{"title":"Enabling the Continuous Evolution of Ontologies for Ontology-Based Data Management","authors":"André Pomp, Johannes Lipp, Tobias Meisen","doi":"10.35708/tai1868-126244","DOIUrl":"https://doi.org/10.35708/tai1868-126244","url":null,"abstract":"","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130284516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For table tennis robots, it is a significant challenge to understand the opponent's movements and return the ball accordingly with high performance. One has to cope with various ball speeds and spins resulting from different stroke types. In this paper, we propose a real-time 6D racket pose detection method and classify racket movements into five stroke categories with a neural network. By using two monocular cameras, we can extract the racket's contours and choose some special points as feature points in image coordinates. With the 3D geometrical information of a racket, a wide baseline stereo matching method is proposed to find the corresponding feature points and compute the 3D position and orientation of the racket by triangulation and plane fitting. Then, a Kalman filter is adopted to track the racket pose, and a multilayer perceptron (MLP) neural network is used to classify the pose movements. We conduct two experiments to evaluate the accuracy of racket pose detection and classification, in which the average error in position and orientation is around 7.8 mm and 7.2 by comparing with the ground truth from a KUKA robot. The classification accuracy is 98%, the same as the human pose estimation method with Convolutional Pose Machines (CPMs).
{"title":"Real-time 6D Racket Pose Estimation and Classification\u0000for Table Tennis Robots","authors":"Yapeng Gao","doi":"10.35708/rc1868-126249","DOIUrl":"https://doi.org/10.35708/rc1868-126249","url":null,"abstract":"For table tennis robots, it is a significant challenge to understand the opponent's movements and return the ball accordingly with\u0000high performance. One has to cope with various ball speeds and spins\u0000resulting from different stroke types. In this paper, we propose a real-time\u00006D racket pose detection method and classify racket movements into five\u0000stroke categories with a neural network. By using two monocular cameras, we can extract the racket's contours and choose some special points\u0000as feature points in image coordinates. With the 3D geometrical information of a racket, a wide baseline stereo matching method is proposed\u0000to find the corresponding feature points and compute the 3D position\u0000and orientation of the racket by triangulation and plane fitting. Then, a\u0000Kalman filter is adopted to track the racket pose, and a multilayer perceptron (MLP) neural network is used to classify the pose movements.\u0000We conduct two experiments to evaluate the accuracy of racket pose\u0000detection and classification, in which the average error in position and\u0000orientation is around 7.8 mm and 7.2 by comparing with the ground\u0000truth from a KUKA robot. The classification accuracy is 98%, the same\u0000as the human pose estimation method with Convolutional Pose Machines\u0000(CPMs).","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125058553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes a framework for precise self-localization using 2D radar scan matching based on a digitalized map. For this purpose, radars, odometers, a gyroscope and a global digital map are combined. Basically estimated ego-motion using motion sensors is improved using a novel scan matching approach in order to attain globally corrected self-localization results. The matching process is based on map information, iterative optimization using the Gauß-Helmert-Model and two novel weighting methods to register the environment map using radar information. This approach focuses on self-localization in a global frame without using Global Navigation Satellite Systems (GNSS). Beside our main innovation of using native non-discretized map lines for matching we also apply two novel weighting methods to cope with noisy radar scans for matching problem. By applying the Gauß-Helmert-Model we also consider the individual measurement uncertainties to make the approach even more robust against noisy data. Using map lines and data points categorizes our approach in the point-to-feature scan matching family. The performance and usability of the proposed approach is evaluated in real-world experiments and compared qualitatively and quantitatively to the state of the art matching methods. The results illustrate an improvement in precision and computational demand compared to other point cloud based matching methods.
{"title":"Automotive Radar-based Self Localization Using\u0000Navigation Maps for Autonomous Driving","authors":"Ahmad Pishehvari","doi":"10.35708/rc1868-126251","DOIUrl":"https://doi.org/10.35708/rc1868-126251","url":null,"abstract":"This paper describes a framework for precise self-localization\u0000using 2D radar scan matching based on a digitalized map. For this purpose, radars, odometers, a gyroscope and a global digital map are combined. Basically estimated ego-motion using motion sensors is improved\u0000using a novel scan matching approach in order to attain globally corrected self-localization results. The matching process is based on map\u0000information, iterative optimization using the Gauß-Helmert-Model and\u0000two novel weighting methods to register the environment map using radar\u0000information. This approach focuses on self-localization in a global frame\u0000without using Global Navigation Satellite Systems (GNSS).\u0000Beside our main innovation of using native non-discretized map lines for\u0000matching we also apply two novel weighting methods to cope with noisy\u0000radar scans for matching problem. By applying the Gauß-Helmert-Model\u0000we also consider the individual measurement uncertainties to make the\u0000approach even more robust against noisy data. Using map lines and data\u0000points categorizes our approach in the point-to-feature scan matching\u0000family. The performance and usability of the proposed approach is evaluated in real-world experiments and compared qualitatively and quantitatively to the state of the art matching methods.\u0000The results illustrate an improvement in precision and computational\u0000demand compared to other point cloud based matching methods.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128616707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We discuss in this paper some aspects of NKRL, the Narrative Knowledge Representation Language. This is a high-level n-ary conceptual tool specially conceived for the representation and management of real world, dynamically characterized entities like situations, events and complex events, actions (e.g., in a robotics context) scripts/scenarios/narratives etc. After having pointed out some shortcomings of the standard ontological solutions for dealing with this sort of information, and having recalled some general characteristics of NKRL (like the addition of an "ontology of events" to the usual "ontology of objects"), we focus on the rules/inferential aspects proper to this language. We introduce, then, the general, formal model of "rule" used in an NKRL context and we show how this can be appropriately adapted to the setup of advanced types of inference operations based, e.g., on "analogical" and "causal" reasoning.
{"title":"Advanced Knowledge Representation and Reasoning Making Use of an Advanced N-ary Model","authors":"G. P. Zarri","doi":"10.35708/rc1868-126248","DOIUrl":"https://doi.org/10.35708/rc1868-126248","url":null,"abstract":"We discuss in this paper some aspects of NKRL, the Narrative Knowledge Representation Language. This is a high-level n-ary conceptual tool specially conceived for the representation and management of real world, dynamically characterized entities like situations, events and complex events, actions (e.g., in a robotics context) scripts/scenarios/narratives etc. After having pointed out some shortcomings of the standard ontological solutions for dealing with this sort of information, and having recalled some general characteristics of NKRL (like the addition of an \"ontology of events\" to the usual \"ontology of objects\"), we focus on the rules/inferential aspects proper to this language. We introduce, then, the general, formal model of \"rule\" used in an NKRL context and we show how this can be appropriately adapted to the setup of advanced\u0000types of inference operations based, e.g., on \"analogical\" and \"causal\" reasoning.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133789331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. C. M. W. Schwartz, Lars-Peter Ellekilde, N. Krüger
Object aligning and holding fixtures for robotic assembly tasks are important in industry in order to successfully complete an assembly. However, the designing of a fixture is usually done manually which can be a long and tedious process including many iterations, even for experienced engineers. This paper presents a method to design fixtures automatically for use in robotic assemblies and pick-and-place tasks. To achieve this, a new automated method to design the cut-out for a fixture is introduced. The method uses a parameterized version of the object's imprint to design the cut-out. The fixtures generated using this method are optimized in simulations to determine their final parameters for a specific application. Dynamic simulations are used to evaluate each iteration of the cut-out. Lastly, the method is applied to a use-case from the industry to design a fixture for use in a robotic assembly task.
{"title":"Automated Fixture Design using an Imprint-based Design Approach and Optimisation in Simulation","authors":"L. C. M. W. Schwartz, Lars-Peter Ellekilde, N. Krüger","doi":"10.35708/RC1868-126250","DOIUrl":"https://doi.org/10.35708/RC1868-126250","url":null,"abstract":"Object aligning and holding fixtures for robotic assembly tasks are important in industry in order to successfully complete an assembly. However, the designing of a fixture is usually done manually which can be a long and tedious process including many iterations, even for experienced engineers. This paper presents a method to design fixtures automatically for use in robotic assemblies and pick-and-place tasks. To achieve this, a new automated method to design the cut-out for a fixture is introduced. The method uses a parameterized version of the object's imprint to design the cut-out. The fixtures generated using this method are optimized in simulations to determine their final parameters for a specific application. Dynamic simulations are used to evaluate each iteration of the cut-out. Lastly, the method is applied to a use-case from the industry to design a fixture for use in a robotic assembly task.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"89 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113985691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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