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Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition
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Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 9714

Special Issue Editors

Dr. Gregor Klančar

E-Mail Website
Guest Editor
Laboratory of Control Systems and Cybernetics, Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: autonomous mobile robots; motion control; trajectory tracking; path planning; localization; multiagent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Marija Seder

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
Interests: autonomous mobile robots; motion planning; motion control; path planning; coverage planning; environment exploration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sašo Blažič

E-Mail Website
Guest Editor
Laboratory of Control Systems and Cybernetics, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia
Interests: control of nonlinear systems; modeling of nonlinear systems; autonomous mobile systems; mobile robotics; motion control; trajectory tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on mobile robotics systems, where we are witnessing a widespread surge in current applications as well as promising future applications that are made possible due to recent technologies in sensors development. Mobile robots are already started to penetrate our homes, modern manufacturing and warehouse systems are hard to imagine without automated guided vehicles, self-driving cars already drive in normal traffic, flying taxies are about to take off and offer new travel experience, drones already have applications in delivery and remote sensing, not to mention applications in agriculture, construction, medical care, surveillance, entertainment, and others where some will also appear in unforeseen ways, all of them offering an emerging market with great potential. Advanced sensor technologies are of crucial importance in mobile robotics—a multidisciplinary research field—for obtaining automated or autonomous operation of mobile robots in these applications. They have a part in every navigation, motion control, action planning, decision making, environment sensing, localization, awareness, object detection, target tracking, or object manipulation.

This Special Issue on advanced sensor technologies welcomes contributions on recent developments in mobile robotics systems and associated research from a theoretic and application perspective. Topics related to mobile robotics include but are not limited to new sensor developments, innovations in theory, algorithms, reviews, sensor applications, sensor processing, sensor fusion, sensor calibration, object tracking, localization, scene recognition, SLAM, control algorithms, navigation, motion control, mobile robotics, and autonomous system design.

Dr. Gregor Klančar
Dr. Marija Seder
Prof. Dr. Sašo Blažič
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.

Keywords

  • mobile robots localization
  • SLAM mapping and navigation sensor-based planning motion control sensor
  • fusion learning and evolving algorithms in robots collaborative robots multi-robot systems

Published Papers (8 papers)

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Research

Jump to: Review

28 pages, 12660 KiB  
Article
Dynamic Validation of Calibration Accuracy and Structural Robustness of a Multi-Sensor Mobile Robot
by Yang Liu, Ximin Cui, Shenghong Fan, Qiang Wang, Yuhan Liu, Yanbiao Sun and Guo Wang
Sensors 2024, 24(12), 3896; https://doi.org/10.3390/s24123896 - 16 Jun 2024
Viewed by 327
Abstract
For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for [...] Read more.
For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for dynamic traveling situations. This paper presents a novel validation method for the calibration accuracy and structural robustness of a multi-sensor mobile robot. The method employs a ground–object–air cooperation mechanism, termed the “ground surface simulation field (GSSF)—mobile robot -photoelectric transmitter station (PTS)”. Firstly, a static high-precision GSSF is established with the true north datum as a unified reference. Secondly, a rotatable synchronous tracking system (PTS) is assembled to conduct real-time pose measurements for a mobile vehicle. The relationship between each sensor and the vehicle body is utilized to measure the dynamic pose of each sensor. Finally, the calibration accuracy and structural robustness of the sensors are dynamically evaluated. In this context, epipolar line alignment is employed to assess the accuracy of the evaluation of relative orientation calibration of binocular cameras. Point cloud projection and superposition are utilized to realize the evaluation of absolute calibration accuracy and structural robustness of individual sensors, including the navigation camera (Navcam), hazard avoidance camera (Hazcam), multispectral camera, time-of-flight depth camera (TOF), and light detection and ranging (LiDAR), with respect to the vehicle body. The experimental results demonstrate that the proposed method offers a reliable means of dynamic validation for the testing phase of a mobile robot. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition)
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31 pages, 13428 KiB  
Article
SEG-SLAM: Dynamic Indoor RGB-D Visual SLAM Integrating Geometric and YOLOv5-Based Semantic Information
by Peichao Cong, Jiaxing Li, Junjie Liu, Yixuan Xiao and Xin Zhang
Sensors 2024, 24(7), 2102; https://doi.org/10.3390/s24072102 - 25 Mar 2024
Cited by 1 | Viewed by 1082
Abstract
Simultaneous localisation and mapping (SLAM) is crucial in mobile robotics. Most visual SLAM systems assume that the environment is static. However, in real life, there are many dynamic objects, which affect the accuracy and robustness of these systems. To improve the performance of [...] Read more.
Simultaneous localisation and mapping (SLAM) is crucial in mobile robotics. Most visual SLAM systems assume that the environment is static. However, in real life, there are many dynamic objects, which affect the accuracy and robustness of these systems. To improve the performance of visual SLAM systems, this study proposes a dynamic visual SLAM (SEG-SLAM) system based on the orientated FAST and rotated BRIEF (ORB)-SLAM3 framework and you only look once (YOLO)v5 deep-learning method. First, based on the ORB-SLAM3 framework, the YOLOv5 deep-learning method is used to construct a fusion module for target detection and semantic segmentation. This module can effectively identify and extract prior information for obviously and potentially dynamic objects. Second, differentiated dynamic feature point rejection strategies are developed for different dynamic objects using the prior information, depth information, and epipolar geometry method. Thus, the localisation and mapping accuracy of the SEG-SLAM system is improved. Finally, the rejection results are fused with the depth information, and a static dense 3D mapping without dynamic objects is constructed using the Point Cloud Library. The SEG-SLAM system is evaluated using public TUM datasets and real-world scenarios. The proposed method is more accurate and robust than current dynamic visual SLAM algorithms. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition)
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26 pages, 9725 KiB  
Article
Phased Array Ultrasonic Method for Robotic Preload Measurement in Offshore Wind Turbine Bolted Connections
by Yashar Javadi, Brandon Mills, Charles MacLeod, David Lines, Farhad Abad, Saeid Lotfian, Ali Mehmanparast, Gareth Pierce, Feargal Brennan, Anthony Gachagan and Carmelo Mineo
Sensors 2024, 24(5), 1421; https://doi.org/10.3390/s24051421 - 22 Feb 2024
Viewed by 1337
Abstract
This paper presents a novel approach for preload measurement of bolted connections, specifically tailored for offshore wind applications. The proposed method combines robotics, Phased Array Ultrasonic Testing (PAUT), nonlinear acoustoelasticity, and Finite Element Analysis (FEA). Acceptable defects, below a pre-defined size, are shown [...] Read more.
This paper presents a novel approach for preload measurement of bolted connections, specifically tailored for offshore wind applications. The proposed method combines robotics, Phased Array Ultrasonic Testing (PAUT), nonlinear acoustoelasticity, and Finite Element Analysis (FEA). Acceptable defects, below a pre-defined size, are shown to have an impact on preload measurement, and therefore conducting simultaneous defect detection and preload measurement is discussed in this paper. The study demonstrates that even slight changes in the orientation of the ultrasonic transducer, the non-automated approach, can introduce a significant error of up to 140 MPa in bolt stress measurement and therefore a robotic approach is employed to achieve consistent and accurate measurements. Additionally, the study emphasises the significance of considering average preload for comparison with ultrasonic data, which is achieved through FEA simulations. The advantages of the proposed robotic PAUT method over single-element approaches are discussed, including the incorporation of nonlinearity, simultaneous defect detection and stress measurement, hardware and software adaptability, and notably, a substantial improvement in measurement accuracy. Based on the findings, the paper strongly recommends the adoption of the robotic PAUT approach for preload measurement, whilst acknowledging the required investment in hardware, software, and skilled personnel. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition)
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11 pages, 1794 KiB  
Article
Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures
by Youngho Kim, Incheol Han, Jeyong Jung, Sumin Yang, Seunghee Lee, Bummo Koo, Soonjae Ahn, Yejin Nam and Sung-Hyuk Song
Sensors 2024, 24(3), 917; https://doi.org/10.3390/s24030917 - 31 Jan 2024
Cited by 1 | Viewed by 892
Abstract
The quantification of comfort in binding parts, essential human–machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of [...] Read more.
The quantification of comfort in binding parts, essential human–machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition)
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14 pages, 2668 KiB  
Article
Space Robot Sensor Noise Amelioration Using Trajectory Shaping
by Emily Kuck and Timothy Sands
Sensors 2024, 24(2), 666; https://doi.org/10.3390/s24020666 - 20 Jan 2024
Viewed by 779
Abstract
Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller’s bandwidth. A variety of input trajectories can be used to drive a controller’s attempt to ameliorate the [...] Read more.
Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller’s bandwidth. A variety of input trajectories can be used to drive a controller’s attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A benchmark feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system’s flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Benchmark feedforward whiplash compensation is additionally implemented for comparison. The method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition)
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19 pages, 7575 KiB  
Article
Using Interoperability between Mobile Robot and KNX Technology for Occupancy Monitoring in Smart Home Care
by Jan Vanus, Radim Hercik and Petr Bilik
Sensors 2023, 23(21), 8953; https://doi.org/10.3390/s23218953 - 3 Nov 2023
Cited by 1 | Viewed by 1152
Abstract
It is important for older and disabled people who live alone to be able to cope with the daily challenges of living at home. In order to support independent living, the Smart Home Care (SHC) concept offers the possibility of providing comfortable control [...] Read more.
It is important for older and disabled people who live alone to be able to cope with the daily challenges of living at home. In order to support independent living, the Smart Home Care (SHC) concept offers the possibility of providing comfortable control of operational and technical functions using a mobile robot for operating and assisting activities to support independent living for elderly and disabled people. This article presents a unique proposal for the implementation of interoperability between a mobile robot and KNX technology in a home environment within SHC automation to determine the presence of people and occupancy of occupied spaces in SHC using measured operational and technical variables (to determine the quality of the indoor environment), such as temperature, relative humidity, light intensity, and CO2 concentration, and to locate occupancy in SHC spaces using magnetic contacts monitoring the opening/closing of windows and doors by indirectly monitoring occupancy without the use of cameras. In this article, a novel method using nonlinear autoregressive Neural Networks (NN) with exogenous inputs and nonlinear autoregressive is used to predict the CO2 concentration waveform to transmit the information from KNX technology to mobile robots for monitoring and determining the occupancy of people in SHC with better than 98% accuracy. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition)
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31 pages, 9102 KiB  
Article
Smooth Autonomous Patrolling for a Differential-Drive Mobile Robot in Dynamic Environments
by Ana Šelek, Marija Seder and Ivan Petrović
Sensors 2023, 23(17), 7421; https://doi.org/10.3390/s23177421 - 25 Aug 2023
Cited by 1 | Viewed by 1346
Abstract
Today, mobile robots have a wide range of real-world applications where they can replace or assist humans in many tasks, such as search and rescue, surveillance, patrolling, inspection, environmental monitoring, etc. These tasks usually require a robot to navigate through a dynamic environment [...] Read more.
Today, mobile robots have a wide range of real-world applications where they can replace or assist humans in many tasks, such as search and rescue, surveillance, patrolling, inspection, environmental monitoring, etc. These tasks usually require a robot to navigate through a dynamic environment with smooth, efficient, and safe motion. In this paper, we propose an online smooth-motion-planning method that generates a smooth, collision-free patrolling trajectory based on clothoid curves. Moreover, the proposed method combines global and local planning methods, which are suitable for changing large environments and enabling efficient path replanning with an arbitrary robot orientation. We propose a method for planning a smoothed path based on the golden ratio wherein a robot’s orientation is aligned with a new path that avoids unknown obstacles. The simulation results show that the proposed algorithm reduces the patrolling execution time, path length, and deviation of the tracked trajectory from the patrolling route compared to the original patrolling method without smoothing. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity, and its performance was validated through the results of an experiment employing a differential-drive mobile robot. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition)
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Review

Jump to: Research

15 pages, 496 KiB  
Review
Anomaly Detection Methods in Autonomous Robotic Missions
by Shivoh Chirayil Nandakumar, Daniel Mitchell, Mustafa Suphi Erden, David Flynn and Theodore Lim
Sensors 2024, 24(4), 1330; https://doi.org/10.3390/s24041330 - 19 Feb 2024
Cited by 1 | Viewed by 1715
Abstract
Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in [...] Read more.
Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Trajectory tracking control for mobile robots under output performance specifications and input saturation constraints
Authors: Trakas Panagiotis, Bechlioulis Charalampos
Affiliation: Department of Electrical and Computer Engineering, University of Patras
Abstract: In this work, the trajectory tracking control problem will be studied for mobile robots. Given certain input saturation constraints the control scheme tries to adapt and establish the best achievable performance specifications. When the reference trajectory exceeds the saturation limitations then a novel adaptive relaxation mechanism will secure stability. Experimental studies will verify the theoretical findings.

Title: Automation of a social robot via voice commands
Authors: PATRICIO OLMEDO MEDRANDA [1] JOSÉ YÉPEZ IDROVO [1] CARLOS FLORES-VÁZQUEZ [1,2] CECILIO ANGULO [2] (corresponding author) DAVID VALLEJO-RAMÍREZ [1]
Affiliation: [1] Universidad Católica de Cuenca, Ecuador [2] Universitat Politècnica de Catalunya, Spain
Abstract: This article examines two virtual assistant systems with conversational AI: one with online voice modulator and another with offline voice modulator. These assistants have the ability to provide useful and practical information to the client in various areas, such as health, tourism, food sales and care. The first system uses three fundamental libraries that allow voice transcription to text, speech synthesis from text and playback of audio files. Besides, the second system uses two fundamental libraries for speech recognition and synthesis. The advantages and drawbacks of each system through human-robot interaction by the user are analysed. It is concluded that choosing the right assistant depends on the specific environment in which it will be used.

Title: Anomaly Detection Methods in Autonomous Robotic Missions
Authors: Shivoh Chirayil Nandakumara, Daniel Mitchell, Mustafa Suphi Erden, David Flynn, Theodore Lim
Affiliation: Heriot-Watt University
Abstract: Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized utonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on “anomaly” and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Furthermore, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly and its detection methods that could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.

Title: Data analysis and data visualization of sensor data from an educational mobile robot
Authors: Dorina Plókai 1, Détár Borsa 1,2, Tamás Haidegger 2,3, Enikő Nagy1,*
Affiliation: 1 John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary; [email protected] 2 University Research and Innovation Center (EKIK), Óbuda University, Budapest, Hungary; [email protected] 3 Austrian Center for Medical Innovation and Technology (ACMIT Gmbh), Wiener Neustadt, Austria; [email protected] * Correspondence: [email protected]
Abstract: Analyzing and displaying data from the sensors of mobile robots used for educational purposes is a process of grouping and analyzing the information captured by the robots’ sensors according to different criteria. The data is further processed, either through statistical analysis or machine learning. This data is visualized using visualization tools such as charts and graphs, which help to interpret the data and identify any patterns, trends or discrepancies. This makes it easier to improve the robot's operation, improve its programs or replace sensors. The analysis and visualization of the data will enable more effective training of robots and contribute to the statistical analysis of the measurements, in particular to pinpoint the localization of mobile robots. The main objective is to develop a solution that can process, analyze and visualize sensor data collected by a mobile robot, with a particular focus on statistical analysis and the detection of correlations between data sets.

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