Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8843122
Qixing Wang, Fei Miao, Jie Wu, Yuanfang Niu, Chengliang Wang, N. Lownes
As Autonomous Vehicles (AVs) become possible for E-hailing services operate, especially when telecom companies start deploying next-generation wireless networks (known as 5G), many new technologies may be applied in these vehicles. Dynamic-route-switching is one of these technologies, which could help vehicles find the best possible route based on real-time traffic information. However, allowing all AVs to choose their own optimal routes is not the best solution for a complex city network, since each vehicle ignores its negative effect on the road system due to the additional congestion it creates. As a result, with this system, some of the links may become over-congested, causing the whole road network system performance to degrade. Meanwhile, the travel time reliability, especially during the peak hours, is an essential factor to improve the customers’ ride experience. Unfortunately, these two issues have received relatively less attention. In this paper, we design a link-based dynamic pricing model to improve the road network system and travel time reliability at the same time. In this approach, we assume that all links are eligible with the dynamic pricing, and AVs will be perfect informed with update traffic condition and follow the dynamic road pricing. A heuristic approach is developed to address this computationally difficult problem. The output includes link-based surcharge, new travel demand and traffic condition which would improve the system performance close to the System Optimal (SO) solution and maintain the travel time reliability. Finally, we evaluate the effectiveness and efficiency of the proposed model to the well-known test Sioux Falls network.
{"title":"Dynamic Pricing for Autonomous Vehicle E-hailing Services Reliability and Performance Improvement","authors":"Qixing Wang, Fei Miao, Jie Wu, Yuanfang Niu, Chengliang Wang, N. Lownes","doi":"10.1109/COASE.2019.8843122","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843122","url":null,"abstract":"As Autonomous Vehicles (AVs) become possible for E-hailing services operate, especially when telecom companies start deploying next-generation wireless networks (known as 5G), many new technologies may be applied in these vehicles. Dynamic-route-switching is one of these technologies, which could help vehicles find the best possible route based on real-time traffic information. However, allowing all AVs to choose their own optimal routes is not the best solution for a complex city network, since each vehicle ignores its negative effect on the road system due to the additional congestion it creates. As a result, with this system, some of the links may become over-congested, causing the whole road network system performance to degrade. Meanwhile, the travel time reliability, especially during the peak hours, is an essential factor to improve the customers’ ride experience. Unfortunately, these two issues have received relatively less attention. In this paper, we design a link-based dynamic pricing model to improve the road network system and travel time reliability at the same time. In this approach, we assume that all links are eligible with the dynamic pricing, and AVs will be perfect informed with update traffic condition and follow the dynamic road pricing. A heuristic approach is developed to address this computationally difficult problem. The output includes link-based surcharge, new travel demand and traffic condition which would improve the system performance close to the System Optimal (SO) solution and maintain the travel time reliability. Finally, we evaluate the effectiveness and efficiency of the proposed model to the well-known test Sioux Falls network.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"17 1","pages":"948-953"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86348963","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843236
N. Kimura, Ryo Sakai, Shinichi Katsumata, Nobuhiro Chihara
We propose a deep learning-based method that simultaneously determines a target object to be picked up by an autonomous manipulator and the velocity of an automated guided vehicle (AGV) that passes in front of the manipulator while the AGV carries a carton case containing the target and other objects. Our method can efficiently perform automated piece-picking operations in warehouses without the AGV needing to pause in front of the manipulator. In our method, for preparing supervised data sets with color images of objects that are randomly piled up in the carton case, a simulator checks whether each object is “pickable” or not by trying to plan the manipulator’s motion to have its hand reach the object while avoiding surrounding obstacles by using the depth images in consideration of the carton case’s movement and velocity. Then, we make each of multiple deep convolutional neural networks (DCNNs) corresponding to multiple levels of velocity learn to detect grasp points for only pickable objects from an RGB image. In our experimental test, using our method, a prototype of the system successfully picked ordered objects up without the AGV pausing while the AGV changed its velocity depending on the layout of the objects in the carton case.
{"title":"Simultaneously Determining Target Object and Transport Velocity for Manipulator and Moving Vehicle in Piece-Picking Operation","authors":"N. Kimura, Ryo Sakai, Shinichi Katsumata, Nobuhiro Chihara","doi":"10.1109/COASE.2019.8843236","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843236","url":null,"abstract":"We propose a deep learning-based method that simultaneously determines a target object to be picked up by an autonomous manipulator and the velocity of an automated guided vehicle (AGV) that passes in front of the manipulator while the AGV carries a carton case containing the target and other objects. Our method can efficiently perform automated piece-picking operations in warehouses without the AGV needing to pause in front of the manipulator. In our method, for preparing supervised data sets with color images of objects that are randomly piled up in the carton case, a simulator checks whether each object is “pickable” or not by trying to plan the manipulator’s motion to have its hand reach the object while avoiding surrounding obstacles by using the depth images in consideration of the carton case’s movement and velocity. Then, we make each of multiple deep convolutional neural networks (DCNNs) corresponding to multiple levels of velocity learn to detect grasp points for only pickable objects from an RGB image. In our experimental test, using our method, a prototype of the system successfully picked ordered objects up without the AGV pausing while the AGV changed its velocity depending on the layout of the objects in the carton case.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"23 1","pages":"1066-1073"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87614018","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843188
Xiaodong Jia, Shiming Duan, C. Lee, P. Radecki, Jay Lee
Torque converters (TC) are widely used in automatic transmissions and continuous variation transmissions (CVT) to transfer the engine power to the transmission through fluid and mechanical coupling. The degradation of the TC Clutch (TCC) may result in excessive vibrations in the TC and driveline, elevated transmission temperature, and transmission shudder. The present study develops a systematic approach to detect TCC system degradation utilizing both machine learning techniques and domain expertise. The validation using vehicle data demonstrates the effectiveness of the approach. The early detection of TCC degradation may help to prolong the lifespan of TC, protect transmission components from further damage, and avoid limp-home and walk-home incidents.
{"title":"A Methodology for the Early Diagnosis of Vehicle Torque Converter Clutch Degradation","authors":"Xiaodong Jia, Shiming Duan, C. Lee, P. Radecki, Jay Lee","doi":"10.1109/COASE.2019.8843188","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843188","url":null,"abstract":"Torque converters (TC) are widely used in automatic transmissions and continuous variation transmissions (CVT) to transfer the engine power to the transmission through fluid and mechanical coupling. The degradation of the TC Clutch (TCC) may result in excessive vibrations in the TC and driveline, elevated transmission temperature, and transmission shudder. The present study develops a systematic approach to detect TCC system degradation utilizing both machine learning techniques and domain expertise. The validation using vehicle data demonstrates the effectiveness of the approach. The early detection of TCC degradation may help to prolong the lifespan of TC, protect transmission components from further damage, and avoid limp-home and walk-home incidents.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"23 1","pages":"529-534"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82984922","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 : 2019-08-01DOI: 10.1109/COASE.2019.8842852
M. P. Fanti, A. M. Mangini, M. Roccotelli, B. Silvestri, S. Digiesi
This paper deals with the electric vehicle fleet re-location management in a sharing system. The mobility sharing systems efficiency depends on the vehicles relocation task that strongly affect the company operating cost, and consequently the service price for users. The proposed approach aims at minimizing the cost of vehicles relocation for a sharing company by involving users through an innovative incentive scheme. The idea is to request users of the sharing service to relocate the EVs, e.g. through an IT application, incentivizing them by free-of-charge travels and rewards. The proposed incentive mechanism is based on the application of different levels of incentive proposal. In addition, in case of user unavailability, the vehicle relocation is guaranteed by the company staff. To this aim, a first ILP is formalized to manage the relocation task by the company staff. Moreover, a second ILP allows the company to involve users in the relocation process by the proposed incentive mechanism. Finally, a case study is presented to show the application of the proposed methodology on the relocation of electric cars and light electric vehicles.
{"title":"Electric Vehicle Fleet Relocation Management for Sharing Systems based on Incentive Mechanism","authors":"M. P. Fanti, A. M. Mangini, M. Roccotelli, B. Silvestri, S. Digiesi","doi":"10.1109/COASE.2019.8842852","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842852","url":null,"abstract":"This paper deals with the electric vehicle fleet re-location management in a sharing system. The mobility sharing systems efficiency depends on the vehicles relocation task that strongly affect the company operating cost, and consequently the service price for users. The proposed approach aims at minimizing the cost of vehicles relocation for a sharing company by involving users through an innovative incentive scheme. The idea is to request users of the sharing service to relocate the EVs, e.g. through an IT application, incentivizing them by free-of-charge travels and rewards. The proposed incentive mechanism is based on the application of different levels of incentive proposal. In addition, in case of user unavailability, the vehicle relocation is guaranteed by the company staff. To this aim, a first ILP is formalized to manage the relocation task by the company staff. Moreover, a second ILP allows the company to involve users in the relocation process by the proposed incentive mechanism. Finally, a case study is presented to show the application of the proposed methodology on the relocation of electric cars and light electric vehicles.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"114 1","pages":"1048-1053"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77718626","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843252
Yinan Wu, Gongzhuang Peng, Heming Zhang
Nowadays the increasing demand for high-reliability service compositions in manufacturing networks has brought new challenges for fault tolerance methods. It involves the real-time detection and rapid recovery of manufacturing services to deal with the unavoidable failures and errors. Appropriate dynamic fault tolerance methods need to be adopted to mask faults immediately after they occur in order to improve the reliability of the manufacturing network. Aiming at solving this problem, a dynamic fault tolerance method based on the pathfinding algorithm is thus put forward. First, a network model is constructed to explicitly describe the manufacturing services and their relationships. Then the dynamic fault tolerance problem can be modeled as a Multi-Constrained Optimal Path (MCOP) selection problem. On this basis, a novel Dynamic A* Search based Fault Tolerance (DAS_FT) algorithm is proposed to solve the NP-Complete MCOP problem. The propose d algorithm can find the suitable replacement schemes for failed service compositions with the help of the redundant resources in the manufacturing network, which will satisfy the Quality of Service (QoS) constraints of the manufacturing task at the same time. A set of computational experiments are designed to evaluate the proposed DAS_FT and other popular algorithms such as NSGA II and MFPB_HOSTP, which are applied to the same dataset. The results obtained illustrate that the DAS_FT algorithm can improve the reliability of the manufacturing network effectively. In addition, the DAS_FT can efficiently find the replacement schemes with better QoS compared with NSGA II and MFPB_HOSTP.
{"title":"A heuristic pathfinding algorithm for dynamic fault tolerance in manufacturing networks","authors":"Yinan Wu, Gongzhuang Peng, Heming Zhang","doi":"10.1109/COASE.2019.8843252","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843252","url":null,"abstract":"Nowadays the increasing demand for high-reliability service compositions in manufacturing networks has brought new challenges for fault tolerance methods. It involves the real-time detection and rapid recovery of manufacturing services to deal with the unavoidable failures and errors. Appropriate dynamic fault tolerance methods need to be adopted to mask faults immediately after they occur in order to improve the reliability of the manufacturing network. Aiming at solving this problem, a dynamic fault tolerance method based on the pathfinding algorithm is thus put forward. First, a network model is constructed to explicitly describe the manufacturing services and their relationships. Then the dynamic fault tolerance problem can be modeled as a Multi-Constrained Optimal Path (MCOP) selection problem. On this basis, a novel Dynamic A* Search based Fault Tolerance (DAS_FT) algorithm is proposed to solve the NP-Complete MCOP problem. The propose d algorithm can find the suitable replacement schemes for failed service compositions with the help of the redundant resources in the manufacturing network, which will satisfy the Quality of Service (QoS) constraints of the manufacturing task at the same time. A set of computational experiments are designed to evaluate the proposed DAS_FT and other popular algorithms such as NSGA II and MFPB_HOSTP, which are applied to the same dataset. The results obtained illustrate that the DAS_FT algorithm can improve the reliability of the manufacturing network effectively. In addition, the DAS_FT can efficiently find the replacement schemes with better QoS compared with NSGA II and MFPB_HOSTP.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"75 1","pages":"1580-1585"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80199574","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843299
Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Sean Shao Wei Lam
Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and other predictors such as surgical team, patient, temporal and operational factors in a tree-based model for predicting surgical durations. Experimental results show that the proposed two-step model is more parsimonious and outperforms existing moving averages method used by the hospital. Our model bridges the research-to-practice gap by combining data analytics with expert’s inputs to develop a deployable surgical duration prediction model for a real-world public hospital.
{"title":"Data-Driven Surgical Duration Prediction Model for Surgery Scheduling: A Case-Study for a Practice-Feasible Model in a Public Hospital","authors":"Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Sean Shao Wei Lam","doi":"10.1109/COASE.2019.8843299","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843299","url":null,"abstract":"Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and other predictors such as surgical team, patient, temporal and operational factors in a tree-based model for predicting surgical durations. Experimental results show that the proposed two-step model is more parsimonious and outperforms existing moving averages method used by the hospital. Our model bridges the research-to-practice gap by combining data analytics with expert’s inputs to develop a deployable surgical duration prediction model for a real-world public hospital.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"15 1","pages":"275-280"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81545791","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 : 2019-08-01DOI: 10.1109/COASE.2019.8842930
Yihui Hu, Ziyue Ma, Zhiwu Li
In this paper we study the active diagnosis problem in Petri nets with quiescence. We first generalize the notion of diagnosability to Petri nets that may contain deadlocks. To avoid enumerating the reachability space, we introduce a structure called the Quiescent Basis Reachability Graph, based on which a structure called the Q-diagnoser is computed. Finally, a supervisor is designed based on Q-diagnoser such that the closed-loop system is diagnosable.
{"title":"Active Diagnosis of Petri Nets Using Q-Diagnoser","authors":"Yihui Hu, Ziyue Ma, Zhiwu Li","doi":"10.1109/COASE.2019.8842930","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842930","url":null,"abstract":"In this paper we study the active diagnosis problem in Petri nets with quiescence. We first generalize the notion of diagnosability to Petri nets that may contain deadlocks. To avoid enumerating the reachability space, we introduce a structure called the Quiescent Basis Reachability Graph, based on which a structure called the Q-diagnoser is computed. Finally, a supervisor is designed based on Q-diagnoser such that the closed-loop system is diagnosable.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"77 1","pages":"203-208"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83864005","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843128
Sven Tittel, M. Malekzadeh, Jochen J. Steil
Human robot interaction (HRI) is a major research field in robotics with significant progress over the last decades. While most HRI is focused on novel light weight robots, we here present an admittance control implementation for the 6-DOF industrial robot Stäubli TX60. We use only standard and commercially available interfaces, without adding external force sensing, and present a method to estimate joint friction to improve the robot model. In contrast to most previous works, all six joints are controlled simultaneously to realize a handguided motion of the whole robot. To this aim, we present a modular control framework that allows for seamlessly switching between simulated and real hardware.
{"title":"Full 6-DOF Admittance Control for the Industrial Robot Stäubli TX60","authors":"Sven Tittel, M. Malekzadeh, Jochen J. Steil","doi":"10.1109/COASE.2019.8843128","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843128","url":null,"abstract":"Human robot interaction (HRI) is a major research field in robotics with significant progress over the last decades. While most HRI is focused on novel light weight robots, we here present an admittance control implementation for the 6-DOF industrial robot Stäubli TX60. We use only standard and commercially available interfaces, without adding external force sensing, and present a method to estimate joint friction to improve the robot model. In contrast to most previous works, all six joints are controlled simultaneously to realize a handguided motion of the whole robot. To this aim, we present a modular control framework that allows for seamlessly switching between simulated and real hardware.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"102 1","pages":"1450-1455"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80583246","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843098
A. Dagnino
Process industries use complex control systems to control manufacturing operations. Control systems collect a large variety and volume of sensor data that measure processes and equipment functions. Alarms constitute an integral component of data collected by control systems. These alarms are generated when there is a deviation from normal operating conditions in equipment and processes. With large number of alarms potentially occurring in a plant, it is imperative that operators and plant managers focus on the most important alarms and dismiss un-important alarms. This paper discusses a novel approach on how to reduce unimportant alarms in a control system and how to show operators the most important alarms using Sequence Data Mining and Market Basket Analysis concepts. These approaches help reduce the number of unimportant alarms and highlight alarms that can lead to expensive breakdowns or potential accidents.
{"title":"Data Mining Methods to Analyze Alarm Logs in IoT Process Control Systems","authors":"A. Dagnino","doi":"10.1109/COASE.2019.8843098","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843098","url":null,"abstract":"Process industries use complex control systems to control manufacturing operations. Control systems collect a large variety and volume of sensor data that measure processes and equipment functions. Alarms constitute an integral component of data collected by control systems. These alarms are generated when there is a deviation from normal operating conditions in equipment and processes. With large number of alarms potentially occurring in a plant, it is imperative that operators and plant managers focus on the most important alarms and dismiss un-important alarms. This paper discusses a novel approach on how to reduce unimportant alarms in a control system and how to show operators the most important alarms using Sequence Data Mining and Market Basket Analysis concepts. These approaches help reduce the number of unimportant alarms and highlight alarms that can lead to expensive breakdowns or potential accidents.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"7 1","pages":"323-330"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82965132","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843074
Christopher Robinson, Indika B. Wijayasinghe, D. Popa
As robotic agents become increasingly present in human environments, task completion rates during human-robot interaction is an important topic of research. Safe collaborative robots executing tasks under human supervision often augment their perception and planning capabilities through traded or shared control schemes. In this paper, we present a quantitatively defined model for sliding-scale autonomy, in which levels of autonomy are determined by the relative efficacy of autonomous subroutines. We experimentally test the resulting Variable Autonomy Planning (VAP) algorithm against a traditional traded control scheme in a pick-and-place task. Results show that prioritizing autonomy levels with higher success rates as encoded into VAP, allows users to effectively and intuitively select optimal autonomy levels for efficient task completion.
{"title":"Quantitative Variable Autonomy Levels for Traded Control in a Pick-and-Place Task","authors":"Christopher Robinson, Indika B. Wijayasinghe, D. Popa","doi":"10.1109/COASE.2019.8843074","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843074","url":null,"abstract":"As robotic agents become increasingly present in human environments, task completion rates during human-robot interaction is an important topic of research. Safe collaborative robots executing tasks under human supervision often augment their perception and planning capabilities through traded or shared control schemes. In this paper, we present a quantitatively defined model for sliding-scale autonomy, in which levels of autonomy are determined by the relative efficacy of autonomous subroutines. We experimentally test the resulting Variable Autonomy Planning (VAP) algorithm against a traditional traded control scheme in a pick-and-place task. Results show that prioritizing autonomy levels with higher success rates as encoded into VAP, allows users to effectively and intuitively select optimal autonomy levels for efficient task completion.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"56 1","pages":"697-702"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89261448","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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