In this work a monocular machine vision based pose estimation system is developed for industrial ... more In this work a monocular machine vision based pose estimation system is developed for industrial robots and the accuracy of the estimated pose is improved via sparse regression. The proposed sparse regression based method is used improve the accuracy obtained from the Levenberg-Marquardt (LM) based pose estimation algorithm during the trajectory tracking of an industrial robot’s end effector. The proposed method utilizes a set of basis functions to sparsely identify the nonlinear relationship between the estimated pose and the true pose provided by a laser tracker. Moreover, a camera target was designed and fitted with fiducial markers, and to prevent ambiguities in pose estimation, the markers are placed in such a way to guarantee the detection of at least two distinct non parallel markers from a single camera within ± 90° in all directions of the camera’s view. The effectiveness of the proposed method is validated by an experimental study performed using a KUKA KR240 R2900 ultra r...
This paper deals with the development of a realtime structural health monitoring system for airfr... more This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with...
This paper deals with the development of a realtime structural health monitoring system for airfr... more This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with...
In this study, the functionality of an autonomous mobile robot is improved by interfacing it with... more In this study, the functionality of an autonomous mobile robot is improved by interfacing it with Microsoft Kinect for finding and tracking a previously identified human target while avoiding obstacles both while searching and tracking. A suitable platform for fixing Microsoft Kinect and a laptop on a mobile robot is designed considering simplicity and practicality. The human identification and tracking methodology and algorithm is analysed for real life scenarios. The developed algorithm is applied to the mobile robot using robotics software and evaluated through real life scenarios achieving pleasing results. The mobile robot is able to identify humans, search for them and track them while avoiding static obstacles both while searching and tracking at indoor environments where no direct sunlight is present.
In this work a monocular machine vision based pose estimation system is developed for industrial ... more In this work a monocular machine vision based pose estimation system is developed for industrial robots and the accuracy of the estimated pose is improved via sparse regression. The proposed sparse regression based method is used improve the accuracy obtained from the Levenberg-Marquardt (LM) based pose estimation algorithm during the trajectory tracking of an industrial robot’s end effector. The proposed method utilizes a set of basis functions to sparsely identify the nonlinear relationship between the estimated pose and the true pose provided by a laser tracker. Moreover, a camera target was designed and fitted with fiducial markers, and to prevent ambiguities in pose estimation, the markers are placed in such a way to guarantee the detection of at least two distinct non parallel markers from a single camera within ± 90° in all directions of the camera’s view. The effectiveness of the proposed method is validated by an experimental study performed using a KUKA KR240 R2900 ultra r...
This paper deals with the development of a realtime structural health monitoring system for airfr... more This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with...
This paper deals with the development of a realtime structural health monitoring system for airfr... more This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with...
In this study, the functionality of an autonomous mobile robot is improved by interfacing it with... more In this study, the functionality of an autonomous mobile robot is improved by interfacing it with Microsoft Kinect for finding and tracking a previously identified human target while avoiding obstacles both while searching and tracking. A suitable platform for fixing Microsoft Kinect and a laptop on a mobile robot is designed considering simplicity and practicality. The human identification and tracking methodology and algorithm is analysed for real life scenarios. The developed algorithm is applied to the mobile robot using robotics software and evaluated through real life scenarios achieving pleasing results. The mobile robot is able to identify humans, search for them and track them while avoiding static obstacles both while searching and tracking at indoor environments where no direct sunlight is present.
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