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Task sequencing for autonomous robotic vacuum cleaners

AIP Conference Proceedings 2017年第1期1863卷

作者： Anna Gorbenko Vladimir Popov Ural Federal University Department of Intelligent Systems and Robotics Mathematics and Computer Science Institute Lenin st. 51 620083 Ekaterinburg Russian Federation

Various planning problems for robotic systems are of considerable interest. One of such problems is the problem of task sequencing. In this paper, we consider the problem of task sequencing for autonomous vacuum floor cleaning robots. We consider a graph model for the problem. We propose an efficient approach to solve the problem. In particular, we use an explicit reduction from the decision version of the problem to the satisfiability problem. We present the results of computational experiments for different satisfiability algorithms.

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An efficient algorithm for integrated task sequencing and path planning for robotic remote laser welding

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AIP Conference Proceedings 2017年第1期1863卷

Different planning problems for robotic remote laser welding are of considerable interest. In this paper, we consider the problem of integrated task sequencing and path planning for robotic remote laser welding. We propose an efficient approach to solve the problem. In particular, we consider an explicit reduction from the decision version of the problem to the satisfiability problem. We present the results of computational experiments for different satisfiability algorithms.

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Tongue interface based on surface EMG signals of suprahyoid muscles

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ROBOMECH Journal 2016年第1期3卷 1-11页

作者： Sasaki, Makoto Onishi, Kohei Stefanov, Dimitar Kamata, Katsuhiro Nakayama, Atsushi Yoshikawa, Masahiro Obinata, Goro Graduate School of Engineering Iwate University MoriokaIwate Japan School of Science and Technology Middlesex University London United Kingdom Pattern Art Laboratory Co. Ltd. HanamakiIwate Japan Department of Intelligent Systems Engineering Ichinoseki National College of Technology IchinosekiIwate Japan Graduate School of Information Science Nara Institute of Science and Technology Nara Japan Department of Robotics Science and Engineering Chubu University Kasugai Japan

The research described herein was undertaken to develop and test a novel tongue interface based on classification of tongue motions from the surface electromyography (EMG) signals of the suprahyoid muscles detected at the underside of the jaw. The EMG signals are measured via 22 active surface electrodes mounted onto a special flexible boomerang-shaped base. Because of the sensor’s shape and flexibility, it can adapt to the underjaw skin contour. Tongue motion classification was achieved using a support vector machine (SVM) algorithm for pattern recognition where the root mean square (RMS) features and cepstrum coefficients (CC) features of the EMG signals were analyzed. The effectiveness of the approach was verified with a test for the classification of six tongue motions conducted with a group of five healthy adult volunteer subjects who had normal motor tongue functions. Results showed that the system classified all six tongue motions with high accuracy of 95.1 ± 1.9 %. The proposed method for control of assistive devices was evaluated using a test in which a computer simulation model of an electric wheelchair was controlled using six tongue motions. This interface system, which weighs only 13.6 g and which has a simple appearance, requires no installation of any sensor into the mouth cavity. Therefore, it does not hinder user activities such as swallowing, chewing, or talking. The number of tongue motions is sufficient for the control of most assistive devices. © 2016, Sasaki et al.

关键词： Support vector machines

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Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data

Social activity recognition based on probabilistic merging o...

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IeeE/RSJ International Conference on intelligent Robots and systems

作者： Claudio Coppola Diego R. Faria Urbano Nunes Nicola Bellotto L-CAS School of Computer Science University of Lincoln (UK) Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal

ISBN: (纸本)9781509037636

Social activity based on body motion is a key feature for non-verbal and physical behavior defined as function for communicative signal and social interaction between individuals. Social activity recognition is important to study human-human communication and also human-robot interaction. Based on that, this research has threefold goals: (1) recognition of social behavior (e.g. human-human interaction) using a probabilistic approach that merges spatio-temporal features from individual bodies and social features from the relationship between two individuals;(2) learn priors based on physical proximity between individuals during an interaction using proxemics theory to feed a probabilistic ensemble of activity classifiers;and (3) provide a public dataset with RGB-D data of social daily activities including risk situations useful to test approaches for assisted living, since this type of dataset is still missing. Results show that using the proposed approach designed to merge features with different semantics and proximity priors improves the classification performance in terms of precision, recall and accuracy when compared with other approaches that employ alternative strategies.

关键词： Feature extraction Skeleton Mixture models Robots Covariance matrices Probabilistic logic

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Unsupervised learning using the tensor voting graph 5

Unsupervised learning using the tensor voting graph

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5th International Conference on Scale Space and Variational Methods in computer Vision, SSVM 2015

作者： Deutsch, Shay Medioni, Gérard Computer Science Department Institute for Robotics and Intelligent Systems University of Southern California Los Angeles United States

ISBN: (纸本)9783319184609

Tensor Voting is a local, non parametric method that provides an efficient way to learn the complex geometric manifold structure under a significant amount of outlier noise. The main limitation of the Tensor Voting framework is that it is strictly a local method, thus not efficient to infer the global properties of complex manifolds. We therefore suggest constructing a unique graph which we call the Tensor Voting Graph, in which the affinity is based on the contribution of neighboring points to a point local tangent space estimated by Tensor Voting. The Tensor Voting Graph compactly and effectively represents the global structure of the underlying manifold. We experimentally demonstrate that we can accurately estimate the geodesic distance on complex manifolds, and substantially outperform all state of the art competing approaches, especially when outliers are present. We also demonstrate our method’s superior ability to segment manifolds, first on synthetic data, then on standard data sets for a motion segmentation, with graceful degradation in the presence of noise. © IFIP International Federation for Information Processing 2015.

关键词： Tensors

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Large bridge cable tension estimation with Cyber vibration Sensing System

Large bridge cable tension estimation with Cyber vibration S...

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IeeE International Conference on Cyber Technology in Automation, Control, and intelligent systems

作者： Shaodong Zhan Meng Chen Zhi Li Winston Sun Wen J. Li Guanglie Zhang Shenzhen Academy of Robotics Institute of Intelligent Cyber Sensing Systems China Department of Mechanical and Biomedical Engineering City University of Hong Kong Hong Kong SAR College of Computer Science & Software Engineering Shenzhen University China

ISBN: (纸本)9781509027347

We present in this paper our development of a cyber physical sensing platform for monitoring the structural tension of large bridge cables using MEMS vibration sensors and proprietary wireless technology. In order to implement the online tension analysis of the bridge cables, we developed an improved technology for the general identification of cable tension using the Gauss-Newton method to determine the eigenfrequency order and bending stiffness. The improved identification method was applied to vertical suspender cables, i.e., more commonly known as “hangers”, of the south section of the Huangpu Bridge. The result shows that the proposed method can effectively estimate the cable tension of the bridge's cables of different lengths.

关键词： Bridges Vibrations Sensors Force Bridge circuits Monitoring Acceleration

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Learning multiple collaborative tasks with a mixture of Interaction Primitives

Learning multiple collaborative tasks with a mixture of Inte...

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2015 IeeE International Conference on robotics and Automation, ICRA 2015

作者： Ewerton, Marco Neumann, Gerhard Lioutikov, Rudolf Amor, Heni Ben Peters, Jan Maeda, Guilherme Intelligent Autonomous Systems Lab Department of Computer Science Technische Universität Darmstadt Hochschulstr. 10 Darmstadt64289 Germany Institute of Robotics and Intelligent Machines Georgia Institute of Technology 801 Atlantic Drive AtlantaGA30332-0280 United States Max Planck Institute for Intelligent Systems Spemannstr. 38 Tuebingen72076 Germany

ISBN: (纸本)9781479969234

Robots that interact with humans must learn to not only adapt to different human partners but also to new interactions. Such a form of learning can be achieved by demonstrations and imitation. A recently introduced method to learn interactions from demonstrations is the framework of Interaction Primitives. While this framework is limited to represent and generalize a single interaction pattern, in practice, interactions between a human and a robot can consist of many different patterns. To overcome this limitation this paper proposes a Mixture of Interaction Primitives to learn multiple interaction patterns from unlabeled demonstrations. Specifically the proposed method uses Gaussian Mixture Models of Interaction Primitives to model nonlinear correlations between the movements of the different agents. We validate our algorithm with two experiments involving interactive tasks between a human and a lightweight robotic arm. In the first, we compare our proposed method with conventional Interaction Primitives in a toy problem scenario where the robot and the human are not linearly correlated. In the second, we present a proof-of-concept experiment where the robot assists a human in assembling a box. © 2015 IeeE.

关键词： Demonstrations

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Movement primitives with multiple phase parameters

Movement primitives with multiple phase parameters

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IeeE International Conference on robotics and Automation (ICRA)

作者： Marco Ewerton Guilherme Maeda Gerhard Neumann Viktor Kisner Gerrit Kollegger Josef Wiemeyer Jan Peters department of Computer Science Technische Universität Darmstadt Darmstadt Germany Fachgebiet Regelungstechnik und Mechatronik Technische Universitat Darmstadt Darmstadt Germany Institut für Sportwissenschaft Technische Universität Darmstadc Germany Max Planck Institute for Intelligent Systems Tuebingen Germany

ISBN: (纸本)9781467380270

Movement primitives are concise movement representations that can be learned from human demonstrations, support generalization to novel situations and modulate the speed of execution of movements. The speed modulation mechanisms proposed so far are limited though, allowing only for uniform speed modulation or coupling changes in speed to local measurements of forces, torques or other quantities. Those approaches are not enough when dealing with general velocity constraints. We present a movement primitive formulation that can be used to non-uniformly adapt the speed of execution of a movement in order to satisfy a given constraint, while maintaining similarity in shape to the original trajectory. We present results using a 4-DoF robot arm in a minigolf setup.

关键词： Shape Trajectory Robots Acceleration Learning (artificial intelligence) Modulation computer science

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Knowledge Engineering with Big Data

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IeeE intelligent systems 2015年第5期30卷 46-55页

作者： Wu, Xindong Chen, Huanhuan Wu, Gongqing Liu, Jun Zheng, Qinghua He, Xiaofeng Zhou, Aoying Zhao, Zhong-Qiu Wei, Bifang Gao, Ming Li, Yang Zhang, Qiping Zhang, Shichao Lu, Ruqian Zheng, Nanning School of Computer Science and Information Engineering Hefei University of Technology China University of Vermont United States University of Edinburgh School of Computer Science United Kingdom School of Computer Science University of Science and Technology of China China Department of Computer Science Xi'An Jiaotong University China Institute for Data Science and Engineering East China Normal University China Pennsylvania State University United States Fudan University China Department of Computer Science Zhejiang Gongshang University China Deakin University Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University China Keio University Japan

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation. © 2001-2011 IeeE.

关键词： intelligent systems

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Corrigendum to “Road detection algorithm for Autonomous Navigation systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

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robotics and Autonomous systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

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