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Robotics and AI for Infrastructure Inspection and Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 16707

Special Issue Editor

Gustavo Stefanini Advanced Robotics Research Center, Scuola Superiore Sant’Anna di Studi Universitari e di Perfezionamento, Pisa, Italy
Interests: robotics; artificial intelligence; computer vision; 3D reconstruction; image processing; localization methods; mapping; inspection robotics; deep Learning; industrial monitoring; smart sensors; photogrammetry; LiDAR; SAR; farming applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Infrastructure monitoring and inspection, including bridges, pipelines, electrical grids, roads, tunnels, and other facilities, is crucial for structure usage and safety, yet it is expensive, time-consuming, and dangerous.

According to national regulations, each infrastructure requires a periodical inspection every few years or continuous monitoring in the most severe cases to ensure the absence of critical damages like cracks, rusting, deteriorations, etc. This means that every day, a large number of infrastructures need an inspection. Such inspections usually require crews of professionals, heavy machinery with lifts, people rappelling from dangerous heights, etc., resulting in long inspection durations. The current development of robotics technologies and artificial intelligence algorithms introduces novel approaches that may alleviate such demanding operations.

The proposed Special Issue aims to collect novel approaches of AI and robotics applied in infrastructure inspection and monitoring by taking advantage of existing or novel sensing technologies.

Contributions introducing novel algorithms, approaches, and methodologies are welcomed, as well as papers dealing with specific applications of remote sensing.

A list of non-comprehensive suggested topics includes:

  • Drones for inspection and monitoring
  • SAR based monitoring of infrastructures
  • Road detection from remote sensing images
  • AI methods for detection and classification of structural damages
  • Robotic systems for safe and remote inspection of infrastructures

Dr. Paolo Tripicchio
Guest Editor

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. Remote Sensing 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 2700 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

  • SAR
  • Monitoring
  • Machine learning
  • Robotics
  • Mapping

Published Papers (6 papers)

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Research

22 pages, 48961 KiB  
Article
Flight Planning for Survey-Grade 3D Reconstruction of Truss Bridges
Remote Sens. 2022, 14(13), 3200; https://doi.org/10.3390/rs14133200 - 03 Jul 2022
Cited by 6 | Viewed by 2219
Abstract
Autonomous UAV 3D reconstruction has been widely used for infrastructure inspections and asset management. However, its applications on truss structures remain a challenging task due to geometric complexity and the severe self-occlusion problem of the truss structures when constrained by camera FOV, safety [...] Read more.
Autonomous UAV 3D reconstruction has been widely used for infrastructure inspections and asset management. However, its applications on truss structures remain a challenging task due to geometric complexity and the severe self-occlusion problem of the truss structures when constrained by camera FOV, safety clearance, and flight duration. This paper proposes a new flight planning method to effectively address the self-occlusion problem to enable autonomous and efficient data acquisition for survey-grade 3D truss reconstruction. The proposed method contains two steps: First, identifying a minimal set of viewpoints achieves the maximal reconstruction quality at every observed surface of the truss geometry through an iterative optimization schema. Second, converting the optimal viewpoints into the shortest, collision-free flight trajectories while considering the UAV constraints. The computed flight path can also be implemented in a multi-UAV fashion. Evaluations of the proposed method include a synthetic truss bridge and a real-world truss bridge. The evaluation results suggested that the proposed approach outperforms the existing works in terms of 3D reconstruction quality while taking less time in both the inflight image acquisition and the post-flight 3D reconstruction. Full article
(This article belongs to the Special Issue Robotics and AI for Infrastructure Inspection and Monitoring)
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17 pages, 41849 KiB  
Article
A Novel Remote Visual Inspection System for Bridge Predictive Maintenance
Remote Sens. 2022, 14(9), 2248; https://doi.org/10.3390/rs14092248 - 07 May 2022
Cited by 12 | Viewed by 2822
Abstract
Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact [...] Read more.
Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact that they can cause loss of lives. Traditional inspection methods are not objective, tied to the inspector’s experience and require human presence on site. To overpass the limits of the current technologies and methods, the authors of this paper developed a unique new concept: a remote visual inspection system to perform predictive maintenance on infrastructures such as bridges. This is based on the fusion between advanced robotic technologies and the Automated Visual Inspection that guarantees objective results, high-level of safety and low processing time of the results. Full article
(This article belongs to the Special Issue Robotics and AI for Infrastructure Inspection and Monitoring)
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17 pages, 5005 KiB  
Article
Path Planning and Control of a UAV Fleet in Bridge Management Systems
Remote Sens. 2022, 14(8), 1858; https://doi.org/10.3390/rs14081858 - 12 Apr 2022
Cited by 13 | Viewed by 2812
Abstract
Traditional methodologies for precise inspection of bridges (pavement, beams, column cap, column, joints and inside box girder, etc.) with By-bridge equipment, Aerial Work Platform (AWP) or via ropes have several limits that can be overcome by using Unmanned Aerial Vehicles (UAVs). The constant [...] Read more.
Traditional methodologies for precise inspection of bridges (pavement, beams, column cap, column, joints and inside box girder, etc.) with By-bridge equipment, Aerial Work Platform (AWP) or via ropes have several limits that can be overcome by using Unmanned Aerial Vehicles (UAVs). The constant development in this field allows us to go beyond the manual control and the use of a single UAV. In the context of inspection rules, this research provides new inputs to the multilevel approach used today and to the methods of structural inspection with drones. Today, UAV-based inspections are limited by manual and/or semi-automatic control with many restrictions on trajectory settings, especially for areas of difficult access with Global Navigation Satellite Systems (GNSS) denied that still require the intervention of a human operator. This work proposes the use of autonomous navigation with a fleet of UAVs for infrastructural inspections. Starting from a digital twin, a solution is provided to problems such as the definition of a set of reference trajectories and the design of a position controller. A workflow to integrate a generic Bridge Management System (BMS) with this type of approach is provided. Full article
(This article belongs to the Special Issue Robotics and AI for Infrastructure Inspection and Monitoring)
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21 pages, 32637 KiB  
Article
Design and Experimental Evaluation of an Aerial Solution for Visual Inspection of Tunnel-like Infrastructures
Remote Sens. 2022, 14(1), 195; https://doi.org/10.3390/rs14010195 - 02 Jan 2022
Cited by 8 | Viewed by 2058
Abstract
Current railway tunnel inspections rely on expert operators performing a visual examination of the entire infrastructure and manually annotating encountered defects. Automatizing the inspection and maintenance task of such critical and aging infrastructures has the potential to decrease the associated costs and risks. [...] Read more.
Current railway tunnel inspections rely on expert operators performing a visual examination of the entire infrastructure and manually annotating encountered defects. Automatizing the inspection and maintenance task of such critical and aging infrastructures has the potential to decrease the associated costs and risks. Contributing to this aim, the present work describes an aerial robotic solution designed to perform autonomous inspections of tunnel-like infrastructures. The proposed robotic system is equipped with visual and thermal sensors and uses an inspection-driven path planning algorithm to generate a path that maximizes the quality of the gathered data in terms of photogrammetry goals while optimizing the surface coverage and the total trajectory length. The performance of the planning algorithm is demonstrated in simulation against state-of-the-art methods and a wall-following inspection trajectory. Results of a real inspection test conducted in a railway tunnel are also presented, validating the whole system operation. Full article
(This article belongs to the Special Issue Robotics and AI for Infrastructure Inspection and Monitoring)
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20 pages, 6340 KiB  
Article
InsulatorGAN: A Transmission Line Insulator Detection Model Using Multi-Granularity Conditional Generative Adversarial Nets for UAV Inspection
Remote Sens. 2021, 13(19), 3971; https://doi.org/10.3390/rs13193971 - 04 Oct 2021
Cited by 11 | Viewed by 2050
Abstract
Insulator detection is one of the most significant issues in high-voltage transmission line inspection using unmanned aerial vehicles (UAVs) and has attracted attention from researchers all over the world. The state-of-the-art models in object detection perform well in insulator detection, but the precision [...] Read more.
Insulator detection is one of the most significant issues in high-voltage transmission line inspection using unmanned aerial vehicles (UAVs) and has attracted attention from researchers all over the world. The state-of-the-art models in object detection perform well in insulator detection, but the precision is limited by the scale of the dataset and parameters. Recently, the Generative Adversarial Network (GAN) was found to offer excellent image generation. Therefore, we propose a novel model called InsulatorGAN based on using conditional GANs to detect insulators in transmission lines. However, due to the fixed categories in datasets such as ImageNet and Pascal VOC, the generated insulator images are of a low resolution and are not sufficiently realistic. To solve these problems, we established an insulator dataset called InsuGenSet for model training. InsulatorGAN can generate high-resolution, realistic-looking insulator-detection images that can be used for data expansion. Moreover, InsulatorGAN can be easily adapted to other power equipment inspection tasks and scenarios using one generator and multiple discriminators. To give the generated images richer details, we also introduced a penalty mechanism based on a Monte Carlo search in InsulatorGAN. In addition, we proposed a multi-scale discriminator structure based on a multi-task learning mechanism to improve the quality of the generated images. Finally, experiments on the InsuGenSet and CPLID datasets demonstrated that our model outperforms existing state-of-the-art models by advancing both the resolution and quality of the generated images as well as the position of the detection box in the images. Full article
(This article belongs to the Special Issue Robotics and AI for Infrastructure Inspection and Monitoring)
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25 pages, 18797 KiB  
Article
MUSSOL: A Micro-Uas to Survey Ship Cargo hOLds
Remote Sens. 2021, 13(17), 3419; https://doi.org/10.3390/rs13173419 - 28 Aug 2021
Cited by 4 | Viewed by 2525
Abstract
Because of their high maneuverability and fast deployment times, aerial robots have recently gained popularity for automating inspection tasks. In this paper, we address the visual inspection of vessel cargo holds, aiming at safer, cost-efficient and more intensive visual inspections of ships by [...] Read more.
Because of their high maneuverability and fast deployment times, aerial robots have recently gained popularity for automating inspection tasks. In this paper, we address the visual inspection of vessel cargo holds, aiming at safer, cost-efficient and more intensive visual inspections of ships by means of a multirotor-type platform. To this end, the vehicle is equipped with a sensor suite able to supply the surveyor with imagery from relevant areas, while the control software is supporting the operator during flight with enhanced functionalities and reliable autonomy. All this has been accomplished in the context of the supervised autonomy (SA) paradigm, by means of extensive use of behaviour-based high-level control (including obstacle detection and collision prevention), all specifically devised for visual inspection. The full system has been evaluated both in laboratory and in real environments, on-board two different vessels. Results show the vehicle effective for the referred application, in particular due to the inspection-oriented capabilities it has been fitted with. Full article
(This article belongs to the Special Issue Robotics and AI for Infrastructure Inspection and Monitoring)
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