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Kanellakis, C., Karvelis, P. S., Mansouri, S. S., Agha-mohammadi, A.-a. & Nikolakopoulos, G. (2021). Towards Autonomous Aerial Scouting Using Multi-Rotors in Subterranean Tunnel Navigation. IEEE Access, 9, 66477-66485
Open this publication in new window or tab >>Towards Autonomous Aerial Scouting Using Multi-Rotors in Subterranean Tunnel Navigation
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2021 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 66477-66485Article in journal (Refereed) Published
Abstract [en]

This work establishes a robocentric framework around a non-linear Model Predictive Control (NMPC) for autonomous navigation of quadrotors in tunnel-like environments. The proposed framework enables obstacle free navigation capabilities for resource constraint platforms in areas with critical challenges including darkness, textureless surfaces as well as areas with self-similar geometries, without any prior knowledge. The core contribution of the proposed framework stems from the merging of perception dynamics in a model-based optimization approach, aligning the vehicles heading to the tunnels’ open space expressed in the x axis coordinate in the image frame of the most distant area. Moreover, the aerial vehicle is considered as a free-flying object that plans its actions using egocentric onboard sensors. The proposed method can be deployed in both fully illuminated indoor corridors or featureless dark tunnels, leveraging visual processing from either RGB-D or monocular sensors for generating direction commands to keep flying in the proper direction. Multiple experimental field trials demonstrate the effectiveness of the proposed method in challenging environments.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Perception Aware Control, Vision based navigation, Micro Aerial Vehicles
National Category
Control Engineering
Research subject
Robotics and Artificial Intelligence; Control Engineering
Identifiers
urn:nbn:se:ltu:diva-84178 (URN)10.1109/ACCESS.2021.3076578 (DOI)000648331000001 ()2-s2.0-85105084498 (Scopus ID)
Funder
Interreg Nord, NYPS 20202891EU, Horizon 2020, 869379
Note

Validerad;2021;Nivå 2;2021-05-18 (alebob);

For correction, see: C. Kanellakis, P. S. Karvelis, S. S. Mansouri, A. -A. Agha-Mohammadi and G. Nikolakopoulos, "Correction to “Towards Autonomous Aerial Scouting Using Multi-Rotors in Subterranean Tunnel Navigation”," in IEEE Access, vol. 9, pp. 80208-80208, 2021, doi: 10.1109/ACCESS.2021.3084363.

Available from: 2021-05-07 Created: 2021-05-07 Last updated: 2023-09-05Bibliographically approved
Mansouri, S. S., Kanellakis, C., Karvelis, P., Kominiak, D. & Nikolakopoulos, G. (2020). MAV Navigation in Unknown Dark Underground Mines Using Deep Learning. In: European Control Conference 2020: . Paper presented at 2020 European Control Conference (ECC), 12-15 May, 2020, Saint Petersburg, Russia (pp. 1943-1948). IEEE
Open this publication in new window or tab >>MAV Navigation in Unknown Dark Underground Mines Using Deep Learning
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2020 (English)In: European Control Conference 2020, IEEE, 2020, p. 1943-1948Conference paper, Published paper (Refereed)
Abstract [en]

This article proposes a Deep Learning (DL) method to enable fully autonomous flights for low-cost Micro Aerial Vehicles (MAVs) in unknown dark underground mine tunnels. This kind of environments pose multiple challenges including lack of illumination, narrow passages, wind gusts and dust. The proposed method does not require accurate pose estimation and considers the flying platform as a floating object. The Convolutional Neural Network (CNN) supervised image classifier method corrects the heading of the MAV towards the center of the mine tunnel by processing the image frames from a single on-board camera, while the platform navigates at constant altitude and desired velocity references. Moreover, the output of the CNN module can be used from the operator as means of collision prediction information. The efficiency of the proposed method has been successfully experimentally evaluated in multiple field trials in an underground mine in Sweden, demonstrating the capability of the proposed method in different areas and illumination levels.

Place, publisher, year, edition, pages
IEEE, 2020
Series
European Control Conference (ECC)
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-79113 (URN)10.23919/ECC51009.2020.9143842 (DOI)000613138000334 ()2-s2.0-85090157713 (Scopus ID)
Conference
2020 European Control Conference (ECC), 12-15 May, 2020, Saint Petersburg, Russia
Note

ISBN för värdpublikation: 978-3-90714-402-2, 978-1-7281-8813-3

Available from: 2020-06-01 Created: 2020-06-01 Last updated: 2023-09-05Bibliographically approved
Kanellakis, C., Karvelis, P., Mansouri, S. S., Agha-mohammadi, A.-a. & Nikolakopoulos, G. (2020). Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments. In: Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann (Ed.), 21th IFAC World Congress: . Paper presented at 21st IFAC World Congress, Berlin, Germany, July 11-17, 2020 (pp. 9288-9294). Elsevier
Open this publication in new window or tab >>Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments
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2020 (English)In: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, p. 9288-9294Conference paper, Published paper (Refereed)
Abstract [en]

This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform’s altitude. The extracted visual dynamics are coupled in the sequel with the NMPC problem, structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.

Place, publisher, year, edition, pages
Elsevier, 2020
Series
IFAC-PapersOnLine, E-ISSN 2405-8963 ; 53 (2)
Keywords
Autonomous Vehicles, Robot Navigation, Non linear model predictive control, MAV
National Category
Control Engineering
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-83879 (URN)10.1016/j.ifacol.2020.12.2382 (DOI)000652593100084 ()2-s2.0-85108025677 (Scopus ID)
Conference
21st IFAC World Congress, Berlin, Germany, July 11-17, 2020
Funder
EU, Horizon 2020, 730302 SIMS
Available from: 2021-04-22 Created: 2021-04-22 Last updated: 2023-09-05Bibliographically approved
Kanellakis, C., Mansouri, S. S., Castaño, M., Karvelis, P., Kominiak, D. & Nikolakopoulos, G. (2020). Where to look: a collection of methods for MAV heading correction in underground tunnels. IET Image Processing, 14(10), 2020-2027
Open this publication in new window or tab >>Where to look: a collection of methods for MAV heading correction in underground tunnels
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2020 (Swedish)In: IET Image Processing, ISSN 1751-9659, E-ISSN 1751-9667, Vol. 14, no 10, p. 2020-2027Article in journal (Refereed) Published
Abstract [en]

Degraded Subterranean environments are an attractive case for miniature aerial vehicles, since there is a constant need to increase the safety operations in underground mines. The starting point for integrating aerial vehicles in the mining process is the capability to reliably navigate along tunnels. Inspired by recent advancements, this paper presents a collection of different, experimentally verified, methods tackling the problem of MAVs heading regulation while navigating in dark and textureless tunnel areas. More specifically, four different methods are presented in this work with the common goal to identify open space in the tunnel and align the MAV heading using either visual sensor in methods a) single image depth estimation, b) darkness contour detection, c) Convolutional Neural Network (CNN) regression and 2D Lidar sensor in method d) range geometry. For the works a)-c) the dark scene in the middle of the tunnel is considered as open space and is processed and converted to yaw rate command, while d) examines the geometry of the range measurements to calculate the yaw rate command. Experimental results from real underground tunnel demonstrate the performance of the methods in the field, while setting the ground for further developments in the aerial robotics community.

Place, publisher, year, edition, pages
The Institution of Engineering and Technology, 2020
National Category
Robotics Other Civil Engineering
Research subject
Robotics and Artificial Intelligence; Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-80881 (URN)10.1049/iet-ipr.2019.1423 (DOI)000583360400010 ()2-s2.0-85093857578 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-09-22 (johcin)

Available from: 2020-09-22 Created: 2020-09-22 Last updated: 2023-09-05Bibliographically approved
Kanellakis, C., Karvelis, P. & Nikolakopoulos, G. (2019). Image Enhancing in Poorly Illuminated Subterranean Environments for MAV Applications: A Comparison Study. In: Dimitrios Tzovaras; Dimitrios Giakoumis; Markus Vincze; Antonis Argyros (Ed.), Computer Vision Systems: 12th International Conference, ICVS 2019, Thessaloniki, Greece, September 23–25, 2019, Proceedings. Paper presented at 12th International Conference (ICVS 2019), Thessaloniki, Greece, September 23–25, 2019 (pp. 511-520). Springer
Open this publication in new window or tab >>Image Enhancing in Poorly Illuminated Subterranean Environments for MAV Applications: A Comparison Study
2019 (English)In: Computer Vision Systems: 12th International Conference, ICVS 2019, Thessaloniki, Greece, September 23–25, 2019, Proceedings / [ed] Dimitrios Tzovaras; Dimitrios Giakoumis; Markus Vincze; Antonis Argyros, Springer, 2019, p. 511-520Conference paper, Published paper (Refereed)
Abstract [en]

This work focuses on a comprehensive study and evaluation of existing low-level vision techniques for low light image enhancement, targeting applications in subterranean environments. More specifically, an emerging effort is currently pursuing the deployment of Micro Aerial Vehicles in subterranean environments for search and rescue missions, infrastructure inspection and other tasks. A major part of the autonomy of these vehicles, as well as the feedback to the operator, has been based on the processing of the information provided from onboard visual sensors. Nevertheless, subterranean environments are characterized by a low natural illumination that directly affects the performance of the utilized visual algorithms. In this article, an novel extensive comparison study is presented among five State-of the-Art low light image enhancement algorithms for evaluating their performance and identifying further developments needed. The evaluation has been performed from datasets collected in real underground tunnel environments with challenging conditions from the onboard sensor of a MAV. 

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11754
Keywords
Low light imaging, Image enhancement, Subterranean MAV applications
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-86155 (URN)10.1007/978-3-030-34995-0_46 (DOI)000548737700046 ()2-s2.0-85076943272 (Scopus ID)
Conference
12th International Conference (ICVS 2019), Thessaloniki, Greece, September 23–25, 2019
Funder
EU, Horizon 2020, 730302 SIMS
Note

ISBN för värdpublikation: 978-3-030-34994-3; 978-3-030-34995-0

Available from: 2021-06-30 Created: 2021-06-30 Last updated: 2023-09-05Bibliographically approved
Eleftheroglou, N., Mansouri, S. S., Loutas, T., Karvelis, P., Georgoulas, G., Nikolakopoulos, G. & Zarouchas, D. (2019). Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification. Applied Energy, 254, Article ID 113677.
Open this publication in new window or tab >>Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification
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2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 254, article id 113677Article in journal (Refereed) Published
Abstract [en]

In this paper, the discharge voltage is utilized as a critical indicator towards the probabilistic estimation of the Remaining Useful Life until the End-of-Discharge of the Lithium-Polymer batteries of unmanned aerial vehicles. Several discharge voltage histories obtained during actual flights constitute the in-house developed training dataset. Three data-driven prognostic methodologies are presented based on state-of-the-art as well as innovative mathematical models i.e. Gradient Boosted Trees, Bayesian Neural Networks and Non-Homogeneous Hidden Semi Markov Models. The training and testing process of all models is described in detail. Remaining Useful Life prognostics in unseen data are obtained from all three methodologies. Beyond the mean estimates, the uncertainty associated with the point predictions is quantified and upper/lower confidence bounds are also provided. The Remaining Useful Life prognostics during six random flights starting from fully charged batteries are presented, discussed and the pros and cons of each methodology are highlighted. Several special metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding their prognostic capabilities and potential.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Remaining useful life, Data-driven prognostics, UAVs, Li-Po batteries, End of discharge, Machine learning
National Category
Robotics Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-75673 (URN)10.1016/j.apenergy.2019.113677 (DOI)000497974600073 ()2-s2.0-85070739542 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-08-27 (johcin)

Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2023-09-05Bibliographically approved
Kanellakis, C., Karvelis, P. & Nikolakopoulos, G. (2019). On Image based Enhancement for 3D Dense Reconstruction of Low Light Aerial Visual Inspected Environments. In: Kohei Arai, Supriya Kapoor (Ed.), Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 2. Paper presented at 2019 Computer Vision Conference (CVC), 25-26 April, 2019, Las Vegas, Nevada, USA (pp. 265-279). Springer
Open this publication in new window or tab >>On Image based Enhancement for 3D Dense Reconstruction of Low Light Aerial Visual Inspected Environments
2019 (English)In: Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 2 / [ed] Kohei Arai, Supriya Kapoor, Springer, 2019, p. 265-279Conference paper, Published paper (Refereed)
Abstract [en]

Micro Aerial Vehicles (MAV)s have been distinguished, in the last decade, for their potential to inspect infrastructures in an active manner and provide critical information to the asset owners. Inspired by this trend, the mining industry is lately focusing to incorporate MAVs in their production cycles. Towards this direction, this article proposes a novel method to enhance 3D reconstruction of low-light environments, like underground tunnels, by using image processing. More specifically, the main idea is to enhance the low light resolution of the collected images, captured onboard an aerial platform, before inserting them to the reconstruction pipeline. The proposed method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm that limits the noise, while amplifies the contrast of the image. The overall efficiency and improvement achieved of the novel architecture has been extensively and successfully evaluated by utilizing data sets captured from real scale underground tunnels using a quadrotor.

Place, publisher, year, edition, pages
Springer, 2019
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 944
Keywords
Low-illumination image processing, 3D reconstruction, MAVs
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-76966 (URN)10.1007/978-3-030-17798-0_23 (DOI)000490760000022 ()2-s2.0-85065479828 (Scopus ID)
Conference
2019 Computer Vision Conference (CVC), 25-26 April, 2019, Las Vegas, Nevada, USA
Note

ISBN för värdpublikation: 978-3-030-17797-3, 978-3-030-17798-0

Available from: 2019-11-29 Created: 2019-11-29 Last updated: 2023-09-05Bibliographically approved
Kanellakis, C., Karvelis, P. & Nikolakopoulos, G. (2019). Open Space Attraction Based Navigation in Dark Tunnels for MAVs. In: Dimitrios Tzovaras, Dimitrios Giakoumis, Markus Vincze, Antonis Argyros (Ed.), Computer Vision Systems: 12th International Conference, ICVS 2019 Thessaloniki, Greece, September 23–25, 2019 Proceedings. Paper presented at 12th International Conference on Computer Vision Systems (ICVS 2019), September 23-25, 2019, Thessaloniki, Greece (pp. 110-119). Springer
Open this publication in new window or tab >>Open Space Attraction Based Navigation in Dark Tunnels for MAVs
2019 (English)In: Computer Vision Systems: 12th International Conference, ICVS 2019 Thessaloniki, Greece, September 23–25, 2019 Proceedings / [ed] Dimitrios Tzovaras, Dimitrios Giakoumis, Markus Vincze, Antonis Argyros, Springer, 2019, p. 110-119Conference paper, Published paper (Refereed)
Abstract [en]

This work establishes a novel framework for characterizing the open space of featureless dark tunnel environments for Micro Aerial Vehicles (MAVs) navigation tasks. The proposed method leverages the processing of a single camera to identify the deepest area in the scene in order to provide a collision free heading command for the MAV. In the sequel and inspired by haze removal approaches, the proposed novel idea is structured around a single image depth map estimation scheme, without metric depth measurements. The core contribution of the developed framework stems from the extraction of a 2D centroid in the image plane that characterizes the center of the tunnel’s darkest area, which is assumed to represent the open space, while the robustness of the proposed scheme is being examined under varying light/dusty conditions. Simulation and experimental results demonstrate the effectiveness of the proposed method in challenging underground tunnel environments.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11754
Keywords
Depth map estimation, Open space attraction, Visual navigation, Micro Aerial Vehicles
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-76965 (URN)10.1007/978-3-030-34995-0_10 (DOI)000548737700010 ()2-s2.0-85076963724 (Scopus ID)
Conference
12th International Conference on Computer Vision Systems (ICVS 2019), September 23-25, 2019, Thessaloniki, Greece
Note

ISBN för värdpublikation: 978-3-030-34994-3, 978-3-030-34995-0

Available from: 2019-11-29 Created: 2019-11-29 Last updated: 2023-09-05Bibliographically approved
Eleftheroglou, N., Zarouchas, D., Loutas, T., Mansouri, S. S., Georgoulas, G., Karvelis, P., . . . Benedictus, R. (2019). Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries. In: N. Scott Clements (Ed.), Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019: . Paper presented at Annual Conference of the Prognostics and Health Management Society, 23-26 September, 2019, Scottsdale, Arizona, USA. Prognostics and Health Management Society
Open this publication in new window or tab >>Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries
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2019 (English)In: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019 / [ed] N. Scott Clements, Prognostics and Health Management Society , 2019Conference paper, Published paper (Other academic)
Abstract [en]

This paper examines diagnostics and prognostics of Lithium-Polymer (Li-Po) batteries for unmanned aerial vehicles (UAVs). Several discharge voltage histories obtained during actual indoor flights constitute the training data for a data-driven approach, utilizing the Non-Homogenous Hidden Semi Markov model (NHHSMM). NHHSMM is a suitable candidate as it has a rich mathematical structure, which is capable of describing the discharge process of Li-Po batteries and providing diagnostic and prognostic measures. Diagnostics and prognostics in unseen data are obtained and compared with the actual remaining flight time in order to validate the effectiveness of the selected model.

Place, publisher, year, edition, pages
Prognostics and Health Management Society, 2019
Series
Proceedings of the Annual Conference of the Prognostics and Health Management Society, ISSN 2325-0178 ; 11(1)
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-78813 (URN)10.36001/phmconf.2019.v11i1.785 (DOI)2-s2.0-85083955497 (Scopus ID)
Conference
Annual Conference of the Prognostics and Health Management Society, 23-26 September, 2019, Scottsdale, Arizona, USA
Note

ISBN för värdpublikation: 978-1-936263-29-5

Available from: 2020-05-07 Created: 2020-05-07 Last updated: 2023-09-05Bibliographically approved
Mansouri, S. S., Karvelis, P., Kanellakis, C., Kominiak, D. & Nikolakopoulos, G. (2019). Vision-based MAV Navigation in Underground Mine Using Convolutional Neural Network. In: IECON 2019: 45th Annual Conference of the IEEE Industrial Electronics Society. Paper presented at 45th Annual Conference of the IEEE Industrial Electronics Society (IECON 2019), 14-17 October, 2019, Lisbon, Portugal (pp. 750-755). IEEE
Open this publication in new window or tab >>Vision-based MAV Navigation in Underground Mine Using Convolutional Neural Network
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2019 (English)In: IECON 2019: 45th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2019, p. 750-755Conference paper, Published paper (Refereed)
Abstract [en]

This article presents a Convolutional Neural Network (CNN) method to enable autonomous navigation of low-cost Micro Aerial Vehicle (MAV) platforms along dark underground mine environments. The proposed CNN component provides on-line heading rate commands for the MAV by utilising the image stream from the on-board camera, thus allowing the platform to follow a collision-free path along the tunnel axis. A novel part of the developed method consists of the generation of the data-set used for training the CNN. More specifically, inspired from single image haze removal algorithms, various image data-sets collected from real tunnel environments have been processed offline to provide an estimation of the depth information of the scene, where ground truth is not available. The calculated depth map is used to extract the open space in the tunnel, expressed through the area centroid and is finally provided in the training of the CNN. The method considers the MAV as a floating object, thus accurate pose estimation is not required. Finally, the capability of the proposed method has been successfully experimentally evaluated in field trials in an underground mine in Sweden.

Place, publisher, year, edition, pages
IEEE, 2019
Series
Annual Conference of Industrial Electronics Society, ISSN 2577-1647, E-ISSN 2577-1647
Keywords
Mining Aerial Robotics, Deep Learning for Navigation, MAV
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-75674 (URN)10.1109/IECON.2019.8927168 (DOI)000522050600117 ()2-s2.0-85083963074 (Scopus ID)
Conference
45th Annual Conference of the IEEE Industrial Electronics Society (IECON 2019), 14-17 October, 2019, Lisbon, Portugal
Note

ISBN för värdpublikation: 978-1-7281-4878-6, 978-1-7281-4879-3

Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2023-09-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0483-4868

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