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Intelligent Service Robot Based on Sensors Technology
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Intelligent Service Robot Based on Sensors Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 6626

Special Issue Editors

Dr. TaeWon Seo

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: service robot design and control
Special Issues, Collections and Topics in MDPI journals
Dr. Sung-Hyuk Song

E-Mail Website
Guest Editor
Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Gajeongbuk-ro, 156, Jang-dong, Yuseong-gu, Daejeon, Republic of Korea
Interests: gripper and mobile robot system
Dr. Uikyum Kim

E-Mail Website
Guest Editor
Mechanical Engineering, Ajou University, Woldeukeom-ro, Yeongtong-gu, Suwon, Gyeonggi-do, Republic of Korea
Interests: intelligent and interactive robotic system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Service robots are increasingly being used to replace human workers in dangerous and difficult tasks. To fully automate these tasks, robots must be able to sense their external conditions, ensuring the safe and more precise operation of these technologies. Recently, many kinds of sensors have been developed to measure the physical parameters of robots, such as position and force, using vision or laser sensors. In this Special Issue, we would like to introduce the latest progress in sensor technology, aiming to make service robots more intelligent than ever before. In order to realize sensor technology applications in robotic systems, robot–sensor integration, sensing principles, soft smart materials, and so on need to be considered. We look forward to the participation of researchers who are conducting research in this field. This Special Issue, “Intelligent service robot based on sensors technology”, will highlight state-of-the-art sensors technology through original contributions and reviews.

Topics of interest include but are not limited to service robots, human–robot interaction, smart sensing, sensor integration, intelligent control and soft material sensor.

Dr. TaeWon Seo
Dr. Sung-Hyuk Song
Dr. Uikyum Kim
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. Sensors 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 2600 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.

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Published Papers (3 papers)

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Research

21 pages, 51162 KiB  
Article
Towards Autonomous Retail Stocking and Picking: Methods Enabling Robust Vacuum-Based Robotic Manipulation in Densely Packed Environments
by Peter Kmecl, Marko Munih and Janez Podobnik
Sensors 2024, 24(20), 6687; https://doi.org/10.3390/s24206687 - 17 Oct 2024
Viewed by 227
Abstract
With the advent of robotics and artificial intelligence, the potential for automating tasks within human-centric environments has increased significantly. This is particularly relevant in the retail sector where the demand for efficient operations and the shortage of labor drive the need for rapid [...] Read more.
With the advent of robotics and artificial intelligence, the potential for automating tasks within human-centric environments has increased significantly. This is particularly relevant in the retail sector where the demand for efficient operations and the shortage of labor drive the need for rapid advancements in robot-based technologies. Densely packed retail shelves pose unique challenges for robotic manipulation and detection due to limited space and diverse object shapes. Vacuum-based grasping technologies offer a promising solution but face challenges with object shape adaptability. The study proposes a framework for robotic grasping in retail environments, an adaptive vacuum-based grasping solution, and a new evaluation metric—termed grasp shear force resilience—for measuring the effectiveness and stability of the grasp during manipulation. The metric provides insights into how retail objects behave under different manipulation scenarios, allowing for better assessment and optimization of robotic grasping performance. The study’s findings demonstrate the adaptive suction cups’ ability to successfully handle a wide range of object shapes and sizes, which, in some cases, overcome commercially available solutions, particularly in adaptability. Additionally, the grasp shear force resilience metric highlights the effects of the manipulation process, such as in shear force and shake, on the manipulated object. This offers insights into its interaction with different vacuum cup grasping solutions in retail picking and restocking scenarios. Full article
(This article belongs to the Special Issue Intelligent Service Robot Based on Sensors Technology)
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26 pages, 12589 KiB  
Article
Sensors for Digital Transformation in Smart Forestry
by Florian Ehrlich-Sommer, Ferdinand Hoenigsberger, Christoph Gollob, Arne Nothdurft, Karl Stampfer and Andreas Holzinger
Sensors 2024, 24(3), 798; https://doi.org/10.3390/s24030798 - 25 Jan 2024
Cited by 6 | Viewed by 2875
Abstract
Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition [...] Read more.
Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition in the digital transformation of forestry. However, the complexity and challenging conditions of forest environments often impede data collection efforts. Achieving the full potential of smart forestry necessitates a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data essential for AI applications. This paper highlights the symbiotic relationship between human expertise and the digital transformation in forestry, particularly under challenging conditions. We emphasize the human-in-the-loop approach, which allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. A critical aspect of this integration is the deployment of autonomous robotic systems in forests, functioning both as data collectors and processing hubs. These systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. We present our universal sensor platform, detailing our experiences and the critical importance of the initial phase in digital transformation—the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and our findings underscore its significance in advancing smart forestry. Full article
(This article belongs to the Special Issue Intelligent Service Robot Based on Sensors Technology)
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12 pages, 4722 KiB  
Article
A Multi-Layered 3D NDT Scan-Matching Method for Robust Localization in Logistics Warehouse Environments
by Taeho Kim, Haneul Jeon and Donghun Lee
Sensors 2023, 23(5), 2671; https://doi.org/10.3390/s23052671 - 28 Feb 2023
Cited by 3 | Viewed by 2600
Abstract
This paper proposed a multi-layered 3D NDT (normal distribution transform) scan-matching approach for robust localization even in the highly dynamic environment of warehouse logistics. Our approach partitioned a given 3D point-cloud map and the scan measurements into several layers regarding the degree of [...] Read more.
This paper proposed a multi-layered 3D NDT (normal distribution transform) scan-matching approach for robust localization even in the highly dynamic environment of warehouse logistics. Our approach partitioned a given 3D point-cloud map and the scan measurements into several layers regarding the degree of environmental changes in the height direction and computed the covariance estimates for each layer using 3D NDT scan-matching. Because the covariance determinant is the estimate’s uncertainty, we can determine which layers are better to use in the localization in the warehouse. If the layer gets close to the warehouse’s floor, the degree of environmental changes, such as the cluttered warehouse layout and position of boxes, would be significantly large, while it has many good features for scan-matching. If the observation at a specific layer is not explained well enough, then the layer for localization can be switched to other layers with lower uncertainties. Thus, the main novelty of this approach is that localization robustness can be improved even in very cluttered and dynamic environments. This study also provides the simulation-based validation using Nvidia’s Omniverse Isaac sim and detailed mathematical descriptions for the proposed method. Moreover, the evaluated results of this study can be a good starting point for further mitigating the effects of occlusion in warehouse navigation of mobile robots. Full article
(This article belongs to the Special Issue Intelligent Service Robot Based on Sensors Technology)
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