Self-driving vehicles rely on detailed semanticmaps of the environment for operating. In this letter, we propose a method to autonomously generate such a semantic map enriched with knowledge of parking spot locations....
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Self-driving vehicles rely on detailed semanticmaps of the environment for operating. In this letter, we propose a method to autonomously generate such a semantic map enriched with knowledge of parking spot locations. Our method detects and uses parked vehicles in the surroundings to estimate parking lot topology and infer vacant parking spots via a graph-based approach. We show that our method works for parking lot structures in different environments, such as structured parking lots, unstructured/unmarked parking lots, and typical suburban environments. Using the proposed graph-based approach to infer the parking lot structure, we can extend the estimated parking spots by 57%, averaged over six different areas with ten trials each. We also show that the accuracy of our algorithm increases when combining multiple trials over multiple days. With ten trials combined, we managed to estimate the whole parking lot structure and detected all parking spots in four out of the six evaluated areas.
Root-cause Analysis (RCA) of alarms is a well-established research area in automated Production Systems (aPS). Many RCA algorithms have been proposed and successfully evaluated and new ones are being developed. Recent...
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Root-cause Analysis (RCA) of alarms is a well-established research area in automated Production Systems (aPS). Many RCA algorithms have been proposed and successfully evaluated and new ones are being developed. Recently, researchers focus on the incorporation of formalized information about the technical process in the analysis to gather further evidence for common root causes. In industrial applications, alarm data are usually preprocessed to accommodate for use case-specific properties and prepare subsequent analysis steps. Consequently, this letter proposes a generalized RCA framework, for which an arbitrary number of preprocessing, data-driven RCA, and postprocessing algorithms can be selected, to support varying use cases. The framework was successfully evaluated in an industrial case study, using 1.8 million alarms recorded over 450 days from an industrial nonwoven production plant and analyzed using formalized information from process documentation and expert interviews. Seven preprocessing algorithms, one data-driven RCA algorithm, and nine postprocessing algorithms typical for continuous and hybrid technical processes were realized in an otherwise entirely use case-agnostic implementation.
In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representati...
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ISBN:
(纸本)9781728196817
In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networks for different compression rates to generate consolidated point clouds of varying quality. In contrast, our network is capable of explicitly processing point clouds and generating a compressed description at a comprehensive range of bitrates. Furthermore, our approach ensures that there is no loss of information as a result of the voxelization process and the density of the point cloud does not affect the encoder/decoder performance. An extensive experimental evaluation shows that our model obtains state-of-the-art results, it is computationally efficient, and it can work directly with point cloud data thus avoiding an expensive voxelized representation.
Dropout strategy is a simple and common regularization method in the construction of deep network that it can control the status of units in the Dropout layers according to the constant probability values in the train...
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ISBN:
(纸本)9781728103778
Dropout strategy is a simple and common regularization method in the construction of deep network that it can control the status of units in the Dropout layers according to the constant probability values in the training processes to prevent the training from overfitting. However, the probability values of the Dropout strategy are single and decided by users, which means that we need more training iterations to receive better results and avoid less fitting problem. In this paper, two evolutionary algorithms, genetic algorithm and differential evolution algorithm are used to optimize the set probability values of network units to improve dropout strategy and they are proved to be able to increase the accuracy of the original method to about 5%.
To enable robots to work on real home environments, they have to not only consider common knowledge in the global society, but also be aware of existing rules there. Since such "local rules" are not describa...
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ISBN:
(纸本)9781538680940
To enable robots to work on real home environments, they have to not only consider common knowledge in the global society, but also be aware of existing rules there. Since such "local rules" are not describable beforehand, robot agents must acquire them through their lives after deployment. To achieve this, we developed a framework that a) lets robots record long-term episodic memories in their deployed environments, b) autonomously builds probabilistic object localization map as structurization of logged data and c) make adapted task plans based on the map. We equipped our framework on PR2 and Fetch robots operating and recording episodic memory for 41 days with semantic common knowledge of the environment. We also conducted demonstrations in which a PR2 robot tidied up a room, showing that the robot agent can successfully plan and execute local-rule-aware home assistive tasks by using our proposed framework.
Industrie 4.0 and data analytics blur the separation of operational and information technology that prevailed for industrial automation over the last decades. Decentralized control systems for production plants and ro...
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Industrie 4.0 and data analytics blur the separation of operational and information technology that prevailed for industrial automation over the last decades. Decentralized control systems for production plants and robot cells collaborate actively with higher-level systems for bigdata analytics. In parallel, the complexity of designing and operating a system architecture for data collection and analysis increases dramatically as more experts from different domains get involved. Graphical modeling notations facilitate the design process by formalizing implicit knowledge, but currently do not exist for the combined description of field layer and data analytics. Modeling the system architecture, relevant constraints, and requirements early during the design process can increase the efficiency of the system development and deployment, especially as experts with various backgrounds are involved. In this contribution, a new graphical notation is introduced and evaluated in three industrial case-studies. The notation describes the underlying hardware and software components of cyber-physical systems of systems, the flow of data, and relevant constraints. The evaluation proved that the notation is powerful in supporting the engineering of data collection and analysis architectures in industrial automation. Future work is related to extending the scope of the modeling approach to include safety applications and real-time considerations on the field level.
During the execution of a robotic grasping task,the task may fail due to the close proximity of multiple objects if grasping is the only motion ***-prehensile manipulations,such as pushing,can be used to rearrange obj...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
During the execution of a robotic grasping task,the task may fail due to the close proximity of multiple objects if grasping is the only motion ***-prehensile manipulations,such as pushing,can be used to rearrange objects and benefit *** pushing actions with different speeds,distances,and routines may result in better *** this study,we propose a vision perception-based Adaptive Pushing Assisted Grasping Network(APAGN) system for generating a sequence of actions that includes grasping and adaptive *** can perceive the scene and then predict the locations of objects after an adaptive push,which adjusts the force and direction of pushing based on expected *** achieve a more efficient calculation,an Action Selector of APAGN is designed to choose the object with the highest expected outcome before making a *** value of pushing actions is estimated based on how they benefit grasping,which breaks the limitation of manually designed *** show that APAGN might achieve higher action efficiency than baseline methods,especially in cluttered environments.
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