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Special Issue "Sensing Applications in Robotics"

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

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editors

Prof. Dr. Oscar Reinoso García
Website
Guest Editor
Department of Systems Engineering and Automation, Miguel Hernández University, 03202 Elche, Spain
Interests: computer vision; robotics; cooperative robotics
Special Issues and Collections in MDPI journals
Prof. Dr. Luis Payá
Website
Guest Editor

Special Issue Information

Dear Colleagues,

The presence of robots in a variety of scenarios has increased substantially in recent years, as their ability to solve diverse tasks has improved. Currently, a broad range of research lines continue to be very active in the field of robotics, with the common goal of increasing their safety, autonomy, and adaptability to unexpected circumstances. Additionally, improving collaborative mechanisms between robot and user constitutes an important line in this field. In all cases, sensing technologies play a crucial role in capturing the necessary information from the environment, robot, and/or user.

To address any specific task, the robot has to be equipped with different kinds of sensors to perceive the surroundings, such as touch sensors, laser rangefinders, GPS, visual sensors or combined vision-depth platforms. In some applications, a combination of these is used, and data-fusion algorithms must be implemented. Currently, machine learning and deep learning approaches may play an important role in data analysis, interpretation, and fusion. Additionally, some specific tasks can be performed more efficiently if a team of robots is used, so an optimal combination of the information captured between the different sensors is crucial. In this sense, IoT (Internet of Things) approaches may ease this labor. Finally, in some cases, the robots must operate in social environments, and it is necessary to implement interfaces that permit an easy and intuitive interaction between data captured by the sensors (either pre- or post-processed) and the users.

The aim of this Special Issue is to present current applications of sensing technologies in robotics. In this way, this Special Issue invites contributions to the following topics (but is not limited to them):

  • Sensing technologies in mobile robots;
  • Sensing technologies in industrial robots;
  • Design of new sensors for robots;
  • Processing and interpretation of sensory data;
  • Machine learning and deep learning for data treatment in robotics;
  • Sensing technologies in multirobot systems;
  • Interfaces for robot/user interaction;
  • Sensing technologies in collaborative robot/user applications;
  • Applications of sensing technologies in robotics.

Prof. Dr. Oscar Reinoso García
Prof. Dr. Luis Payá
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 papers will be 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 2000 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

  • Vision sensors
  • Range sensors
  • Touch sensors
  • Sensor networks
  • Intelligent sensors
  • Mobile robots
  • Industrial robots
  • Data interpretation
  • Data fusion
  • Sensor applications

Published Papers (4 papers)

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Research

Open AccessArticle
kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation
Sensors 2020, 20(21), 6002; https://doi.org/10.3390/s20216002 - 22 Oct 2020
Abstract
This paper presents a novel two-stage system which integrates topological localisation candidates from a radar-only place recognition system with precise pose estimation using spectral landmark-based techniques. We prove that the—recently available—seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a [...] Read more.
This paper presents a novel two-stage system which integrates topological localisation candidates from a radar-only place recognition system with precise pose estimation using spectral landmark-based techniques. We prove that the—recently available—seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a style reminiscent of the mapping and localisation systems underpinning visual teach-and-repeat (VTR) systems which have been exhibited robustly in the last decade. Offline experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community with performance comparing favourably with and even rivalling alternative state-of-the-art radar localisation systems. Specifically, we show the long-term durability of the approach and of the sensing technology itself to autonomous navigation. We suggest a range of sensible methods of tuning the system, all of which are suitable for online operation. For both tuning regimes, we achieve, over the course of a month of localisation trials against a single static map, high recalls at high precision, and much reduced variance in erroneous metric pose estimation. As such, this work is a necessary first step towards a radar teach-and-repeat (RTR) system and the enablement of autonomy across extreme changes in appearance or inclement conditions. Full article
(This article belongs to the Special Issue Sensing Applications in Robotics)
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Open AccessArticle
Applying a 6 DoF Robotic Arm and Digital Twin to Automate Fan-Blade Reconditioning for Aerospace Maintenance, Repair, and Overhaul
Sensors 2020, 20(16), 4637; https://doi.org/10.3390/s20164637 - 18 Aug 2020
Abstract
The UK is home to several major air commercial and transport hubs. As a result, there is a high demand for Maintenance, Repair, and Overhaul (MRO) services to ensure that fleets of aircraft are in airworthy conditions. MRO services currently involve heavy manual [...] Read more.
The UK is home to several major air commercial and transport hubs. As a result, there is a high demand for Maintenance, Repair, and Overhaul (MRO) services to ensure that fleets of aircraft are in airworthy conditions. MRO services currently involve heavy manual labor. This creates bottlenecks, low repeatability, and low productivity. Presented in this paper is an investigation to create an automation cell for the fan-blade reconditioning component of MRO. The design and prototype of the automation cell is presented. Furthermore, a digital twin of the grinding process is developed and used as a tool to explore the required grinding force parameters needed to effectively remove surface material. An integration of a 6-DoF industrial robot with an end-effector grinder and a computer vision system was undertaken. The computer vision system was used for the digitization of the fan-blade surface as well as tracking and guidance of material removal. Our findings reveal that our proposed system can perform material removal, track the state of the fan blade during the reconditioning process and do so within a closed-loop automated robotic work cell. Full article
(This article belongs to the Special Issue Sensing Applications in Robotics)
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Open AccessArticle
Optimization Design and Flexible Detection Method of Wall-Climbing Robot System with Multiple Sensors Integration for Magnetic Particle Testing
Sensors 2020, 20(16), 4582; https://doi.org/10.3390/s20164582 - 15 Aug 2020
Abstract
Weld detection is vital to the quality of ship construction and navigation safety, and numerous detection robots have been developed and widely applied. Focusing on the current bottleneck of robot safety, efficiency, and intelligent detection, this paper developed a wall-climbing robot that integrates [...] Read more.
Weld detection is vital to the quality of ship construction and navigation safety, and numerous detection robots have been developed and widely applied. Focusing on the current bottleneck of robot safety, efficiency, and intelligent detection, this paper developed a wall-climbing robot that integrates multiple sensors and uses fluorescent magnetic powder for nondestructive testing. We designed a moving mechanism that can safely move on a curved surface and a serial-parallel hybrid flexible detection mechanism that incorporates a force sensor to solve the robot’s safe adsorption and a flexible detection of the curved surface to complete the flaw detection operation. We optimized the system structure and improved the overall performance of the robot by establishing a unified mechanical model for different operating conditions. Based on the collected sensor information, a multi-degree of freedom component collaborative flexible detection method with a standard detecting process was developed to complete efficient, high-quality detection. Results showed that the developed wall-climbing robot can move safely and steadily on the complex facade and can complete the flaw detection of wall welds. Full article
(This article belongs to the Special Issue Sensing Applications in Robotics)
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Open AccessArticle
Absolute Positioning Accuracy Improvement in an Industrial Robot
Sensors 2020, 20(16), 4354; https://doi.org/10.3390/s20164354 - 05 Aug 2020
Cited by 1
Abstract
The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further [...] Read more.
The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot. Full article
(This article belongs to the Special Issue Sensing Applications in Robotics)
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