Pub Date : 2017-12-11DOI: 10.1109/MFI.2017.8170443
Chang-hun Lee, Hyo-Sang Shin, A. Tsourdos, Z. Skaf
In this article, we propose a data analytics development to detect unusual patterns of flights from a vast amounts of FDR (flight data recorder) data for supporting airline maintenance operations. A fundamental rationale behind this development is that if there are potential issues on mechanical parts of an aircraft during a flight, evidences for these issues are most likely included in the FDR data. Therefore, the data analysis of FDR data enables us to detect the potential issues in the aircraft before they occur. To this end, in a data pre-processing step, a data filtering, a data sampling, and a data transformation are sequentially performed. And then, in this analysis, all time series data in the FDR are classified into three types: a continuous signal, a discrete signal, and a warning signal. For each type of signal, a high-dimensional vector by arranging the time series data is chosen as features. In the feature section process, a correlation analysis, a correlation relaxation, and a dimension reduction are sequentially conducted. Finally, a type of k-nearest neighbor approach is applied to automatically identify the FDR data in which the unusual flight patterns are recorded from a large amount of FDR data. The proposed method is tested with using a realistic FDR data from the NASA's open database.
{"title":"Data analytics development of FDR (Flight Data Recorder) data for airline maintenance operations","authors":"Chang-hun Lee, Hyo-Sang Shin, A. Tsourdos, Z. Skaf","doi":"10.1109/MFI.2017.8170443","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170443","url":null,"abstract":"In this article, we propose a data analytics development to detect unusual patterns of flights from a vast amounts of FDR (flight data recorder) data for supporting airline maintenance operations. A fundamental rationale behind this development is that if there are potential issues on mechanical parts of an aircraft during a flight, evidences for these issues are most likely included in the FDR data. Therefore, the data analysis of FDR data enables us to detect the potential issues in the aircraft before they occur. To this end, in a data pre-processing step, a data filtering, a data sampling, and a data transformation are sequentially performed. And then, in this analysis, all time series data in the FDR are classified into three types: a continuous signal, a discrete signal, and a warning signal. For each type of signal, a high-dimensional vector by arranging the time series data is chosen as features. In the feature section process, a correlation analysis, a correlation relaxation, and a dimension reduction are sequentially conducted. Finally, a type of k-nearest neighbor approach is applied to automatically identify the FDR data in which the unusual flight patterns are recorded from a large amount of FDR data. The proposed method is tested with using a realistic FDR data from the NASA's open database.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121732754","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 : 2017-12-11DOI: 10.1109/MFI.2017.8170388
Le Pham Tuyen, Md. Abu Layek, Ngo Anh Vien, TaeChoong Chung
Based on state-of-the-art deep reinforcement learning (Deep RL) algorithms, two controllers are proposed to pass a ship through a specified gate. Deep RL is a powerful approach to learn a complex controller which is expected to adapt to different situations of systems. This paper explains how to apply these algorithms to ship steering problem. The simulation results show advantages of these algorithms in reproducing reliable and stable controllers.
{"title":"Deep reinforcement learning algorithms for steering an underactuated ship","authors":"Le Pham Tuyen, Md. Abu Layek, Ngo Anh Vien, TaeChoong Chung","doi":"10.1109/MFI.2017.8170388","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170388","url":null,"abstract":"Based on state-of-the-art deep reinforcement learning (Deep RL) algorithms, two controllers are proposed to pass a ship through a specified gate. Deep RL is a powerful approach to learn a complex controller which is expected to adapt to different situations of systems. This paper explains how to apply these algorithms to ship steering problem. The simulation results show advantages of these algorithms in reproducing reliable and stable controllers.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114968100","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 : 2017-12-11DOI: 10.1109/MFI.2017.8170410
M. Lager, E. A. Topp, J. Malec
Many ships today rely on Global Navigation Satellite Systems (GNSS), for their navigation, where GPS (Global Positioning System) is the most well-known. Unfortunately, the GNSS systems make the ships dependent on external systems, which can be malfunctioning, be jammed or be spoofed. There are today some proposed techniques where, e.g., bottom depth measurements are compared with known maps using Bayesian calculations, which results in a position estimation. Both maps and navigational sensor equipment are used in these techniques, most often relying on high-resolution maps, with the accuracy of the navigational sensors being less important. Instead of relying on high-resolution maps and low accuracy navigation sensors, this paper presents an implementation of the opposite, namely using low-resolution maps, but compensating this by using high accuracy navigational sensors and fusing data from both bottom depth measurements and magnetic field measurements. The results from the simulated tests, described in this paper, show that the position error is below 25m throughout the whole test, and that the mean of the error is below 13m, which in most cases would be accurate enough to use for navigation.
{"title":"Underwater Terrain Navigation Using Standard Sea Charts and Magnetic Field Maps","authors":"M. Lager, E. A. Topp, J. Malec","doi":"10.1109/MFI.2017.8170410","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170410","url":null,"abstract":"Many ships today rely on Global Navigation Satellite Systems (GNSS), for their navigation, where GPS (Global Positioning System) is the most well-known. Unfortunately, the GNSS systems make the ships dependent on external systems, which can be malfunctioning, be jammed or be spoofed. There are today some proposed techniques where, e.g., bottom depth measurements are compared with known maps using Bayesian calculations, which results in a position estimation. Both maps and navigational sensor equipment are used in these techniques, most often relying on high-resolution maps, with the accuracy of the navigational sensors being less important. Instead of relying on high-resolution maps and low accuracy navigation sensors, this paper presents an implementation of the opposite, namely using low-resolution maps, but compensating this by using high accuracy navigational sensors and fusing data from both bottom depth measurements and magnetic field measurements. The results from the simulated tests, described in this paper, show that the position error is below 25m throughout the whole test, and that the mean of the error is below 13m, which in most cases would be accurate enough to use for navigation.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124796705","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 : 2017-12-07DOI: 10.1109/MFI.2017.8170362
S. Morino, Masaki Takahashi
Weight gain and stretched and weakens abdominal muscles by an enlarging gravid uterus are remarkable features during pregnancy. These changes elicit postural and movement instability and place strain on various body segments. In general, agonist and antagonist muscles of body segments act simultaneously to increase joint stabilization during human movements. The co-contraction might be well observed in pregnant women because of their unstable body joints. Musculoskeletal models are well used to investigate muscle load. However, very few studies have been conducted on the model for pregnant women. Additionally, it is difficult to estimate the co-contraction of muscles by using musculoskeletal models. Therefore, the purpose of this study is to construct musculoskeletal model for pregnant women and estimate co-contraction of trunk muscles. At first, motion analysis of sit-to-stand for a pregnant woman was conducted to obtain the motion and force data for inputting to musculoskeletal model. Simultaneously, muscle activation of rectus abdominis and longissimus were measured by using surface electromyography. On the other hand, the size and mass of body segment of a musculoskeletal model were changed to meet pregnant women. Then, stretched abdominal muscle was modeled. At last, the co-contraction of rectus abdominis and longissimus was estimated from EMG data and joint torque that was calculated from the musculoskeletal model using genetic algorithm.
{"title":"Musculoskeletal model of a pregnant woman considering stretched rectus abdominis and co-contraction muscle activation","authors":"S. Morino, Masaki Takahashi","doi":"10.1109/MFI.2017.8170362","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170362","url":null,"abstract":"Weight gain and stretched and weakens abdominal muscles by an enlarging gravid uterus are remarkable features during pregnancy. These changes elicit postural and movement instability and place strain on various body segments. In general, agonist and antagonist muscles of body segments act simultaneously to increase joint stabilization during human movements. The co-contraction might be well observed in pregnant women because of their unstable body joints. Musculoskeletal models are well used to investigate muscle load. However, very few studies have been conducted on the model for pregnant women. Additionally, it is difficult to estimate the co-contraction of muscles by using musculoskeletal models. Therefore, the purpose of this study is to construct musculoskeletal model for pregnant women and estimate co-contraction of trunk muscles. At first, motion analysis of sit-to-stand for a pregnant woman was conducted to obtain the motion and force data for inputting to musculoskeletal model. Simultaneously, muscle activation of rectus abdominis and longissimus were measured by using surface electromyography. On the other hand, the size and mass of body segment of a musculoskeletal model were changed to meet pregnant women. Then, stretched abdominal muscle was modeled. At last, the co-contraction of rectus abdominis and longissimus was estimated from EMG data and joint torque that was calculated from the musculoskeletal model using genetic algorithm.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628947","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 : 2017-11-18DOI: 10.1109/MFI.2017.8170360
A. Masoum, N. Meratnia, P. Havinga
Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we introduce a distributed compressive sensing approach, which utilizes spatial correlation among sensor nodes to group them into coalitions. The coalition formation method is represented by a block diagonal measurement matrix whose each diagonal entity corresponds to one of the coalitions. Then, a spatial-temporal correlation-based compressive sensing approach is used inside each coalition to schedule sensor nodes and encode their readings. Distributed data encoding over coalitions increases robustness and scalability of the approach. Simulation results verify that the proposed solution outperforms other compressive sensing approaches significantly in terms of data accuracy and energy efficiency.
{"title":"Compressive sensing based data collection in wireless sensor networks","authors":"A. Masoum, N. Meratnia, P. Havinga","doi":"10.1109/MFI.2017.8170360","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170360","url":null,"abstract":"Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we introduce a distributed compressive sensing approach, which utilizes spatial correlation among sensor nodes to group them into coalitions. The coalition formation method is represented by a block diagonal measurement matrix whose each diagonal entity corresponds to one of the coalitions. Then, a spatial-temporal correlation-based compressive sensing approach is used inside each coalition to schedule sensor nodes and encode their readings. Distributed data encoding over coalitions increases robustness and scalability of the approach. Simulation results verify that the proposed solution outperforms other compressive sensing approaches significantly in terms of data accuracy and energy efficiency.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132767974","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 : 2017-11-16DOI: 10.1109/MFI.2017.8170427
Gihyoun Lee, S. Jin, Seung Hyun Lee, B. Abibullaev, J. An
Functional near-infrared spectroscopy (fNIRS) can be employed to investigate brain activation by measuring the absorption of near-infrared light through an intact skull. fNIRS can measure hemoglobin signals, which are similar to functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signals. The general linear model (GLM), which is a standard method for fMRI imaging, has been applied for fNIRS imaging analysis. However, when the subject moves, the fNIRS signal can contain artifacts during the measurement. These artifacts are called motion artifacts. However, the GLM has a drawback of failure because of motion artifacts. Recently, wavelet and hemodynamic response function based algorithms are popular detrending methods of motion artifact correction for fNIRS signals. However, these methods cannot show impressive performance in harsh environments such as overground walking tasks. This paper suggests a new motion artifact correction method that uses an entropy based unbalanced optode decision rule and a wavelet regression based back propagation neural network. Through the experiments, the performance of the proposed method was proven using graphic results, a brain activation map, and an objective performance index when compared with conventional detrending algorithms.
{"title":"fNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network","authors":"Gihyoun Lee, S. Jin, Seung Hyun Lee, B. Abibullaev, J. An","doi":"10.1109/MFI.2017.8170427","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170427","url":null,"abstract":"Functional near-infrared spectroscopy (fNIRS) can be employed to investigate brain activation by measuring the absorption of near-infrared light through an intact skull. fNIRS can measure hemoglobin signals, which are similar to functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signals. The general linear model (GLM), which is a standard method for fMRI imaging, has been applied for fNIRS imaging analysis. However, when the subject moves, the fNIRS signal can contain artifacts during the measurement. These artifacts are called motion artifacts. However, the GLM has a drawback of failure because of motion artifacts. Recently, wavelet and hemodynamic response function based algorithms are popular detrending methods of motion artifact correction for fNIRS signals. However, these methods cannot show impressive performance in harsh environments such as overground walking tasks. This paper suggests a new motion artifact correction method that uses an entropy based unbalanced optode decision rule and a wavelet regression based back propagation neural network. Through the experiments, the performance of the proposed method was proven using graphic results, a brain activation map, and an objective performance index when compared with conventional detrending algorithms.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127105595","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 : 2017-11-16DOI: 10.1109/MFI.2017.8170377
Rock-Hyun Choi, Won-Seok Kang, C. Son
This research presents a scheme for explainable sleep quality evaluation utilizing the heart rate based sleep index. In the proposed model, the global covering rule induction of LERS (Learning from Examples based on Rough Sets) is used to generate rules associated with sleep quality status, such as ‘Bad,’ ‘Normal,’ and ‘Good.’ These rules are used to interpret the three sleep statuses. To show the applicability of the proposed scheme, we construct a sleep quality evaluation model based on sleep intraday time-series data collected from 280 factory and office workers with Fitbit fitness trackers. An evaluation of the proposed model was provided through statistical cross validation experiments.
{"title":"Explainable sleep quality evaluation model using machine learning approach","authors":"Rock-Hyun Choi, Won-Seok Kang, C. Son","doi":"10.1109/MFI.2017.8170377","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170377","url":null,"abstract":"This research presents a scheme for explainable sleep quality evaluation utilizing the heart rate based sleep index. In the proposed model, the global covering rule induction of LERS (Learning from Examples based on Rough Sets) is used to generate rules associated with sleep quality status, such as ‘Bad,’ ‘Normal,’ and ‘Good.’ These rules are used to interpret the three sleep statuses. To show the applicability of the proposed scheme, we construct a sleep quality evaluation model based on sleep intraday time-series data collected from 280 factory and office workers with Fitbit fitness trackers. An evaluation of the proposed model was provided through statistical cross validation experiments.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133770486","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 : 2017-11-16DOI: 10.1109/MFI.2017.8170359
Florian Rosenthal, B. Noack, U. Hanebeck
In this paper, we consider state estimation in Networked Control Systems where both control inputs and measurements are transmitted via networks which are lossy and introduce random transmission delays. In contrast to the common notion of TCP-like communication, where successful transmissions are acknowledged instantaneously and without losses, we focus on the case where the acknowledgment packets provided by the actuator upon reception of applicable control inputs are also subject to delays and losses. Consequently, the estimator has only partial and belated knowledge on the actually applied control inputs, which results in additional uncertainty. We derive an estimator for the considered setup by generalizing an existing approach for UDP-like communication which integrates estimates of the applied control inputs into the overall state estimation. The presented estimator is assessed in terms of Monte Carlo simulations.
{"title":"State estimation in networked control systems with delayed and lossy acknowledgments","authors":"Florian Rosenthal, B. Noack, U. Hanebeck","doi":"10.1109/MFI.2017.8170359","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170359","url":null,"abstract":"In this paper, we consider state estimation in Networked Control Systems where both control inputs and measurements are transmitted via networks which are lossy and introduce random transmission delays. In contrast to the common notion of TCP-like communication, where successful transmissions are acknowledged instantaneously and without losses, we focus on the case where the acknowledgment packets provided by the actuator upon reception of applicable control inputs are also subject to delays and losses. Consequently, the estimator has only partial and belated knowledge on the actually applied control inputs, which results in additional uncertainty. We derive an estimator for the considered setup by generalizing an existing approach for UDP-like communication which integrates estimates of the applied control inputs into the overall state estimation. The presented estimator is assessed in terms of Monte Carlo simulations.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134011129","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 : 2017-11-16DOI: 10.1109/MFI.2017.8170409
Justin D. Brody, Anna M. R. Dixon, Daniel Donavanik, R. Robinson, W. Nothwang
Human-autonomy teaming using physiological sensors poses a novel sensor fusion problem due to the dynamic nature of the sensor models and the difficulty of modeling their temporal and inter-subject variability. Developing analytical models therefore requires defining objective criteria for selection and weighting of sensors under an appropriate fusion paradigm. We investigate a selection methodology grounded in two intuitions: 1) that maximizing the relevance between sensors and target classes will enhance overall performance within a given fusion scheme; and 2) that minimizing redundancy amongst the selected sensors will not harm fusion performance and may improve precision and recall. We apply these intuitions to a human-autonomy image classification task. Preliminary results indicate strong support for the relevance hypothesis and weaker effects for the redundancy hypothesis. This relationship and its application to human-autonomy sensor fusion are explored within a framework employing three common fusion methodologies: Naive Bayes fusion, Dempster-Shafer theory, and Dynamic Belief Fusion.
{"title":"Relevance and redundancy as selection techniques for human-autonomy sensor fusion","authors":"Justin D. Brody, Anna M. R. Dixon, Daniel Donavanik, R. Robinson, W. Nothwang","doi":"10.1109/MFI.2017.8170409","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170409","url":null,"abstract":"Human-autonomy teaming using physiological sensors poses a novel sensor fusion problem due to the dynamic nature of the sensor models and the difficulty of modeling their temporal and inter-subject variability. Developing analytical models therefore requires defining objective criteria for selection and weighting of sensors under an appropriate fusion paradigm. We investigate a selection methodology grounded in two intuitions: 1) that maximizing the relevance between sensors and target classes will enhance overall performance within a given fusion scheme; and 2) that minimizing redundancy amongst the selected sensors will not harm fusion performance and may improve precision and recall. We apply these intuitions to a human-autonomy image classification task. Preliminary results indicate strong support for the relevance hypothesis and weaker effects for the redundancy hypothesis. This relationship and its application to human-autonomy sensor fusion are explored within a framework employing three common fusion methodologies: Naive Bayes fusion, Dempster-Shafer theory, and Dynamic Belief Fusion.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125412752","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 : 2017-11-01DOI: 10.1109/MFI.2017.8170398
Sujeong You, S. Ji
In this paper, we propose a robotic delivery service in a situation of reception party. For the purpose, we first try to select the goal points to which the mobile servant robot should be positioned according to the social interaction of the recognized group. And we derive a collision-free route which the robot may not disturb people's conversation while moving along with. Finally, we verify the effectiveness of our proposed algorithm with simulation results.
{"title":"F-formation based navigation planner for a mobile servant robot","authors":"Sujeong You, S. Ji","doi":"10.1109/MFI.2017.8170398","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170398","url":null,"abstract":"In this paper, we propose a robotic delivery service in a situation of reception party. For the purpose, we first try to select the goal points to which the mobile servant robot should be positioned according to the social interaction of the recognized group. And we derive a collision-free route which the robot may not disturb people's conversation while moving along with. Finally, we verify the effectiveness of our proposed algorithm with simulation results.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":" 40","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120828856","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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