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Nikolakopoulos, GeorgeORCID iD iconorcid.org/0000-0003-0126-1897
Publications (10 of 141) Show all publications
Röijezon, U., Prellwitz, M., Ahlmark, D. I., van Deventer, J., Nikolakopoulos, G. & Hyyppä, K. (2019). A haptic navigation aid for individuals with visual impairments: Indoor and outdoor feasibility evaluations of the LaserNavigator. Journal of Visual Impairment & Blindness, 113(2), 194-201
Open this publication in new window or tab >>A haptic navigation aid for individuals with visual impairments: Indoor and outdoor feasibility evaluations of the LaserNavigator
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2019 (English)In: Journal of Visual Impairment & Blindness, ISSN 0145-482X, E-ISSN 1559-1476, Vol. 113, no 2, p. 194-201Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Sage Publications, 2019
National Category
Physiotherapy Occupational Therapy Other Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering
Research subject
Physiotherapy; Occupational therapy; Industrial Electronics; Control Engineering
Identifiers
urn:nbn:se:ltu:diva-69826 (URN)10.1177/0145482X19842491 (DOI)000489695600009 ()2-s2.0-85064590614 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-05-15 (johcin)

Available from: 2018-06-25 Created: 2018-06-25 Last updated: 2019-10-29Bibliographically approved
Jafari, H., Pauelsen, M., Röijezon, U., Nyberg, L., Nikolakopoulos, G. & Gustafsson, T. (2019). A novel data driven model of ageing postural control. In: : . Paper presented at EU Falls Festival.
Open this publication in new window or tab >>A novel data driven model of ageing postural control
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2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Background

Postural control is a complex system. Based on sensorimotor integration, the central nervous system (CNS) maintains balance by sending suitable motor commands to the muscles. Physiological decline due to ageing, affects balance performance through failing postural control – and in turn affects falls self-efficacy and activity participation. Understanding how the CNS adapts to these changes and predicts the appropriate motor commands to stabilize the body, has been a challenge for postural control research the latest years.

Aims

To understand and model the performance of the central nervous system as the controller of the human body.

Methods

Modelling was based on postural control data from 45 older adults (70 years and older). Ankle, knee and hip joint kinematics were measured during quiet stance using a motion capture system. Principal component analysis was used in order to reduce the measured multidimensional kinematics from a set of correlated discrete time series to a set of principal components. The outcome was utilized to predict the motor commands. The adaptive behaviour of the CNS was modelled by recurrent neural network including the efference copy for rapid predictions. The data from joint kinematics and electromyography (EMG) signals of the lower limb muscles were measured and separated into training and test data sets.

Results

The model can predict postural motor commands with very high accuracy regardless of a large physiological variability or balancing strategies. This model has three characteristics: a) presents an adaptive scheme to individual variability, 2) showcases the existence of an efference copy, and 3) is human experimental data driven.

Conclusion

The model can adapt to physical body characteristics and individual differences in balancing behaviour, while successfully predict motor commands. It should therefore be utilised in the continued pursuit of a better understanding of ageing postural control.

National Category
Physiotherapy Robotics
Research subject
Physiotherapy; Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-76838 (URN)
Conference
EU Falls Festival
Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-11-25
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
Kanellakis, C. & Nikolakopoulos, G. (2019). Guidance for Autonomous Aerial Manipulator Using Stereo Vision. Journal of Intelligent and Robotic Systems
Open this publication in new window or tab >>Guidance for Autonomous Aerial Manipulator Using Stereo Vision
2019 (English)In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409Article in journal (Refereed) Epub ahead of print
Abstract [en]

Combining the agility of Micro Aerial Vehicles (MAV) with the dexterity of robotic arms leads to a new era of Aerial Robotic Workers (ARW) targeting infrastructure inspection and maintenance tasks. Towards this vision, this work focuses on the autonomous guidance of the aerial end-effector to either reach or keep desired distance from areas/objects of interest. The proposed system: 1) is structured around a real-time object tracker, 2) employs stereo depth perception to extract the target location within the surrounding scene, and finally 3) generates feasible poses for both the arm and the MAV relative to the target. The performance of the proposed scheme is experimentally demonstrated in multiple scenarios of increasing complexity.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Vision based guidance, Aerial manipulator, MAV
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-75822 (URN)10.1007/s10846-019-01060-8 (DOI)
Available from: 2019-09-03 Created: 2019-09-03 Last updated: 2019-12-11
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)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: 2019-08-27Bibliographically 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
Jafari, H., Nikolakopoulos, G. & Gustafsson, T. (2019). Stabilization of an Inverted Pendulum via Human Brain Inspired Controller Design. In: : . Paper presented at IEEE-RAS International Conference on Humanoid Robots. IEEE
Open this publication in new window or tab >>Stabilization of an Inverted Pendulum via Human Brain Inspired Controller Design
2019 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2019
National Category
Engineering and Technology
Identifiers
urn:nbn:se:ltu:diva-75901 (URN)
Conference
IEEE-RAS International Conference on Humanoid Robots
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-10-22
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0126-1897

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