requested by the industry. To simulate HRC scenarios in advance, virtual reality (VR) technology can be utilized and ensure safety. [...] Read more.
Today’s research on fenceless human–robot collaboration (HRC) is challenged by a relatively slow development of safety features. Simultaneously, design recommendations for HRC are requested by the industry. To simulate HRC scenarios in advance, virtual reality (VR) technology can be utilized and ensure safety. VR also allows researchers to study the effects of safety-restricted features like close distance during movements and events of robotic malfunctions. In this paper, we present a VR experiment with 40 participants collaborating with a heavy-load robot and compare the results to a similar real-world experiment to study transferability and validity. The participant’s proximity to the robot, interaction level, and occurring system failures were varied. State anxiety, trust, and intention to use were used as dependent variables, and valence and arousal values were assessed over time. Overall, state anxiety was low and trust and intention to use were high. Only simulated failures significantly increased state anxiety, reduced trust, and resulted in reduced valence and increased arousal. In comparison with the real-world experiment, non-significant differences in all dependent variables and similar progression of valence and arousal were found during scenarios without system failures. Therefore, the suitability of applying VR in HRC research to study safety-restricted features can be supported; however, further research should examine transferability for high-intensity emotional experiences. Full article
(This article belongs to the Special Issue Collection in Honor of Women's Contribution in Robotics)
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20 pages, 22238 KiB  
Article
An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments
by Kosmas Tsiakas, Alexios Papadimitriou, Eleftheria Maria Pechlivani, Dimitrios Giakoumis, Nikolaos Frangakis, Antonios Gasteratos and Dimitrios Tzovaras
Robotics 2023, 12(6), 146; https://doi.org/10.3390/robotics12060146 - 28 Oct 2023
Cited by 3 | Viewed by 1880
Abstract
Due to the accelerated growth of the world’s population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure [...] Read more.
Due to the accelerated growth of the world’s population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties. In this paper, we present a comprehensive autonomous navigation architecture for holonomic mobile robots in greenhouses. Our approach utilizes the heating system rails to navigate through the crop rows using a single stereo camera for perception and a LiDAR sensor for accurate distance measurements. A finite state machine orchestrates the sequence of required actions, enabling fully automated task execution, while semantic segmentation provides essential cognition to the robot. Our approach has been evaluated in a real-world greenhouse using a custom-made robotic platform, showing its overall efficacy for automated inspection tasks in greenhouses. Full article
(This article belongs to the Special Issue Collection in Honor of Women's Contribution in Robotics)
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12 pages, 2854 KiB  
Article
Data-Driven Inverse Kinematics Approximation of a Delta Robot with Stepper Motors
by Anni Zhao, Arash Toudeshki, Reza Ehsani and Jian-Qiao Sun
Robotics 2023, 12(5), 135; https://doi.org/10.3390/robotics12050135 - 30 Sep 2023
Cited by 2 | Viewed by 2229
Abstract
The Delta robot is a parallel robot that is over-actuated and has a highly nonlinear dynamic model, which poses a significant challenge to its control design. The inverse kinematics that maps the motor angles to the position of the end effector is highly [...] Read more.
The Delta robot is a parallel robot that is over-actuated and has a highly nonlinear dynamic model, which poses a significant challenge to its control design. The inverse kinematics that maps the motor angles to the position of the end effector is highly nonlinear and extremely important for the control design of the Delta robot. It has been experimentally shown that geometry-based inverse kinematics is not accurate enough to capture the dynamics of the Delta robot due to manufacturing component errors, measurement errors, joint flexibility, backlash, friction, etc. To address this issue, we propose a neural network model to approximate the inverse kinematics of the Delta robot with stepper motors. The neural network model is trained with randomly sampled experimental data and implemented on the hardware in an open-loop control for trajectory tracking. Extensive experimental results show that the neural network model achieves excellent performance in terms of the trajectory tracking of the Delta robot under different operation conditions, and outperforms the geometry-based inverse kinematics model. A critical numerical observation indicates that neural networks trained with the specific trajectory data fall short of anticipated performance due to a lack of data. Conversely, neural networks trained on random experimental data capture the rich dynamics of the Delta robot and are quite robust to model uncertainties compared to geometry-based inverse kinematics. Full article
(This article belongs to the Special Issue Collection in Honor of Women's Contribution in Robotics)
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13 pages, 4008 KiB  
Article
Coordinating Tethered Autonomous Underwater Vehicles towards Entanglement-Free Navigation
by Abhishek Patil, Myoungkuk Park and Jungyun Bae
Robotics 2023, 12(3), 85; https://doi.org/10.3390/robotics12030085 - 13 Jun 2023
Viewed by 1513
Abstract
This paper proposes an algorithm that provides operational strategies for multiple tethered autonomous underwater vehicle (T-AUV) systems for entanglement-free navigation. T-AUVs can perform underwater tasks under reliable communication and power supply, which is the most substantial benefit of their operation. Thus, if one [...] Read more.
This paper proposes an algorithm that provides operational strategies for multiple tethered autonomous underwater vehicle (T-AUV) systems for entanglement-free navigation. T-AUVs can perform underwater tasks under reliable communication and power supply, which is the most substantial benefit of their operation. Thus, if one can overcome the entanglement issues while utilizing multiple tethered vehicles, the potential applications of the system increase including ecosystem exploration, infrastructure inspection, maintenance, search and rescue, underwater construction, and surveillance. In this study, we focus on developing strategies for task allocation, path planning, and scheduling that ensure entanglement-free operations while considering workload balancing among the vehicles. We do not impose restrictions on the size or shape of the vehicles at this stage; our primary focus is on efficient tether management as an initial work on the topic. To achieve entanglement-free navigation, we propose a heuristic based on the primal-dual technique, which enables initial task allocation and path planning while minimizing the maximum travel cost of the vehicles. Although this heuristic often generates sectioned paths due to its workload-balancing nature, we also propose a mixed approach to provide feasible solutions for non-sectioned initial paths. This approach combines entanglement avoidance techniques with time scheduling and sectionalization methods. To evaluate the effectiveness of our algorithm, extensive simulations were conducted with varying problem sizes. The computational results demonstrate the potential of our algorithm to be applied in real-time operations, as it consistently generates reliable solutions within a reasonable time frame. Full article
(This article belongs to the Special Issue Collection in Honor of Women's Contribution in Robotics)
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15 pages, 5918 KiB  
Article
Experiments on the Artificial Potential Field with Local Attractors for Mobile Robot Navigation
by Matteo Melchiorre, Laura Salamina, Leonardo Sabatino Scimmi, Stefano Mauro and Stefano Pastorelli
Robotics 2023, 12(3), 81; https://doi.org/10.3390/robotics12030081 - 07 Jun 2023
Cited by 4 | Viewed by 1584
Abstract
Obstacle avoidance is a challenging task in robot navigation, as it requires efficient and reliable methods to avoid collision and reach the desired goal. Artificial potential field methods are widely used for this purpose, as they are efficient, effective, and easy to implement. [...] Read more.
Obstacle avoidance is a challenging task in robot navigation, as it requires efficient and reliable methods to avoid collision and reach the desired goal. Artificial potential field methods are widely used for this purpose, as they are efficient, effective, and easy to implement. However, they are limited by the use of only one global attractor at the goal. This paper introduces and evaluates experimentally a novel technique that enhances the artificial potential field method with local attractors. Local attractors can be positioned around the obstacle so as to guide the robot detouring through preferred regions. Thus, the side the robot will pass by can be determined in advance, making the collision-free path predictable. The technique is formulated by modelling local attractors as optimal inflections, i.e., regions that do not show local minima, which coexist with the potential field generated by the obstacle and the global attractor. The method is validated using a laboratory setup that employs a camera and markers to track the poses of the robot, the obstacle, and the target. A series of experiments are conducted to examine the effect of the local attractor under different test conditions, obtained by varying the obstacle pose, the attraction intensity, and the robot velocity. The experimental results demonstrate the effectiveness of the proposed technique and highlight the aspects that require further investigation for its improvement and application. Full article
(This article belongs to the Special Issue Collection in Honor of Women's Contribution in Robotics)
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