| 1. |
- Adaldo, Antonio, et al.
(författare)
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Cooperative coverage for surveillance of 3D structures
- 2017
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Ingår i: IEEE International Conference on Intelligent Robots and Systems. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781538626825 ; , s. 1838-1845
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Konferensbidrag (refereegranskat)abstract
- In this article, we propose a planning algorithm for coverage of complex structures with a network of robotic sensing agents, with multi-robot surveillance missions as our main motivating application. The sensors are deployed to monitor the external surface of a 3D structure. The algorithm controls the motion of each sensor so that a measure of the collective coverage attained by the network is nondecreasing, while the sensors converge to an equilibrium configuration. A modified version of the algorithm is also provided to introduce collision avoidance properties. The effectiveness of the algorithm is demonstrated in a simulation and validated experimentally by executing the planned paths on an aerial robot.
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| 2. |
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| 3. |
- Carlbaum, Erik, et al.
(författare)
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Towards Robust Localization Deep Feature Extraction by CNN
- 2020
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Ingår i: Proceedings. - : IEEE. ; , s. 807-812
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Konferensbidrag (refereegranskat)abstract
- Robust localization is a fundamental capability to increase the autonomy levels of robotic platforms. A core processing step in vision based odometry methods is the extraction and tracking of distinctive features in the image frame. Nevertheless, when deploying robots in challenging environments like underground tunnels, the sensor measurements are noisy with lack of information due to low light conditions, introducing a bottleneck for feature detection methods. This paper proposes a deep classifier Convolutional Neural Network (CNN) architecture to retain detailed and noise tolerant feature maps from RBG images, establishing a novel feature tracking scheme in the context of localization. The proposed method is feeding the RGB image into the AlexNet or VGG-16 network and extracts a feature map at a specific layer. This feature map consists of feature points which are then paired between frames resulting in a discrete vector field of feature change. Finally, the proposed method is evaluated with RGB camera footage of the Micro Aerial Vehicle (MAV) flights in dark underground mines and the performance is compared with existing feature extraction methods, while the noise is added to the images.
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| 4. |
- Eleftheroglou, Nick, et al.
(författare)
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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
- 2019
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 254
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Eleftheroglou, Nick, et al.
(författare)
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Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries
- 2019
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Ingår i: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019. - : Prognostics and Health Management Society.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Fresk, Emil, et al.
(författare)
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Reduced complexity calibration of MEMS IMUs
- 2017
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Ingår i: 2017 25th Mediterranean Conference on Control and Automation, MED 2017. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509045334 ; , s. 1316-1320
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Konferensbidrag (refereegranskat)abstract
- In this article a reduced complexity calibration method for Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Units (IMUs) will be presented, which does not need the rotating reference tables, commonly used in the gyroscope calibration. As it will be presented, in the proposed novel scheme fixed angle rotations have been utilized to observe the integral of the gyroscope signals to find the corresponding sensitivity, axis misalignment and acceleration sensitivity matrices. This appraoch has the significant merit of high norm accuracy, easiness of use, low cost and simplicity in construction, thus allowing anyone with a basic electronics knowledge to calibrate an IMU.
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| 7. |
- Jafari, Hedyeh, et al.
(författare)
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On the Fear of Falling Detection by Moving Horizon Estimation
- 2020
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Ingår i: 21th IFAC World Congress. - : Elsevier. ; , s. 16512-16517
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Konferensbidrag (refereegranskat)abstract
- Fear of falling (FoF) is a major health problem, especially in elders, which can lead to falls, injury, loss of independence, and premature needs of nursing and assistance. However, most of the studies have focused on the psychological aspect of the FoF and there is a significant lack of technological assistance and methodology to detect and eliminate the effects of this fear on maintaining balance. In this article, we propose a novel method to detect the FoF as a quantitative signal. In our proposed novel approach, fear is considered as an internal disturbance inside a Central Nervous System (CNS) that can affect the generated output torque to each joint of the psychical body. By assuming the human body in a quiet stance, as an inverted pendulum model, this disturbance signal is estimated by Moving Horizon Estimation (MHE). For this purpose, the body kinetics and kinematics measurements of forty-five subjects during upright stance trails, as well as the psychological FoF falls efficacy test, were collected and utilized for the estimation and validation of the results. The experimental results show that the subjects with FoF present a higher variation in the estimated signal. This method can sufficiently detect the FoF by the posturographic and motion data, which can be utilized on the future assistive devices for prevention and treatment of the FoF and falls.
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| 8. |
- Kanellakis, Christoforos, et al.
(författare)
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Aerial imaging and reconstruction of infrastructures by UAVs
- 2020
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Ingår i: Imaging and Sensing for Unmanned Aircraft Systems Volume 2: Deployment and Applications. - : Institution of Engineering and Technology. - 9781785616440 - 9781785616457 ; , s. 157-176
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter presents a compilation of experimental field trials aiming vision-based reconstruction of large-scale infrastructures using micro aerial vehicles (MAVs). The main focus of this study is on the sensor selection, the data-set generation and on the computer vision algorithms for generating three-dimensional (3D) models. In general, MAVs are distinguished for their ability to fly at various speeds, to stabilise their position and to perform manoeuvres close to large-scale infrastructures. The aforementioned merits constitute aerial robots a highly paced evolving robotic platform for infrastructure inspection and maintenance tasks. Different MAV solutions with task-oriented sensory modalities can be developed to address unique tasks, such as 3D modelling of infrastructures. In this chapter, aerial agents navigate around/along different environments, while collecting visual data for post-processing using structure from motion (SfM) and multi-view stereo (MVS) techniques to generate 3D models [1, 2]. The proposed framework has been successfully experimentally demonstrated in real indoor, outdoor and subterranean environments.
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| 9. |
- Kanellakis, Christoforos, et al.
(författare)
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Cooperative UAVs as a Tool for Aerial Inspection of Large Scale Aging Infrastructure
- 2018
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Ingår i: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - Piscataway, NJ : IEEE. - 9781538680957 - 9781538680940 - 9781538680933 ; , s. 5040-5040
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 10. |
- Kanellakis, Christoforos, et al.
(författare)
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Dynamic visual sensing based on MPC controlled UAVs
- 2017
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Ingår i: 2017 25th Mediterranean Conference on Control and Automation, MED 2017. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509045334 ; , s. 1201-1206
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Konferensbidrag (refereegranskat)abstract
- This article considers the establishment of a dynamic visual sensor from monocular cameras to enable a reconfigurable environmental perception. The cameras are mounted on Micro Aerial Vehicles (MAV) which are coordinated by a Model Predictive Control (MPC) scheme to retain overlapping field of views and form a global sensor with varying baseline. The specific merits of the proposed scheme are: a) the ability to form a configurable stereo rig, according to the application needs, and b) the simple design, the reduction of the payload and the corresponding cost. Moreover, the proposed configurable sensor provides a glpobal 3D reconstruction of the surrounding area, based on a modified Structure from Motion approach. The efficiency of the suggested flexible visual sensor is demonstrated in simulation results that highlight the novel concept of cooperative flying cameras and their 3D reconstruction capabilities
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