Change search
Link to record
Permanent link

Direct link
BETA
Mansouri, Sina SharifORCID iD iconorcid.org/0000-0001-7631-002x
Publications (10 of 18) Show all publications
Mansouri, S. S., Kanellakis, C., Kominiak, D. & Nikolakopoulos, G. (2020). Deploying MAVs for autonomous navigation in dark underground mine environments. Robotics and Autonomous Systems, 126, Article ID 103472.
Open this publication in new window or tab >>Deploying MAVs for autonomous navigation in dark underground mine environments
2020 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 126, article id 103472Article in journal (Refereed) Published
Abstract [en]

Operating Micro Aerial Vehicles (MAVs) in subterranean environments is becoming more and more relevant in the field of aerial robotics. Despite the large spectrum of technological advances in the field, flying in such challenging environments is still an ongoing quest that requires the combination of multiple sensor modalities like visual/thermal cameras as well as 3D and 2D lidars. Nevertheless, there exist cases in subterranean environments where the aim is to deploy fast and lightweight aerial robots for area reckoning purposes after an event (e.g. blasting in production areas). This work proposes a novel baseline approach for the navigation of resource constrained robots, introducing the aerial underground scout, with the main goal to rapidly explore unknown areas and provide a feedback to the operator. The main proposed framework focuses on the navigation, control and vision capabilities of the aerial platforms with low-cost sensor suites, contributing significantly towards real-life applications. The merit of the proposed control architecture is that it considers the flying platform as a floating object, composing a velocity controller on the x, y axes and altitude control to navigate along the tunnel. Two novel approaches make up the cornerstone of the proposed contributions for the task of navigation: (1) a vector geometry method based on 2D lidar, and (2) a Deep Learning (DL) method through a classification process based on an on-board image stream, where both methods correct the heading towards the center of the mine tunnel. Finally, the framework has been evaluated in multiple field trials in an underground mine in Sweden.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
MAVs navigation, Autonomous tunnel inspection, Mining aerial robotics
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-77844 (URN)10.1016/j.robot.2020.103472 (DOI)000524382000003 ()
Note

Validerad;2020;Nivå 2;2020-02-25 (alebob)

Available from: 2020-02-25 Created: 2020-02-25 Last updated: 2020-04-29Bibliographically approved
Mansouri, S. S., Arranz, M. C., Kanellakis, C. & Nikolakopoulos, G. (2019). Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection. In: : . Paper presented at 12th International Conference on Computer Vision Systems (ICVS 2019).
Open this publication in new window or tab >>Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This article considers a low-cost and light weight platform for the task of autonomous flying for inspection in underground mine tunnels. The main contribution of this paper is integrating simple, efficient and well-established methods in the computer vision community in a state of the art vision-based system for Micro Aerial Vehicle (MAV) navigation in dark tunnels. These methods include Otsu's threshold and Moore-Neighborhood object tracing. The vision system can detect the position of low-illuminated tunnels in image frame by exploiting the inherent darkness in the longitudinal direction. In the sequel, it is converted from the pixel coordinates to the heading rate command of the MAV for adjusting the heading towards the center of the tunnel. The efficacy of the proposed framework has been evaluated in multiple experimental field trials in an underground mine in Sweden, thus demonstrating the capability of low-cost and resource-constrained aerial vehicles to fly autonomously through tunnel confined spaces.

Keywords
Micro Aerial Vehicles (MAVs), Vision-based Navigation, Autonomous Drift Inspection, Otsu's Theshold, Moore-Neighborhood Tracing
National Category
Control Engineering Other Civil Engineering
Research subject
Control Engineering; Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75270 (URN)
Conference
12th International Conference on Computer Vision Systems (ICVS 2019)
Funder
EU, Horizon 2020, 730302
Available from: 2019-07-09 Created: 2019-07-09 Last updated: 2019-08-13
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
Show others...
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: 2019-12-18Bibliographically approved
Koval, A., Mansouri, S. S. & Nikolakopoulos, G. (2019). Online Multi-Agent Based Cooperative Exploration and Coverage in Complex Environment. In: : . Paper presented at The European Control Conference (ECC 2019).
Open this publication in new window or tab >>Online Multi-Agent Based Cooperative Exploration and Coverage in Complex Environment
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this article, an online collaborative exploration and coverage method is proposed for the unknown complex environment with multiple agents. The exploration and coverage is based on Boustrophedon motion, while the detection conditions for backtracking points have been modified based on mission requirements, the battery charge level of each agent is considered to reduce agent loss, and collision free paths are generated. The proposed method is evaluated in simulation, where complex environment with multiple branches is explored by multiple agents.

National Category
Robotics
Identifiers
urn:nbn:se:ltu:diva-73507 (URN)
Conference
The European Control Conference (ECC 2019)
Projects
Swedish Institute VISBY programme
Funder
EU, Horizon 2020, 730302
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-18
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
Show others...
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: 2020-05-07Bibliographically approved
Kanellakis, C., Mansouri, S. S., Georgoulas, G. & Nikolakopoulos, G. (2019). Towards Autonomous Surveying of Underground Mine using MAVs. In: : . Paper presented at 27th International Conference on Robotics in Alpe-Adria-Danube Region, Patras, Greece, June 6-8, 2018 (pp. 173-180). Springer, 67
Open this publication in new window or tab >>Towards Autonomous Surveying of Underground Mine using MAVs
2019 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Micro Aerial Vehicles (MAVs) are platforms that received great attention during the last decade. Recently, the mining industry has been considering the usage of aerial autonomous platforms in their processes. This article initially investigates potential application scenarios for this technology in mining. Moreover, one of the main tasks refer to surveillance and maintenance of infrastructure assets. Employing these robots for underground surveillance processes of areas like shafts, tunnels or large voids after blasting, requires among others the development of elaborate navigation modules. This paper proposes a method to assist the navigation capabilities of MAVs in challenging mine environments, like tunnels and vertical shafts. The proposed method considers the use of Potential Fields method, tailored to implement a sense-and-avoid system using a minimal ultrasound-based sensory system. Simulation results demonstrate the effectiveness of the proposed strategy.

Place, publisher, year, edition, pages
Springer, 2019
Series
Mechanisms and Machine Science, ISSN 2211-0984
Keywords
MAV, Underground Mines, Navigation
National Category
Engineering and Technology Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-70113 (URN)10.1007/978-3-030-00232-9_18 (DOI)000465020800018 ()2-s2.0-85054305469 (Scopus ID)
Conference
27th International Conference on Robotics in Alpe-Adria-Danube Region, Patras, Greece, June 6-8, 2018
Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2020-04-28Bibliographically 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
Show others...
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: 2020-05-14Bibliographically approved
Mansouri, S. S., Karvelis, P., Kanellakis, C., Koval, A. & Nikolakopoulos, G. (2019). Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks. In: IECON 2019: 45th Annual Conference of the IEEE Industrial Electronics Society. Paper presented at IEEE 45th Annual Conference of the Industrial Electronics Society (IECON 2019), 14-17 October, 2019, Lisbon, Portugal (pp. 192-197). IEEE
Open this publication in new window or tab >>Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks
Show others...
2019 (English)In: IECON 2019: 45th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2019, p. 192-197Conference paper, Published paper (Other academic)
Abstract [en]

This article proposes a novel visual framework for detecting tunnel crossings/junctions in underground mine areas towards the autonomous navigation of Micro Aeril Vehicles (MAVs). Usually mine environments have complex geometries, including multiple crossings with different tunnels that challenge the autonomous planning of aerial robots. Towards the envisioned scenario of autonomous or semi-autonomous deployment of MAVs with limited Line-of-Sight in subterranean environments, the proposed module acknowledges the existence of junctions by providing crucial information to the autonomy and planning layers of the aerial vehicle. The capability for a junction detection is necessary in the majority of mission scenarios, including unknown area exploration, known area inspection and robot homing missions. The proposed novel method has the ability to feed the image stream from the vehicles’ on-board forward facing camera in a Convolutional Neural Network (CNN) classification architecture, expressed in four categories: 1) left junction, 2) right junction, 3) left & right junction, and 4) no junction in the local vicinity of the vehicle. The core contribution stems for the incorporation of AlexNet in a transfer learning scheme for detecting multiple branches in a subterranean environment. The validity of the proposed method has been validated through multiple data-sets collected from real underground environments, demonstrating the performance and merits of the proposed module.

Place, publisher, year, edition, pages
IEEE, 2019
Series
Annual Conference of the Industrial Electronics Society, ISSN 1553-572X, E-ISSN 2577-1647
Keywords
Visual junction Detection, Convolutional Neural Network, Subterranean Autonomous Navigation, MAVs
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-75555 (URN)10.1109/IECON.2019.8926916 (DOI)000522050600027 ()2-s2.0-85084124174 (Scopus ID)
Conference
IEEE 45th Annual Conference of the 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: 2019-08-16 Created: 2019-08-16 Last updated: 2020-05-14Bibliographically approved
Mansouri, S. S., Kanellakis, C., Georgoulas, G., Kominiak, D., Gustafsson, T. & Nikolakopoulos, G. (2018). 2D visual area coverage and path planning coupled with camera footprints. Control Engineering Practice, 75, 1-16
Open this publication in new window or tab >>2D visual area coverage and path planning coupled with camera footprints
Show others...
2018 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 75, p. 1-16Article in journal (Refereed) Published
Abstract [en]

Unmanned Aerial Vehicles (UAVs) equipped with visual sensors are widely used in area coverage missions. Guaranteeing full coverage coupled with camera footprint is one of the most challenging tasks, thus, in the presented novel approach a coverage path planner for the inspection of 2D areas is established, a 3 Degree of Freedom (DoF) camera movement is considered and the shortest path from the taking off to the landing station is generated, while covering the target area. The proposed scheme requires a priori information about the boundaries of the target area and generates the paths in an offline process. The efficacy and the overall performance of the proposed method has been experimentally evaluated in multiple indoor inspection experiments with convex and non convex areas. Furthermore, the image streams collected during the coverage tasks were post-processed using image stitching for obtaining a single overview of the covered scene.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-68057 (URN)10.1016/j.conengprac.2018.03.011 (DOI)000433648100001 ()2-s2.0-85044107984 (Scopus ID)
Projects
Collaborative Aerial Robotic Workers, AEROWORKS
Funder
EU, Horizon 2020, 644128
Note

Validerad;2018;Nivå 2;2018-03-26 (andbra)

Available from: 2018-03-26 Created: 2018-03-26 Last updated: 2018-08-09Bibliographically approved
Kanellakis, C., Mansouri, S. S., Fresk, E., Kominiak, D. & Nikolakopoulos, G. (2018). Cooperative UAVs as a Tool for Aerial Inspection of Large Scale Aging Infrastructure. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),Madrid, Spain,1-5 Oct. 2018 (pp. 5040-5040). Piscataway, NJ: IEEE
Open this publication in new window or tab >>Cooperative UAVs as a Tool for Aerial Inspection of Large Scale Aging Infrastructure
Show others...
2018 (English)In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Piscataway, NJ: IEEE, 2018, p. 5040-5040Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

This work presents an aerial tool towards the autonomous cooperative coverage and inspection of a large scale 3D infrastructure using multiple Unmanned Aerial Vehicles (UAVs). In the presented approach the UAVs are relying only on their onboard computer and sensory system, deployed for inspection of the 3D structure. In this application each agent covers a different part of the scene autonomously, while avoiding collisions. The autonomous navigation of each platform on the designed path is enabled by the localization system that fuses Ultra Wideband with inertial measurements through an Error- State Kalman Filter. The visual information collected from the aerial team is collaboratively processed to create the 3D model. The performance of the overall setup has been experimentally evaluated in realistic wind turbine inspection experiments, providing dense 3D reconstruction of the inspected structures.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Robotics Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-72850 (URN)10.1109/IROS.2018.8593996 (DOI)000458872704097 ()978-1-5386-8095-7 (ISBN)978-1-5386-8094-0 (ISBN)978-1-5386-8093-3 (ISBN)
Conference
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),Madrid, Spain,1-5 Oct. 2018
Funder
EU, Horizon 2020
Note

abstarct + video

Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-03-27Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7631-002x

Search in DiVA

Show all publications