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Advanced Sensors Technologies Applied in Mobile Robot

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 44906

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Special Issue Editors

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
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
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

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

Published Papers (20 papers)

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Editorial

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4 pages, 175 KiB  
Editorial
Advanced Sensors Technologies Applied in Mobile Robot
by Gregor Klančar, Marija Seder and Sašo Blažič
Sensors 2023, 23(6), 2958; https://0-doi-org.brum.beds.ac.uk/10.3390/s23062958 - 08 Mar 2023
Viewed by 1348
Abstract
This special issue focuses on mobile robotic systems, where we are seeing a widespread increase in current applications as well as promising future applications enabled by the latest technologies in sensor development [...] Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)

Research

Jump to: Editorial

16 pages, 2680 KiB  
Article
Minimum-Time Trajectory Generation for Wheeled Mobile Systems Using Bézier Curves with Constraints on Velocity, Acceleration and Jerk
by Martina Benko Loknar, Gregor Klančar and Sašo Blažič
Sensors 2023, 23(4), 1982; https://0-doi-org.brum.beds.ac.uk/10.3390/s23041982 - 10 Feb 2023
Cited by 10 | Viewed by 1914
Abstract
This paper considers the problem of minimum-time smooth trajectory planning for wheeled mobile robots. The smooth path is defined by several Bézier curves and the calculated velocity profiles on individual segments are minimum-time with continuous velocity and acceleration in the joints. We describe [...] Read more.
This paper considers the problem of minimum-time smooth trajectory planning for wheeled mobile robots. The smooth path is defined by several Bézier curves and the calculated velocity profiles on individual segments are minimum-time with continuous velocity and acceleration in the joints. We describe a novel solution for the construction of a 5th order Bézier curve that enables a simple and intuitive parameterization. The proposed trajectory optimization considers environment space constraints and constraints on the velocity, acceleration, and jerk. The operation of the trajectory planning algorithm has been demonstrated in two simulations: on a racetrack and in a warehouse environment. Therefore, we have shown that the proposed path construction and trajectory generation algorithm can be applied to a constrained environment and can also be used in real-world driving scenarios. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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23 pages, 6358 KiB  
Article
Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints
by Peter Sarcevic, Dominik Csik and Akos Odry
Sensors 2023, 23(4), 1855; https://0-doi-org.brum.beds.ac.uk/10.3390/s23041855 - 07 Feb 2023
Cited by 15 | Viewed by 2259
Abstract
Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained [...] Read more.
Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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13 pages, 2823 KiB  
Article
Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game
by Wonjun Lee, Deokwon Lee, Sanghyub Lee, Kooksung Jun and Mun Sang Kim
Sensors 2023, 23(1), 246; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010246 - 26 Dec 2022
Cited by 7 | Viewed by 2277
Abstract
The identification of attention deficit hyperactivity disorder (ADHD) in children, which is increasing every year worldwide, is very important for early diagnosis and treatment. However, since ADHD is not a simple disease that can be diagnosed with a simple test, doctors require a [...] Read more.
The identification of attention deficit hyperactivity disorder (ADHD) in children, which is increasing every year worldwide, is very important for early diagnosis and treatment. However, since ADHD is not a simple disease that can be diagnosed with a simple test, doctors require a large period of time and substantial effort for accurate diagnosis and treatment. Currently, ADHD classification studies using various datasets and machine learning or deep learning algorithms are actively being conducted for the screening diagnosis of ADHD. However, there has been no study of ADHD classification using only skeleton data. It was hypothesized that the main symptoms of ADHD, such as distraction, hyperactivity, and impulsivity, could be differentiated through skeleton data. Thus, we devised a game system for the screening and diagnosis of children’s ADHD and acquired children’s skeleton data using five Azure Kinect units equipped with depth sensors, while the game was being played. The game for screening diagnosis involves a robot first travelling on a specific path, after which the child must remember the path the robot took and then follow it. The skeleton data used in this study were divided into two categories: standby data, obtained when a child waits while the robot demonstrates the path; and game data, obtained when a child plays the game. The acquired data were classified using the RNN series of GRU, RNN, and LSTM algorithms; a bidirectional layer; and a weighted cross-entropy loss function. Among these, an LSTM algorithm using a bidirectional layer and a weighted cross-entropy loss function obtained a classification accuracy of 97.82%. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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12 pages, 10711 KiB  
Communication
Experimental Investigations into Using Motion Capture State Feedback for Real-Time Control of a Humanoid Robot
by Mihaela Popescu, Dennis Mronga, Ivan Bergonzani, Shivesh Kumar and Frank Kirchner
Sensors 2022, 22(24), 9853; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249853 - 15 Dec 2022
Cited by 5 | Viewed by 2729
Abstract
Regardless of recent advances, humanoid robots still face significant difficulties in performing locomotion tasks. Among the key challenges that must be addressed to achieve robust bipedal locomotion are dynamically consistent motion planning, feedback control, and state estimation of such complex systems. In this [...] Read more.
Regardless of recent advances, humanoid robots still face significant difficulties in performing locomotion tasks. Among the key challenges that must be addressed to achieve robust bipedal locomotion are dynamically consistent motion planning, feedback control, and state estimation of such complex systems. In this paper, we investigate the use of an external motion capture system to provide state feedback to an online whole-body controller. We present experimental results with the humanoid robot RH5 performing two different whole-body motions: squatting with both feet in contact with the ground and balancing on one leg. We compare the execution of these motions using state feedback from (i) an external motion tracking system and (ii) an internal state estimator based on inertial measurement unit (IMU), forward kinematics, and contact sensing. It is shown that state-of-the-art motion capture systems can be successfully used in the high-frequency feedback control loop of humanoid robots, providing an alternative in cases where state estimation is not reliable. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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21 pages, 4782 KiB  
Article
Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot
by Ana Šelek, Marija Seder, Mišel Brezak and Ivan Petrović
Sensors 2022, 22(23), 9269; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239269 - 28 Nov 2022
Cited by 6 | Viewed by 1785
Abstract
The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path. In this paper, we propose a complete coverage path planning algorithm that generates smooth complete [...] Read more.
The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path. In this paper, we propose a complete coverage path planning algorithm that generates smooth complete coverage paths based on clothoids that allow a nonholonomic mobile robot to move in optimal time while following the path. This algorithm greatly reduces coverage time, the path length, and overlap area, and increases the coverage rate compared to the state-of-the-art complete coverage algorithms, which is verified by simulation. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity and allows path replanning in case the robot encounters unknown obstacles. The efficiency of the proposed algorithm is validated by experimental results on the Pioneer 3DX mobile robot. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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21 pages, 2291 KiB  
Article
Efficient Approach for Extracting High-Level B-Spline Features from LIDAR Data for Light-Weight Mapping
by Muhammad Usman, Ahmad Ali, Abdullah Tahir, Muhammad Zia Ur Rahman and Abdul Manan Khan
Sensors 2022, 22(23), 9168; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239168 - 25 Nov 2022
Cited by 3 | Viewed by 1311
Abstract
Light-weight and accurate mapping is made possible by high-level feature extraction from sensor readings. In this paper, the high-level B-spline features from a 2D LIDAR are extracted with a faster method as a solution to the mapping problem, making it possible for the [...] Read more.
Light-weight and accurate mapping is made possible by high-level feature extraction from sensor readings. In this paper, the high-level B-spline features from a 2D LIDAR are extracted with a faster method as a solution to the mapping problem, making it possible for the robot to interact with its environment while navigating. The computation time of feature extraction is very crucial when mobile robots perform real-time tasks. In addition to the existing assessment measures of B-spline feature extraction methods, the paper also includes a new benchmark time metric for evaluating how well the extracted features perform. For point-to-point association, the most reliable vertex control points of the spline features generated from the hints of low-level point feature FALKO were chosen. The standard three indoor and one outdoor data sets were used for the experiment. The experimental results based on benchmark performance metrics, specifically computation time, show that the presented approach achieves better results than the state-of-the-art methods for extracting B-spline features. The classification of the methods implemented in the B-spline features detection and the algorithms are also presented in the paper. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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22 pages, 4542 KiB  
Article
An Adaptive Prescribed Performance Tracking Motion Control Methodology for Robotic Manipulators with Global Finite-Time Stability
by Anh Tuan Vo, Thanh Nguyen Truong and Hee-Jun Kang
Sensors 2022, 22(20), 7834; https://0-doi-org.brum.beds.ac.uk/10.3390/s22207834 - 15 Oct 2022
Cited by 2 | Viewed by 1242
Abstract
In this paper, the problem of an APPTMC for manipulators is investigated. During the robot’s operation, the error states should be kept within an outlined range to ensure a steady-state and dynamic attitude. Firstly, we propose the modified PPFs. Afterward, a series of [...] Read more.
In this paper, the problem of an APPTMC for manipulators is investigated. During the robot’s operation, the error states should be kept within an outlined range to ensure a steady-state and dynamic attitude. Firstly, we propose the modified PPFs. Afterward, a series of transformed errors is used to convert “constrained” systems into equivalent “unconstrained” ones, to facilitate control design. The modified PPFs ensure position tracking errors are managed in a pre-designed performance domain. Especially, the SSE boundaries will be symmetrical to zero, so when the transformed error is zero, the tracking error will be as well. Secondly, a modified NISMS based on the transformed errors allows for determining the highest acceptable range of the tracking errors in the steady-state, finite-time convergence index, and singularity elimination. Thirdly, a fixed-time USOSMO is proposed to directly estimate the lumped uncertainty. Fourthly, an ASTwCL is applied to deal with observer output errors and chattering. Finally, an observer-based-control solution is synthesized from the above techniques to achieve PCP in the sense of finite-time Lyapunov stability. In addition, the precision, robustness, as well as harmful chattering reduction of the proposed APPTMC are improved significantly. The Lyapunov theory is used to analyze the stability of closed-loop systems. Throughout simulations, the proposed PPTMC has been shown to perform well and be effective. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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16 pages, 753 KiB  
Article
Efficient 3D Lidar Odometry Based on Planar Patches
by Andres Galeote-Luque, Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez
Sensors 2022, 22(18), 6976; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186976 - 15 Sep 2022
Cited by 2 | Viewed by 1865
Abstract
In this paper we present a new way to compute the odometry of a 3D lidar in real-time. Due to the significant relation between these sensors and the rapidly increasing sector of autonomous vehicles, 3D lidars have improved in recent years, with modern [...] Read more.
In this paper we present a new way to compute the odometry of a 3D lidar in real-time. Due to the significant relation between these sensors and the rapidly increasing sector of autonomous vehicles, 3D lidars have improved in recent years, with modern models producing data in the form of range images. We take advantage of this ordered format to efficiently estimate the trajectory of the sensor as it moves in 3D space. The proposed method creates and leverages a flatness image in order to exploit the information found in flat surfaces of the scene. This allows for an efficient selection of planar patches from a first range image. Then, from a second image, keypoints related to said patches are extracted. This way, our proposal computes the ego-motion by imposing a coplanarity constraint between pairs <point, plane> whose correspondences are iteratively updated. The proposed algorithm is tested and compared with state-of-the-art ICP algorithms. Experiments show that our proposal, running on a single thread, can run 5× faster than a multi-threaded implementation of GICP, while providing a more accurate localization. A second version of the algorithm is also presented, which reduces the drift even further while needing less than half of the computation time of GICP. Both configurations of the algorithm run at frame rates common for most 3D lidars, 10 and 20 Hz on a standard CPU. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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18 pages, 25002 KiB  
Article
Visual Servoing Approach to Autonomous UAV Landing on a Moving Vehicle
by Azarakhsh Keipour, Guilherme A. S. Pereira, Rogerio Bonatti, Rohit Garg, Puru Rastogi, Geetesh Dubey and Sebastian Scherer
Sensors 2022, 22(17), 6549; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176549 - 30 Aug 2022
Cited by 17 | Viewed by 2502
Abstract
Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle [...] Read more.
Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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19 pages, 15557 KiB  
Article
Modeling and Control of a Spherical Robot in the CoppeliaSim Simulator
by Guelis Montenegro, Roberto Chacón, Ernesto Fabregas, Gonzalo Garcia, Karla Schröder, Alberto Marroquín, Sebastián Dormido-Canto and Gonzalo Farias
Sensors 2022, 22(16), 6020; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166020 - 12 Aug 2022
Cited by 8 | Viewed by 2704
Abstract
This article presents the development of a model of a spherical robot that rolls to move and has a single point of support with the surface. The model was developed in the CoppeliaSim simulator, which is a versatile tool for implementing this kind [...] Read more.
This article presents the development of a model of a spherical robot that rolls to move and has a single point of support with the surface. The model was developed in the CoppeliaSim simulator, which is a versatile tool for implementing this kind of experience. The model was tested under several scenarios and control goals (i.e., position control, path-following and formation control) with control strategies such as reinforcement learning, and Villela and IPC algorithms. The results of these approaches were compared using performance indexes to analyze the performance of the model under different scenarios. The model and examples with different control scenarios are available online. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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14 pages, 2902 KiB  
Article
Real-Time Short-Term Pedestrian Trajectory Prediction Based on Gait Biomechanics
by Leticia González, Antonio M. López, Juan C. Álvarez and Diego Álvarez
Sensors 2022, 22(15), 5828; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155828 - 04 Aug 2022
Cited by 1 | Viewed by 1484
Abstract
The short-term prediction of a person’s trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton’s laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with [...] Read more.
The short-term prediction of a person’s trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton’s laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with the foundations of the basic kinematical models compromising their performance. In this paper, we propose a short-time prediction method based on gait biomechanics for real-time applications. This method relays on a single biomechanical variable, and it has a low computational burden, turning it into a feasible solution to implement in low-cost portable devices. We evaluate its performance from an experimental benchmark where several subjects walked steadily over straight and curved paths. With this approach, the results indicate a performance good enough to be applicable to a wide range of human–robot interaction applications. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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22 pages, 8929 KiB  
Article
A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms
by Thejus Pathmakumar, M. A. Viraj J. Muthugala, S. M. Bhagya P. Samarakoon, Braulio Félix Gómez and Mohan Rajesh Elara
Sensors 2022, 22(14), 5317; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145317 - 16 Jul 2022
Cited by 4 | Viewed by 1386
Abstract
Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain [...] Read more.
Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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15 pages, 10863 KiB  
Article
Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning Auditing Applications
by Thejus Pathmakumar, Mohan Rajesh Elara, Shreenhithy V Soundararajan and Balakrishnan Ramalingam
Sensors 2022, 22(14), 5201; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145201 - 12 Jul 2022
Cited by 1 | Viewed by 1430
Abstract
Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning [...] Read more.
Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset’s quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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22 pages, 1573 KiB  
Article
Robot Navigation Based on Potential Field and Gradient Obtained by Bilinear Interpolation and a Grid-Based Search
by Gregor Klančar, Andrej Zdešar and Mohan Krishnan
Sensors 2022, 22(9), 3295; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093295 - 25 Apr 2022
Cited by 6 | Viewed by 2061
Abstract
The original concept of the artificial potential field in robot path planning has spawned a variety of extensions to address its main weakness, namely the formation of local minima in which the robot may be trapped. In this paper, a smooth navigation function [...] Read more.
The original concept of the artificial potential field in robot path planning has spawned a variety of extensions to address its main weakness, namely the formation of local minima in which the robot may be trapped. In this paper, a smooth navigation function combining the Dijkstra-based discrete static potential field evaluation with bilinear interpolation is proposed. The necessary modifications of the bilinear interpolation method are developed to make it applicable to the path-planning application. The effect is that the strategy makes it possible to solve the problem of the local minima, to generate smooth paths with moderate computational complexity, and at the same time, to largely preserve the product of the computationally intensive static plan. To cope with detected changes in the environment, a simple planning strategy is applied, bypassing the static plan with the solution of the A* algorithm to cope with dynamic discoveries. Results from several test environments are presented to illustrate the advantages of the developed navigation model. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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22 pages, 8189 KiB  
Article
Passive Landmark Geometry Optimization and Evaluation for Reliable Autonomous Navigation in Mining Tunnels Using 2D Lidars
by Miguel Torres-Torriti , Paola Nazate-Burgos , Fabián Paredes-Lizama , Javier Guevara and Fernando Auat Cheein
Sensors 2022, 22(8), 3038; https://0-doi-org.brum.beds.ac.uk/10.3390/s22083038 - 15 Apr 2022
Cited by 5 | Viewed by 1940
Abstract
Autonomous navigation in mining tunnels is challenging due to the lack of satellite positioning signals and visible natural landmarks that could be exploited by ranging systems. Solutions requiring stable power feeds for locating beacons and transmitters are not accepted because of accidental damage [...] Read more.
Autonomous navigation in mining tunnels is challenging due to the lack of satellite positioning signals and visible natural landmarks that could be exploited by ranging systems. Solutions requiring stable power feeds for locating beacons and transmitters are not accepted because of accidental damage risks and safety requirements. Hence, this work presents an autonomous navigation approach based on artificial passive landmarks, whose geometry has been optimized in order to ensure drift-free localization of mobile units typically equipped with lidar scanners. The main contribution of the approach lies in the design and optimization of the landmarks that, combined with scan matching techniques, provide a reliable pose estimation in modern smoothly bored mining tunnels. A genetic algorithm is employed to optimize the landmarks’ geometry and positioning, thus preventing that the localization problem becomes ill-posed. The proposed approach is validated both in simulation and throughout a series of experiments with an industrial skid-steer CAT 262C robotic excavator, showing the feasibility of the approach with inexpensive passive and low-maintenance landmarks. The results show that the optimized triangular and symmetrical landmarks improve the positioning accuracy by 87.5% per 100 m traveled compared to the accuracy without landmarks. The role of optimized artificial landmarks in the context of modern smoothly bored mining tunnels should not be understated. The results confirm that without the optimized landmarks, the localization error accumulates due to odometry drift and that, contrary to the general intuition or belief, natural tunnel features alone are not sufficient for unambiguous localization. Therefore, the proposed approach ensures grid-based SLAM techniques can be implemented to successfully navigate in smoothly bored mining tunnels. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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21 pages, 4596 KiB  
Article
Coordinated Multi-Robotic Vehicles Navigation and Control in Shop Floor Automation
by Gregor Klančar and Marija Seder
Sensors 2022, 22(4), 1455; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041455 - 14 Feb 2022
Cited by 9 | Viewed by 1997
Abstract
In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a [...] Read more.
In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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20 pages, 9995 KiB  
Article
False Ceiling Deterioration Detection and Mapping Using a Deep Learning Framework and the Teleoperated Reconfigurable ‘Falcon’ Robot
by Archana Semwal, Rajesh Elara Mohan, Lee Ming Jun Melvin, Povendhan Palanisamy, Chanthini Baskar, Lim Yi, Sathian Pookkuttath and Balakrishnan Ramalingam
Sensors 2022, 22(1), 262; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010262 - 30 Dec 2021
Cited by 5 | Viewed by 2407
Abstract
Periodic inspection of false ceilings is mandatory to ensure building and human safety. Generally, false ceiling inspection includes identifying structural defects, degradation in Heating, Ventilation, and Air Conditioning (HVAC) systems, electrical wire damage, and pest infestation. Human-assisted false ceiling inspection is a laborious [...] Read more.
Periodic inspection of false ceilings is mandatory to ensure building and human safety. Generally, false ceiling inspection includes identifying structural defects, degradation in Heating, Ventilation, and Air Conditioning (HVAC) systems, electrical wire damage, and pest infestation. Human-assisted false ceiling inspection is a laborious and risky task. This work presents a false ceiling deterioration detection and mapping framework using a deep-neural-network-based object detection algorithm and the teleoperated ‘Falcon’ robot. The object detection algorithm was trained with our custom false ceiling deterioration image dataset composed of four classes: structural defects (spalling, cracks, pitted surfaces, and water damage), degradation in HVAC systems (corrosion, molding, and pipe damage), electrical damage (frayed wires), and infestation (termites and rodents). The efficiency of the trained CNN algorithm and deterioration mapping was evaluated through various experiments and real-time field trials. The experimental results indicate that the deterioration detection and mapping results were accurate in a real false-ceiling environment and achieved an 89.53% detection accuracy. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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14 pages, 1330 KiB  
Article
AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots
by Sathian Pookkuttath, Mohan Rajesh Elara, Vinu Sivanantham and Balakrishnan Ramalingam
Sensors 2022, 22(1), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010013 - 21 Dec 2021
Cited by 8 | Viewed by 3326
Abstract
Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive [...] Read more.
Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot ‘Snail’ with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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17 pages, 9511 KiB  
Article
Autonomous UAV System for Cleaning Insulators in Power Line Inspection and Maintenance
by Ricardo Lopez Lopez, Manuel Jesus Batista Sanchez, Manuel Perez Jimenez, Begoña C. Arrue and Anibal Ollero
Sensors 2021, 21(24), 8488; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248488 - 20 Dec 2021
Cited by 21 | Viewed by 4349
Abstract
The inspection and maintenance tasks of electrical installations are very demanding. Nowadays, insulator cleaning is carried out manually by operators using scaffolds, ropes, or even helicopters. However, these operations involve potential risks for humans and the electrical structure. The use of Unmanned Aerial [...] Read more.
The inspection and maintenance tasks of electrical installations are very demanding. Nowadays, insulator cleaning is carried out manually by operators using scaffolds, ropes, or even helicopters. However, these operations involve potential risks for humans and the electrical structure. The use of Unmanned Aerial Vehicles (UAV) to reduce the risk of these tasks is rising. This paper presents an UAV to autonomously clean insulators on power lines. First, an insulator detection and tracking algorithm has been implemented to control the UAV in operation. Second, a cleaning tool has been designed consisting of a pump, a tank, and an arm to direct the flow of cleaning liquid. Third, a vision system has been developed that is capable of detecting soiled areas using a semantic segmentation neuronal network, calculating the trajectory for cleaning in the image plane, and generating arm trajectories to efficiently clean the insulator. Fourth, an autonomous system has been developed to land on a charging pad to charge the batteries and potentially fill the tank with cleaning liquid. Finally, the autonomous system has been validated in a controlled outdoor environment. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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