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Mansouri, Sina SharifORCID iD iconorcid.org/0000-0001-7631-002x
Publications (10 of 16) Show all publications
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
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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: 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
Kanellakis, C., Mansouri, S. S., Georgoulas, G. & Nikolakopoulos, G. (2019). Towards Autonomous Surveying of Underground Mine using MAVs geogeo. 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 geogeo
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; 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: 2019-05-02Bibliographically 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: : . Paper presented at IEEE Industrial Electronics Society.
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)Conference 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.

Keywords
Mining Aerial Robotics, Deep Learning for Navigation, MAV
National Category
Robotics
Identifiers
urn:nbn:se:ltu:diva-75674 (URN)
Conference
IEEE Industrial Electronics Society
Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-11-29
Mansouri, S. S., Karvelis, P., Kanellakis, C., Koval, A. & Nikolakopoulos, G. (2019). Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks. In: : . Paper presented at IEEE 45th Annual Conference of the Industrial Electronics Society (IECON 2019).
Open this publication in new window or tab >>Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks
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2019 (English)Conference paper, Published paper (Refereed)
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.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:ltu:diva-75555 (URN)
Conference
IEEE 45th Annual Conference of the Industrial Electronics Society (IECON 2019)
Available from: 2019-08-16 Created: 2019-08-16 Last updated: 2019-11-29
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
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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
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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
Mansouri, S. S. (2018). On Visual Area Coverage Using Micro Aerial Vehicles. (Licentiate dissertation). Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>On Visual Area Coverage Using Micro Aerial Vehicles
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The aim of this Licentiate is to advance the field of cooperative visual coverage path planners for multiple Micro Aerial Vehicles (MAVs), while aiming for their real life adoption towards the tasks of aerial infrastructure inspection. The fields that will be addressed are focusing in: a) the collaborative perception of the environment, b) the collaborative visual inspection, and c) the optimization of the aerial missions based on the remaining flying battery, camera constraints, coverage constraints and other real life mission induced constraints.

Towards this envisioned aim, this Licentiate will present the following main theoretical contributions: a) centralized and distributed Model Predictive Control (MPC) schemes for the cooperative motion control of MAVs focusing in the establishing of a formation control architecture to enable a dynamic visual sensor from monocular cameras towards a reconfigurable environmental perception, b) revisiting the Cooperative Coverage Path Planning (C-CPP) problem for the inspection of complex infrastructures, c) developing a holistic approach to the problems of 2-D area coverage with MAVs for polygon areas, while considering the camera footprint, and d) designing of a scheme to estimate the Remaining Useful Life (RUL) of the battery during a flight mission, a fact that directly effects the flying capabilities of the MAVs. The theoretical contributions of this thesis have been extensively evaluated in simulation and real life large scale field trials, a direction that adds another contribution of the suggested framework towards the massive insertion of the aerial platforms as aerial tools in the close future.

In the first part of this Licentiate, the vision, motivation, open challenges, contributions, and future works are discussed, while in the second part the full articles connected to the presented contributions in this Licentiate are presented in the annex.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2018
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-68666 (URN)978-91-7790-140-2 (ISBN)978-91-7790-141-9 (ISBN)
Presentation
2018-06-15, A1547, Luleå tekniska universitet, Luleå, 13:00 (English)
Opponent
Supervisors
Available from: 2018-05-07 Created: 2018-05-07 Last updated: 2018-06-08Bibliographically approved
Karvelis, P., Röijezon, U., Faleij, R., Georgoulas, G., Mansouri, S. S. & Nikolakopoulos, G. (2017). A Laser Dot Tracking Method for the Assessment of Sensorimotor Function of the Hand. In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017: . Paper presented at 2017 25th Mediterranean Conference on Control and Automation (MED), Valletta, Malta, July 3-6, 2017 (pp. 217-222). Piscataway. NJ: Institute of Electrical and Electronics Engineers (IEEE), Article ID 7984121.
Open this publication in new window or tab >>A Laser Dot Tracking Method for the Assessment of Sensorimotor Function of the Hand
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2017 (English)In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway. NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 217-222, article id 7984121Conference paper, Published paper (Refereed)
Abstract [en]

Assessment of sensorimotor function is crucial during the rehabilitation process of various physical disorders, including impairments of the hand. While moment performance can be accurately assessed in movement science laboratories involving highly specialized personnel and facilities there is a lack of feasible objective methods for the general clinic. This paper describes a novel approach to sensorimotor assessment using an intuitive test and a specifically tailored image processing pipeline for the quantification of the test. More specifically the test relies on the patient being instructed on following a zig-zag pattern using a handled laser pointer. The movement of the pointer is tracked using image processing algorithm capable of automating the whole procedure. The method has potential for feasible objective clinical assessment of the hand and other body parts

Place, publisher, year, edition, pages
Piscataway. NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Mediterranean Conference on Control and Automation, ISSN 2325-369X
National Category
Signal Processing Medical and Health Sciences Other Health Sciences
Research subject
Signal Processing; Health Science
Identifiers
urn:nbn:se:ltu:diva-64955 (URN)10.1109/MED.2017.7984121 (DOI)000426926300036 ()2-s2.0-85028511995 (Scopus ID)9781509045334 (ISBN)
Conference
2017 25th Mediterranean Conference on Control and Automation (MED), Valletta, Malta, July 3-6, 2017
Available from: 2017-08-04 Created: 2017-08-04 Last updated: 2018-04-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7631-002x

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