the proceedings contain 39 papers. the topics discussed include: EEG motion artifacts removal for robotic motion control using brain computer interface;encoding topology information for deep reinforcement learning wit...
ISBN:
(纸本)9781728147406
the proceedings contain 39 papers. the topics discussed include: EEG motion artifacts removal for robotic motion control using brain computer interface;encoding topology information for deep reinforcement learning with continuous action space;an experimental ferromagnetic wall climbing and paint fixing robot control by an upper computer with color discrimination and its development prospect;method combining machine vision and machine learning for reed positioning in automatic aerophone manufacturing;automatic GTAW robot arc length control based on particle filter;comparative assessment of reinforcement learning algorithms in the task of robotic manipulation of deformable linear objects;automation of quality control measurement of deformable object formed by translation of closed planar curve along an elliptical path;and aerodynamic investigation on a novel paper-folding micro annular aerial robot.
this article presents a solution developed with Doosan robotics and Onrobot for identifying missing or broken pull tabs in manufacturing processes using computervision and robotics integration. the solution involves ...
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Social robot acts based on any perceived information to interact and communicate with humans or other robots. One essential type of information is visual-based information captured by camera. Visual data from camera i...
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In this research, we developed an AI autonomous mobile robot based on open-source software. this robot is capable of voice recognition and object recognition, and withthese functions, the robot can find and approach ...
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As the development of the computervision, we could employ the facial expressions recognition technique in the human-computer interaction domain, such as chatting robot, psychological counselling robot and so on. In t...
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this paper proposes a novel framework for vision based door traversal that contributes to the ultimate goal of purely vision based mobile robot navigation. the door detection, door tracking and door traversal is accom...
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ISBN:
(纸本)9789898111692
this paper proposes a novel framework for vision based door traversal that contributes to the ultimate goal of purely vision based mobile robot navigation. the door detection, door tracking and door traversal is accomplished by processing onmidirectional images. In door detection candidate line segments detected in the image are grouped and matched with prototypical door patterns. In door localisation and tracking a Kalman filter aggregates the visual information withthe robots odometry. Door traversal is accomplished by a 2D visual servoing approach. the feasibility and robustness of the scheme are confirmed and validated in several robotic experiments in an office environment.
In this paper, we have developed a visual tracking control system on a purposely built movable robot head mounted on a Baxter robot. the tracking control algorithm used a stereo vision system provided by a Bumblebee c...
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In this paper, we have developed a visual tracking control system on a purposely built movable robot head mounted on a Baxter robot. the tracking control algorithm used a stereo vision system provided by a Bumblebee camera, facilitated by the MATLAB computervision toolbox. the objective of this work is to enable a robot to move its head as in a similar manner as our humans beings to focus vision on a moving object. Identification of object and estimation of its coordinate are performed based on image processing techniques. A fuzzy logic technique is applied to control the moving head in order to bring human like motion into the robot. the method developed in this work can be extended to any other movable robot head platform. Extensive experimental studies have been performed to test the effectiveness and efficiency of the proposed method. (C) 2016, IFAC (International Federation of Antomatic Control) Hosting by Elsevier Ltd. All rights reserved.
In the transition from industrial to service robotics, robots will have to deal with increasingly unpredictable and variable environments. We present a system that is able to recognize objects of a certain class in an...
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In the transition from industrial to service robotics, robots will have to deal with increasingly unpredictable and variable environments. We present a system that is able to recognize objects of a certain class in an image and to identify their parts for potential interactions. the method can recognize objects from arbitrary viewpoints and generalizes to instances that have never been observed during training, even if they are partially occluded and appear against cluttered backgrounds. Our approach builds on the implicit shape model of Leibe et al. We extend it to couple recognition to the provision of meta-data useful for a task and to the case of multiple viewpoints by integrating it withthe dense multi-view correspondence finder of Ferrari et al. Meta-data can be part labels but also depth estimates, information on material types, or any other pixelwise annotation. We present experimental results on wheelchairs, cars, and motorbikes.
In manufacturing industry there is a need for an adaptable automated visual inspection (AVI) system that can be used for different inspection tasks under different operation condition without requiring excessive retun...
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ISBN:
(纸本)9781424442119
In manufacturing industry there is a need for an adaptable automated visual inspection (AVI) system that can be used for different inspection tasks under different operation condition without requiring excessive retuning or retraining. this paper proposes an adaptable AVI scheme using an efficient and effective online learning approach. the AVI scheme uses a novel inspection model that consists of the two sub-models for localization and verification. In the AVI scheme, the region localization module is implemented by using a template-matching technique to locate the subject to be inspected based on the localization sub-mode. the defect detection module is realized by using the representative features obtained from the feature extraction module and executing the verification sub-model built in the model training module. A support vector machine (SVM) based online learning algorithm is proposed for training and updating the verification sub-model. In the case studies, the adaptable A VI scheme demonstrated its promising performances with respect to the training efficiency and inspection accuracy. the expected outcome of this research will be beneficial to the manufacturing industry.
Automatic sign language recognition is an open problem that has received a lot of attention recently, not only because of its usefulness to signers, but also due to the numerous applications a sign classifier can have...
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ISBN:
(纸本)9781479943388
Automatic sign language recognition is an open problem that has received a lot of attention recently, not only because of its usefulness to signers, but also due to the numerous applications a sign classifier can have. In this article, we present a new feature extraction technique for hand pose recognition using depth and intensity images captured from a Microsoft Kinect(TM) sensor. We applied our technique to American Sign Language fingerspelling classification using a Deep Belief Network, for which our feature extraction technique is tailored. We evaluated our results on a multi-user data set with two scenarios: one with all known users and one with an unseen user. We achieved 99 % recall and precision on the first, and 77 % recall and 79 % precision on the second. Our method is also capable of real-time sign classification and is adaptive to any environment or lightning intensity.
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