Pub Date : 2023-01-01DOI: 10.1007/978-3-031-26474-0
{"title":"Informatics in Control, Automation and Robotics: 18th International Conference, ICINCO 2021 Lieusaint - Paris, France, July 6–8, 2021, Revised Selected Papers","authors":"","doi":"10.1007/978-3-031-26474-0","DOIUrl":"https://doi.org/10.1007/978-3-031-26474-0","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90338802","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}
: Being able to provide trustworthy localization for a robot in a map is essential for various tasks with safety-related requirements. In contrast to classical probabilistic approaches that represent the uncertainty as a Gaussian distribution, we use interval error bounds for the uncertainty estimation of a localization problem. To tackle and identify the limitations of probabilistic localization uncertainty estimation, we carry out comparison experiments between an interval-based method and a factor graph-based probabilistic method. Different measurement error models are propagated by the two methods to derive the robot pose uncertainty estimates. Results show that the probabilistic approach can provide very good pose uncertainty when there is no non-Gaussian systematic sensor error. However, if the measurements have unmodeled systematic errors, the interval approach is able to robustly contain the true poses whereas the probabilistic approach gives completely wrong results.
{"title":"Interval-based Robot Localization with Uncertainty Evaluation","authors":"Yuehan Jiang, Aaronkumar Ehambram, Bernardo Wagner","doi":"10.5220/0011143700003271","DOIUrl":"https://doi.org/10.5220/0011143700003271","url":null,"abstract":": Being able to provide trustworthy localization for a robot in a map is essential for various tasks with safety-related requirements. In contrast to classical probabilistic approaches that represent the uncertainty as a Gaussian distribution, we use interval error bounds for the uncertainty estimation of a localization problem. To tackle and identify the limitations of probabilistic localization uncertainty estimation, we carry out comparison experiments between an interval-based method and a factor graph-based probabilistic method. Different measurement error models are propagated by the two methods to derive the robot pose uncertainty estimates. Results show that the probabilistic approach can provide very good pose uncertainty when there is no non-Gaussian systematic sensor error. However, if the measurements have unmodeled systematic errors, the interval approach is able to robustly contain the true poses whereas the probabilistic approach gives completely wrong results.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"60 1","pages":"296-303"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73825643","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}
Pub Date : 2022-01-01DOI: 10.5220/0011259900003271
V. Sima
{"title":"Solving Stable Generalized Lyapunov Equations for Hankel Singular Values Computation","authors":"V. Sima","doi":"10.5220/0011259900003271","DOIUrl":"https://doi.org/10.5220/0011259900003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"12 1","pages":"130-137"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75795753","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}
Pub Date : 2022-01-01DOI: 10.5220/0011320700003271
Pooja Bhat, Matthias Nieuwenhuisen, Dirk Schulz
: Grasping of objects is not always feasible for robot manipulators, e.g
:对于机械手来说，抓取物体并不总是可行的，例如
{"title":"Design and Implementation of Non-prehensile Manipulation Strategies","authors":"Pooja Bhat, Matthias Nieuwenhuisen, Dirk Schulz","doi":"10.5220/0011320700003271","DOIUrl":"https://doi.org/10.5220/0011320700003271","url":null,"abstract":": Grasping of objects is not always feasible for robot manipulators, e.g","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"40 1","pages":"67-78"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75706026","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}
Pub Date : 2022-01-01DOI: 10.5220/0011287000003271
Wen-Lin Sun, Yu-Lun Huang, Kai-Wei Yeh
{"title":"A Recommendation Mechanism of Selecting Machine Learning Models for Fault Diagnosis","authors":"Wen-Lin Sun, Yu-Lun Huang, Kai-Wei Yeh","doi":"10.5220/0011287000003271","DOIUrl":"https://doi.org/10.5220/0011287000003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"91 1","pages":"49-57"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79410267","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}
Pub Date : 2022-01-01DOI: 10.5220/0011268400003271
Yann Koeberle, S. Sabatini, D. Tsishkou, C. Sabourin
: Traffic simulation has gained a lot of interest for autonomous driving companies for qualitative safety evaluation of self driving vehicles. In order to improve self driving systems from synthetic simulated experiences, traffic agents need to adapt to various situations while behaving as a human driver would do. However, simulating realistic traffic agents is still challenging because human driving style cannot easily be encoded in a driving policy. Adversarial Imitation learning (AIL) already proved that realistic driving policies could be learnt from demonstration but mainly on highways (NGSIM Dataset). Nevertheless, traffic interactions are very restricted on straight lanes and practical use cases of traffic simulation requires driving agents that can handle more various road topologies like roundabouts, complex intersections or merging. In this work, we analyse how to learn realistic driving policies on real and highly interactive driving scenes of Interaction Dataset based on AIL algorithms. We introduce a new driving policy architecture built upon the Lanelet2 map format which combines a path planner and an action space in curvilinear coordinates to reduce exploration complexity during learning. We leverage benefits of reward engineering and variational information bottleneck to propose an algorithm that outperforms all AIL baselines. We show that our learning agent is not only able to imitate humane like drivers but can also adapts safely to situations unseen during training.
{"title":"Learning Human-like Driving Policies from Real Interactive Driving Scenes","authors":"Yann Koeberle, S. Sabatini, D. Tsishkou, C. Sabourin","doi":"10.5220/0011268400003271","DOIUrl":"https://doi.org/10.5220/0011268400003271","url":null,"abstract":": Traffic simulation has gained a lot of interest for autonomous driving companies for qualitative safety evaluation of self driving vehicles. In order to improve self driving systems from synthetic simulated experiences, traffic agents need to adapt to various situations while behaving as a human driver would do. However, simulating realistic traffic agents is still challenging because human driving style cannot easily be encoded in a driving policy. Adversarial Imitation learning (AIL) already proved that realistic driving policies could be learnt from demonstration but mainly on highways (NGSIM Dataset). Nevertheless, traffic interactions are very restricted on straight lanes and practical use cases of traffic simulation requires driving agents that can handle more various road topologies like roundabouts, complex intersections or merging. In this work, we analyse how to learn realistic driving policies on real and highly interactive driving scenes of Interaction Dataset based on AIL algorithms. We introduce a new driving policy architecture built upon the Lanelet2 map format which combines a path planner and an action space in curvilinear coordinates to reduce exploration complexity during learning. We leverage benefits of reward engineering and variational information bottleneck to propose an algorithm that outperforms all AIL baselines. We show that our learning agent is not only able to imitate humane like drivers but can also adapts safely to situations unseen during training.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"75 1","pages":"419-426"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77969266","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}
Pub Date : 2022-01-01DOI: 10.5220/0011347900003271
F. Youssef, A. B. Zaki, W. Gomaa
: Squats are one of the most frequent at-home fitness activities. If the squat is performed improperly for a long time, it might result in serious injuries. This study presents a multiclass, multi-label dataset for squat workout evaluation. The dataset collects the most typical faults that novices make when practicing squats without supervision. As a first step toward universal virtual coaching for indoor exercises, the main objective is to contribute to the creation of a virtual coach for the squat exercise. A 3d position estimation is used to extract critical points from a squatting subject, then placed them in a distance matrix as the input to a multi-layer convolution neural network with residual blocks. The proposed approach uses the exact match ratio performance metric and is able to achieve 94% accuracy. The performance of transfer learning as a known machine learning technique is evaluated for the squat activity classification task. Transfer learning is essential when changing the setup and configuration of the data collection process to reduce the computational efforts and resources.
{"title":"Analysis of the Squat Exercise from Visual Data","authors":"F. Youssef, A. B. Zaki, W. Gomaa","doi":"10.5220/0011347900003271","DOIUrl":"https://doi.org/10.5220/0011347900003271","url":null,"abstract":": Squats are one of the most frequent at-home fitness activities. If the squat is performed improperly for a long time, it might result in serious injuries. This study presents a multiclass, multi-label dataset for squat workout evaluation. The dataset collects the most typical faults that novices make when practicing squats without supervision. As a first step toward universal virtual coaching for indoor exercises, the main objective is to contribute to the creation of a virtual coach for the squat exercise. A 3d position estimation is used to extract critical points from a squatting subject, then placed them in a distance matrix as the input to a multi-layer convolution neural network with residual blocks. The proposed approach uses the exact match ratio performance metric and is able to achieve 94% accuracy. The performance of transfer learning as a known machine learning technique is evaluated for the squat activity classification task. Transfer learning is essential when changing the setup and configuration of the data collection process to reduce the computational efforts and resources.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"14 1","pages":"79-88"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78090968","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}
Pub Date : 2022-01-01DOI: 10.5220/0011287200003271
Chris Schöberlein, J. Quellmalz, H. Schlegel, M. Dix
: Condition monitoring of modern production systems has established itself as an independent area of research in recent years. Main goal is to achieve an increase in machine productivity by reducing downtime and maintenance costs. In particular, the installed electromechanical axes offer great potential for improvement. Besides an installation of additional sensors, modern drive systems also provide various signals suitable for superordinated monitoring systems. The paper presents a novel approach for monitoring of specific mechanical axis components based solely on internal control loop signals. Fundamental idea is to combine a parametric approach for vibration analysis, the so-called Prony analysis, with a drive-based setpoint generation and data aquisition. The method is verified by detecting emulated malfunctions on a single-axis test stand and a three-axis vertical milling machining center. Experimental investigations prove that the presented approach is capable of reliably detecting the artificially introduced defects on different axis components.
{"title":"Sensorless Condition Monitoring of Feed Axis Components in Production Systems by Applying Prony Analysis","authors":"Chris Schöberlein, J. Quellmalz, H. Schlegel, M. Dix","doi":"10.5220/0011287200003271","DOIUrl":"https://doi.org/10.5220/0011287200003271","url":null,"abstract":": Condition monitoring of modern production systems has established itself as an independent area of research in recent years. Main goal is to achieve an increase in machine productivity by reducing downtime and maintenance costs. In particular, the installed electromechanical axes offer great potential for improvement. Besides an installation of additional sensors, modern drive systems also provide various signals suitable for superordinated monitoring systems. The paper presents a novel approach for monitoring of specific mechanical axis components based solely on internal control loop signals. Fundamental idea is to combine a parametric approach for vibration analysis, the so-called Prony analysis, with a drive-based setpoint generation and data aquisition. The method is verified by detecting emulated malfunctions on a single-axis test stand and a three-axis vertical milling machining center. Experimental investigations prove that the presented approach is capable of reliably detecting the artificially introduced defects on different axis components.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"66 1","pages":"214-221"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77031764","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}
Pub Date : 2022-01-01DOI: 10.5220/0011271500003271
Isaac Okola
{"title":"Input-Output Multiobjective Optimization Approach for Food-Energy-Water Nexus","authors":"Isaac Okola","doi":"10.5220/0011271500003271","DOIUrl":"https://doi.org/10.5220/0011271500003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"53 1","pages":"155-160"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81410082","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}
Pub Date : 2022-01-01DOI: 10.5220/0011350000003271
J. Belikov, Molika Meas, R. Machlev, A. Kose, A. Tepljakov, Lauri Loo, E. Petlenkov, Y. Levron
{"title":"Explainable AI based Fault Detection and Diagnosis System for Air Handling Units","authors":"J. Belikov, Molika Meas, R. Machlev, A. Kose, A. Tepljakov, Lauri Loo, E. Petlenkov, Y. Levron","doi":"10.5220/0011350000003271","DOIUrl":"https://doi.org/10.5220/0011350000003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"108 1","pages":"271-279"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89315172","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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