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2008 IEEE/RSJ International Conference on Intelligent Robots and systems, IROS

作者： Buss, Martin Peer, Angelika Schauß, Thomas Stefanov, Nikolay Unterhinninghofen, Ulrich Behrendt, Stephan Färber, Georg Leupold, Jan Diepold, Klaus Keyrouz, Fakheredine Sarkis, Michel Hinterseer, Peter Steinbach, Eckehard Färber, Berthold Pongrac, Helena Institute of Automatic Control Engineering Technische Universität München Munich Germany Institute for Real-Time Computer Systems Technische Universität München Munich Germany Institute for Data Processing Technische Universität München Munich Germany Institute of Communication Networks Technische Universität München Munich Germany Human Factors Institute University of the Bundeswehr Munich Germany

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Utilizing spatial relations for natural language access to an autonomous mobile robot 18th

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18th Annual German Conference on Artificial Intelligence, KI 1994

作者： Stopp, Eva Gapp, Klaus-Peter Herzog, Gerd Laengle, Thomas Lueth, Tim C. SFB 314-Project VITRA FB 14 Informatik Universität des Saarlandes SaarbrückenD-66041 Germany Institute for Real-Time Computer Systems and Robotics Prof. Dr.-Ing. U. Rembold and Prof. Dr.-Ing. R. Dillmann Universität Karlsruhe KarlsruheD-76128 Germany

ISBN: (纸本)9783540584674

Natural language, a primary communication medium for humans, facilitates better human-machine interaction and could be an efficient means to use intelligent robots in a more flexible manner. In this paper, we report on our joint efforts at providing natural language access to the autonomous mobile two-arm robot KAMRO. The robot is able to perform complex assembly tasks. To achieve autonomous behaviour, several camera systems are used for the perception of the environment during task execution. Since natural language utterances must be interpreted with respect to the robot’s current environment the processing must be based on a referential semantics that is perceptually anchored. Considering localization expressions, we demonstrate how, on the one hand, verbal descriptions, and on the other hand, knowledge about the physical environment, i.e., visual and geometric information, can be connected to each other. © 1994, Springer Verlag. All rights reserved.

关键词： Intelligent robots

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Microrobot-Based Microassembly Station and Its control Using a Graphical User Interface

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IFAC Proceedings Volumes 1997年第20期30卷 781-786页

作者： J. Seyfried S. Fatikow University of Karlsruhe Institute for Real-Time Computer Systems and Robotics (IPR) Kaiserstr. 12 (Geb. 40.28) D-76128 Karlsruhe F. R. of Germany Phone: +49-721-608-3656 Fax: +49-721-606740

In the fields of biology, microelectronics and microsystem technology many operations are today performed by hand which might also be done by micromanipulation robots. Several prototypes of such robots have already been built and tested, but in the field of control systems and user interfaces of such micromanipulation systems, there is still much work to be done. Different applications demand different levels of control and planning: easyier, single tasks may be performed by teleoperation, while others require a programmed motion sequence or even task planning to accomplish a complex assembly task a with minimum of human interaction. This paper presents a micromanipluation system consisting of a microassembly robot working under a light-optical microscope, a CCD camera and an XY-stage. Then, the software infrastructure for the microrobot control system is presented and the underlying hardware system is described. It also shows a user interface based on this structures, a semi-automated teleoperation system. The user interface presented here uses the live image of the CCD camera to perform either open or closed loop control of the robot's position. All parameters of the microscope and XY-stage can be controlled with dialog windows. The software infrastructure allows also the development of task planning algorithms by providing an intermediate layer to control the robot

关键词： Robots microsystems assembly teleoperation user interfaces

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Multi-stage Objective Function Optimized Hand-Eye Self-calibration of Robot in Autonomous Environment 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Liu, Kaibo Zheng, Wangli Min, Huasong Lin, Yunhan School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China State Grid Electric Power Research Institute Jiangsu Nanjing211106 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Hubei Wuhan430081 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

In the application scenario of robot autonomous tasks, the robot needs to be able to complete calibration online and automatically to achieve self-maintenance, which differs from traditional robot hand-eye calibration in that the traditional robot hand-eye calibration requires a dedicated calibration board to assist offline completion. Aiming at the problem that the existing self-calibration methods cannot be optimized as a whole, which leads to low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed, which describes the solution of hand-eye self-calibration as a minimization objective function problem involving multiple stages. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Two different types of experiments were designed to validate our method. One is a single camera dataset experiment, which shows that our method is more accurate and robust than the existing self-calibration method;the other is an application platform experiment, which verifies the feasibility and availability of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Calibration

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Efficient 2D Simulators for Deep-Reinforcement-Learning-based Training of Navigation Approaches

Efficient 2D Simulators for Deep-Reinforcement-Learning-base...

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International Conference on Ubiquitous Robots (UR)

作者： Huajian Zeng Linh Kästner Jens Lambrecht The Chair of Robotics Artificial Intelligence and Real-time Systems Technical University Munich (TUM) Germany The Chair of Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

In recent years, Deep Reinforcement Learning (DRL) has emerged as a competitive approach for mobile robot navigation. However, training DRL agents often comes at the cost of difficult and tedious training procedures in which powerful hardware is required to conduct oftentimes long training runs. Especially, for complex environments, this proves to be a major bottleneck for widespread adoption of DRL approaches into industries. In this paper we integrate an efficient 2D simulator into the Arena-Rosnav framework of our previous work as an alternative simulation platform to train and develop DRL agents. Therefore, we utilized the provided API to integrate necessary components into the ecosystem of Arena-Rosnav. We evaluated our simulator by training a DRL agent within that platform and compared the training and navigational performance against the baseline 2D simulator Flatland, which is the default simulating platform of Arena-Rosnav. Results demonstrate that using our Arena2D simulator results in substantially faster training times and in some scenarios better agents. This proves to be an important step towards resource-efficient DRL training, which accelerates training times and improve the development cycle of DRL agents for navigation tasks. We made our simulator openly available at https://***/Arena-Rosnav/arena2d.

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Development of a Cloud- and Edge-Architecture for adaptive model weight optimization of a CNN exemplified by optical detection of hairpin welding

Development of a Cloud- and Edge-Architecture for adaptive m...

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International Electric Drives Production Conference (EDPC)

作者： Johannes Vater Maximilian Kirschning Dominik Scheurenberg Dirk Abel Alois Knoll Planning and Production of Electrified Powertrains BMW Group Munich Germany Chair of Robotics Artificial Intelligence and Real-time Systems Technical University Munich Munich Germany Institute of Automatic Control RWTH Aachen University Aachen Germany

ISBN: (数字)9781728184586

ISBN: (纸本)9781728196145

The beginning of a global reorientation towards an increasingly conscientious approach to nature and the human habitat has been accompanied by changes in industry and society. The automotive industry, where a transition from combustion to electrically powered vehicles is currently underway, is also concerned with this change. In addition to increasing the capacity of the battery, improving the efficiency of the electric motor is essential. To achieve these goals, however, new technologies such as hairpins for the stator are needed. An important process step involves the welding of two pairs of hairpins, which often leads to welding defects. Nevertheless, expert knowledge in this field is limited. Optical monitoring of the welding process with the help of a convolutional neural network (CNN) is a good approach. This approach can compensate for the low level of expert knowledge and detects and classifies welding defects directly in the production line. However, the disadvantage of optical monitoring is that production conditions and the surrounding environment change over time. This has an impact on optical detection and can negatively affect the accuracy of a CNN. For example, the camera perspective can change, which has a negative effect on optical quality monitoring. Therefore, this paper presents an approach for monitoring and evaluating the quality of a CNN in a cloud instance online. If a deteriorating quality is detected, the CNN in the cloud is re-trained by continuously collected data and then automatically deployed to the production line. This allows the CNN to adapt to the changing environmental conditions. The present approach is demonstrated and validated with real data of the stator production process. Compared with the current state-of-the-art, this control loop is highly automated and requires a minimum of human intervention.

关键词： Industries Welding Production Optical computing Stators Monitoring Optimization

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Scalable and low-cost remote lab platforms: Teaching industrial robotics using open-source tools and understanding its social implications

arXiv

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arXiv 2024年

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab. Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © 2024, CC BY.

关键词： Costs

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Making Configuration Expert System Development Less Costly

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IFAC Proceedings Volumes 1992年第23期25卷 145-150页

作者： Bernd G. Welz Institute for Real-Time Computer Systems and Robotics Prof Dr.-Ing. U. Rembold and Prof. Dr.-Ing. R. Dillmann University of Karlsruhe Postfach 69 80 D-7500 Karlsruhe 1 F.R.Germany

In this paper the software environment KONTEST is introduced which allOWS a non-programmer to build and maintain a knowledge-based configuration tool for a restricted class of configuration tasks. The use of an environment like KONTEST can be especially interesting for small and medium sized companies, that could not afford a customised configuration tool developed from scratch.

关键词： Artificial Intelligence expert system tools and techniques knoweldge based configuration knowledge acquisition

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A General Gray Code Quantized Method of Binary Feature Descriptors for Fast and Efficient Keypoint Matching

A General Gray Code Quantized Method of Binary Feature Descr...

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International Conference on Intelligent Autonomous systems (ICoIAS)

作者： Yunhan Lin Yalan Sun Huasong Min College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

In this paper, a general Gray code quantized method of binary feature descriptors is proposed for fast and efficient keypoint matching on 3D point clouds. In our method, it includes 2 variable L and N. L is rule variable which can be used to set the encoding group length according to the feature of the real-valued descriptor, and N is bits variable which can be used to set the number of Gray code bits according to the actual system requirements. Be different from the exist method, such as B-SHOT, our proposed method has the advantages of reasonable and flexible. As an example, our method is applied on the feature descriptor SHOT and tested in a standard benchmark dataset, different variable combinations of L and N are tested in the Gray code quantized processes, the best combination of L and N is dubbed as GRAY-SHOT through performance comparison. At last, GRAY-SHOT is compared with the state-of-the-art binary 3D feature descriptor B-SHOT, experimental evaluation result shows that GRAY-SHOT offers better keypoint matching performances to B-SHOT on a standard benchmark dataset with a slight more memory footprint and time consumption.

关键词： Three-dimensional displays Reflective binary codes Encoding Histograms Feature extraction Standards Benchmark testing

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Residual Squeeze-and-Excitation Network with Multi-scale Spatial Pyramid Module for Fast Robotic Grasping Detection

Residual Squeeze-and-Excitation Network with Multi-scale Spa...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hu Cao Guang Chen Zhijun Li Jianjie Lin Alois Knoll Chair of Robotics Artificial Intelligence and Real-Time Systems Technische Universität München München Germany State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Tongji University Shanghai China University of Science and Technology of China China

This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.

关键词： Automation Conferences Pose estimation Grasping Manipulators Feature extraction Inference algorithms

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