Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551484
Christian Eymüller, Julian Hanke, A. Hoffmann, W. Reif, Markus Kugelmann, Florian Grätz
The industry of tomorrow is changing from central hierarchical industrial and robot controls to distributed controls on the industrial shop floor. These fundamental changes in network structure make it possible to implement technologies such as Plug & Produce. In other words, to integrate, change and remove devices without much effort at runtime. In order to achieve this goal, a uniform architecture with defined interfaces is necessary to establish real-time communication between the varying devices. Therefore, we propose an approach to use the combination of OPC UA and TSN to automatically configure real-time capable communication paths between robots and other cyber-physical components and execute real-time critical tasks in the distributed control system.
{"title":"RealCaPP: Real-time capable Plug & Produce communication platform with OPC UA over TSN for distributed industrial robot control","authors":"Christian Eymüller, Julian Hanke, A. Hoffmann, W. Reif, Markus Kugelmann, Florian Grätz","doi":"10.1109/CASE49439.2021.9551484","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551484","url":null,"abstract":"The industry of tomorrow is changing from central hierarchical industrial and robot controls to distributed controls on the industrial shop floor. These fundamental changes in network structure make it possible to implement technologies such as Plug & Produce. In other words, to integrate, change and remove devices without much effort at runtime. In order to achieve this goal, a uniform architecture with defined interfaces is necessary to establish real-time communication between the varying devices. Therefore, we propose an approach to use the combination of OPC UA and TSN to automatically configure real-time capable communication paths between robots and other cyber-physical components and execute real-time critical tasks in the distributed control system.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127417538","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551492
Kenneth Ezukwoke, H. Toubakh, Anis Hoayek, M. Batton-Hubert, X. Boucher, Pascal Gounet
Microelectronics production failure analysis is a time-consuming and complicated task involving successive steps of analysis of complex process chains. The analysis is triggered to find the root cause of a failure and its findings, recorded in a reporting system using natural language. Fault analysis, physical analysis, sample preparation and package construction analysis are arguably the most used analysis activity for determining the root-cause of a failure. Intelligent automation of this analysis decision process using artificial intelligence is the objective of the FA 4.0 consortium; creating a reliable and efficient semiconductor industry. This research presents natural language processing (NLP) techniques to find a coherent representation of the expert decisions during fault analysis. The adopted methodology is a Deep learning algorithm based on $beta$-variational autoencoder ($beta$-VAE) for latent space disentanglement and Gaussian Mixture Model for clustering of the latent space for class identification.
{"title":"Intelligent Fault Analysis Decision Flow in Semiconductor Industry 4.0 Using Natural Language Processing with Deep Clustering","authors":"Kenneth Ezukwoke, H. Toubakh, Anis Hoayek, M. Batton-Hubert, X. Boucher, Pascal Gounet","doi":"10.1109/CASE49439.2021.9551492","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551492","url":null,"abstract":"Microelectronics production failure analysis is a time-consuming and complicated task involving successive steps of analysis of complex process chains. The analysis is triggered to find the root cause of a failure and its findings, recorded in a reporting system using natural language. Fault analysis, physical analysis, sample preparation and package construction analysis are arguably the most used analysis activity for determining the root-cause of a failure. Intelligent automation of this analysis decision process using artificial intelligence is the objective of the FA 4.0 consortium; creating a reliable and efficient semiconductor industry. This research presents natural language processing (NLP) techniques to find a coherent representation of the expert decisions during fault analysis. The adopted methodology is a Deep learning algorithm based on $beta$-variational autoencoder ($beta$-VAE) for latent space disentanglement and Gaussian Mixture Model for clustering of the latent space for class identification.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129943449","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551423
S. Rastegarpour, L. Ferrarini
This paper studies the problem of performance improvement and energy consumption reduction of the heating, ventilation and air conditioning system of a large-scale university building through the application of nonlinear predictive control strategies concerning also practical and implementation issues. The system consists of two heat pumps, a water-to-water and an air-to-water type, and two different air handling units, which regulate and circulate air in all thermal zones. In such applications, prediction of the future dynamical behavior of the heat pumps is extremely important to enforce efficiency, but it is also very challenging due to the load dependency and nonlinearity of the coefficient of performances of those heat pumps. On the other hand, another source of potential model mismatch is the nonlinear characterization of the heat transfer coefficients of the AHU induced by variable air and water velocity, which gives rise to a non-trivial nonlinear system. To do so, two nonlinear model predictive control strategies are investigated to deal with many physical constraints and nonlinear problems. Finally, a sensitivity and robustness analysis are performed to highlight the merits, defects and impacts of those control algorithms on the energy performance of the building.
{"title":"Hierarchical Nonlinear MPC for Large Buildings HVAC Optimization","authors":"S. Rastegarpour, L. Ferrarini","doi":"10.1109/CASE49439.2021.9551423","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551423","url":null,"abstract":"This paper studies the problem of performance improvement and energy consumption reduction of the heating, ventilation and air conditioning system of a large-scale university building through the application of nonlinear predictive control strategies concerning also practical and implementation issues. The system consists of two heat pumps, a water-to-water and an air-to-water type, and two different air handling units, which regulate and circulate air in all thermal zones. In such applications, prediction of the future dynamical behavior of the heat pumps is extremely important to enforce efficiency, but it is also very challenging due to the load dependency and nonlinearity of the coefficient of performances of those heat pumps. On the other hand, another source of potential model mismatch is the nonlinear characterization of the heat transfer coefficients of the AHU induced by variable air and water velocity, which gives rise to a non-trivial nonlinear system. To do so, two nonlinear model predictive control strategies are investigated to deal with many physical constraints and nonlinear problems. Finally, a sensitivity and robustness analysis are performed to highlight the merits, defects and impacts of those control algorithms on the energy performance of the building.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130033218","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551628
Kaiyuan Chen, Yafei Liang, Nikhil Jha, Jeffrey Ichnowski, Michael Danielczuk, Joseph E. Gonzalez, J. Kubiatowicz, Ken Goldberg
As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource for systems that do not contain high computing power onboard. Despite its immense computing capacity, it is often underused by the robotics and automation community due to lack of expertise in cloud computing and cloud-based infrastructure. Fog Robotics balances computing and data between cloud edge devices. We propose a software framework, FogROS, as an extension of the Robot Operating System (ROS), the defacto standard for creating robot automation applications and components. It allows researchers to deploy components of their software to the cloud with minimal effort, and correspondingly gain access to additional computing cores, GPUs, FPGAs, and TPUs, as well as predeployed software made available by other researchers. FogROS allows a researcher to specify which components of their software will be deployed to the cloud and to what type of computing hardware. We evaluate FogROS on 3 examples: (1) simultaneous localization and mapping (ORB-SLAM2), (2) Dexterity Network (Dex-Net) GPU-based grasp planning, and (3) multi-core motion planning using a 96-core cloud-based server. In all three examples, a component is deployed to the cloud and accelerated with a small change in system launch configuration, while incurring additional latency of 1.2 s, 0.6 s, and 0.5 s due to network communication, the computation speed is improved by 2.6×, 6.0× and 34.2×, respectively. Code, videos, and supplementary material can be found at https://github.com/BerkeleyAutomation/FogROS.
{"title":"FogROS: An Adaptive Framework for Automating Fog Robotics Deployment","authors":"Kaiyuan Chen, Yafei Liang, Nikhil Jha, Jeffrey Ichnowski, Michael Danielczuk, Joseph E. Gonzalez, J. Kubiatowicz, Ken Goldberg","doi":"10.1109/CASE49439.2021.9551628","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551628","url":null,"abstract":"As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource for systems that do not contain high computing power onboard. Despite its immense computing capacity, it is often underused by the robotics and automation community due to lack of expertise in cloud computing and cloud-based infrastructure. Fog Robotics balances computing and data between cloud edge devices. We propose a software framework, FogROS, as an extension of the Robot Operating System (ROS), the defacto standard for creating robot automation applications and components. It allows researchers to deploy components of their software to the cloud with minimal effort, and correspondingly gain access to additional computing cores, GPUs, FPGAs, and TPUs, as well as predeployed software made available by other researchers. FogROS allows a researcher to specify which components of their software will be deployed to the cloud and to what type of computing hardware. We evaluate FogROS on 3 examples: (1) simultaneous localization and mapping (ORB-SLAM2), (2) Dexterity Network (Dex-Net) GPU-based grasp planning, and (3) multi-core motion planning using a 96-core cloud-based server. In all three examples, a component is deployed to the cloud and accelerated with a small change in system launch configuration, while incurring additional latency of 1.2 s, 0.6 s, and 0.5 s due to network communication, the computation speed is improved by 2.6×, 6.0× and 34.2×, respectively. Code, videos, and supplementary material can be found at https://github.com/BerkeleyAutomation/FogROS.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":" 32","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132074317","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551546
Kyshalee Vazquez-Santiago, C. Goh, K. Shimada
Motion planning for kinematic redundancy is an area of great importance for maximizing the mobility of robotic systems. However, generating optimized motions for this type of system is a challenging task given the large search space of possible configurations. Previously proposed methods do not address path following tasks with constrained end-effector position and orientation for a mobile manipulator system with more than 6 degrees of freedom (DoF). This paper presents a novel computational method for simultaneous optimization of base and manipulator robotic system with 8 DoF for welding tasks, constraining both end-effector position and orientation. The mobile manipulator consists of a 2 DoF non-holonomic base and a 6 DoF manipulator. The proposed method applies a Genetic Algorithm (GA) to solve for optimized configurations for the base and manipulator for strategically sampled end-effector waypoints. The base configurations and end-effector orientations are interpolated between the GA solutions and used as inputs for an inverse kinematics solver to find the optimal manipulator pose. The experiment results show that the proposed methods create optimized smooth and continuous motions for both the base and manipulator while constraining the end-effector position and orientation. The proposed method is a novel application of GA optimization, with improved results for path following motion planning by including sampling, interpolation, and inverse kinematics steps within the methodology.
{"title":"Motion Planning for Kinematically Redundant Mobile Manipulators with Genetic Algorithm, Pose Interpolation, and Inverse Kinematics","authors":"Kyshalee Vazquez-Santiago, C. Goh, K. Shimada","doi":"10.1109/CASE49439.2021.9551546","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551546","url":null,"abstract":"Motion planning for kinematic redundancy is an area of great importance for maximizing the mobility of robotic systems. However, generating optimized motions for this type of system is a challenging task given the large search space of possible configurations. Previously proposed methods do not address path following tasks with constrained end-effector position and orientation for a mobile manipulator system with more than 6 degrees of freedom (DoF). This paper presents a novel computational method for simultaneous optimization of base and manipulator robotic system with 8 DoF for welding tasks, constraining both end-effector position and orientation. The mobile manipulator consists of a 2 DoF non-holonomic base and a 6 DoF manipulator. The proposed method applies a Genetic Algorithm (GA) to solve for optimized configurations for the base and manipulator for strategically sampled end-effector waypoints. The base configurations and end-effector orientations are interpolated between the GA solutions and used as inputs for an inverse kinematics solver to find the optimal manipulator pose. The experiment results show that the proposed methods create optimized smooth and continuous motions for both the base and manipulator while constraining the end-effector position and orientation. The proposed method is a novel application of GA optimization, with improved results for path following motion planning by including sampling, interpolation, and inverse kinematics steps within the methodology.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"34 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132262658","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551452
Máté Fazekas, P. Gáspár, B. Németh
The motion estimation of a self-driving car has to be as accurate as possible for proper control and safe driving. Therefore, the GNSS, IMU, or perception-based methods should be improved, e.g. with the integration of the wheel motion. This method is robust and cost-effective, but the calibration of the model parameters behind the wheel-based odometry is difficult. It is resulted from the nonlinear dynamics of the system and the requirement of parameter estimation with high precision, which is an open problem in the presence of noises yet. This paper proposes a novel architecture that simultaneously detects the faulty measurement segments, which results in biased parameter estimation. Furthermore, the measurements utilized for the calibration are also corrected to improve the efficiency of the parameter estimation. With the algorithm, the distortion effects of the noises can be eliminated, and accurate calibration of the nonlinear wheel odometry model can be obtained. The effectiveness of the detection and pose correction techniques, and the operation of the calibration process are illustrated through vehicle test experiments.
{"title":"Improving the wheel odometry calibration of self-driving vehicles via detection of faulty segments","authors":"Máté Fazekas, P. Gáspár, B. Németh","doi":"10.1109/CASE49439.2021.9551452","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551452","url":null,"abstract":"The motion estimation of a self-driving car has to be as accurate as possible for proper control and safe driving. Therefore, the GNSS, IMU, or perception-based methods should be improved, e.g. with the integration of the wheel motion. This method is robust and cost-effective, but the calibration of the model parameters behind the wheel-based odometry is difficult. It is resulted from the nonlinear dynamics of the system and the requirement of parameter estimation with high precision, which is an open problem in the presence of noises yet. This paper proposes a novel architecture that simultaneously detects the faulty measurement segments, which results in biased parameter estimation. Furthermore, the measurements utilized for the calibration are also corrected to improve the efficiency of the parameter estimation. With the algorithm, the distortion effects of the noises can be eliminated, and accurate calibration of the nonlinear wheel odometry model can be obtained. The effectiveness of the detection and pose correction techniques, and the operation of the calibration process are illustrated through vehicle test experiments.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132270110","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}
Optimization and parameter adjustment of an injection molding (IM) process depend largely on a good modelling and prediction of industrial process, which has been received considerable attention in recent years. However, IM process is a typical multivariate production process with multiple product quality indices. It poses a great challenge for multi-output quality prediction problem to select key process variables as input with good interpretability. This study proposes a multivariate quality prediction method for IM process using copula entropy (CE) and multi-output support vector regression (MSVR). First, copula entropy is employed to characterize the association relationships between each process variable and the set of quality indices, thus key process variables can be selected by ranking CE. Then, the quantitative relationship between key process variables and quality indices is established by MSVR. Finally, the proposed method is tested by the experiment on a real IM process dataset. This study will provide an important reference for modelling and prediction of IM process and other multi-output problems.
{"title":"Modelling and Prediction of Injection Molding Process Using Copula Entropy and Multi-Output SVR","authors":"Yanning Sun, Yu Chen, Wu-Yin Wang, Hongwei Xu, Wei Qin","doi":"10.1109/CASE49439.2021.9551391","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551391","url":null,"abstract":"Optimization and parameter adjustment of an injection molding (IM) process depend largely on a good modelling and prediction of industrial process, which has been received considerable attention in recent years. However, IM process is a typical multivariate production process with multiple product quality indices. It poses a great challenge for multi-output quality prediction problem to select key process variables as input with good interpretability. This study proposes a multivariate quality prediction method for IM process using copula entropy (CE) and multi-output support vector regression (MSVR). First, copula entropy is employed to characterize the association relationships between each process variable and the set of quality indices, thus key process variables can be selected by ranking CE. Then, the quantitative relationship between key process variables and quality indices is established by MSVR. Finally, the proposed method is tested by the experiment on a real IM process dataset. This study will provide an important reference for modelling and prediction of IM process and other multi-output problems.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130971192","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551403
Yurong Zhong, Xin Luo
Large-scale undirected weighted networks are frequently encountered in real applications. They can be described by a Symmetric, High-Dimensional and Sparse (SHiDS) matrix, whose sparse and symmetric data should be addressed with care. However, existing models either fail to handle its sparsity effectively, or fail to correctly describe its symmetry. For addressing these issues, this study proposes an Alternating-direction-method-of-multipliers-based Symmetric Nonnegative Latent Factor Analysis (ASNL) model. Its main idea is three-fold: 1) introducing an equality constraint into a data density-oriented learning objective for a flexible and effective learning process; 2) confining an augmented term to be data density-oriented to enhance generalization the model's ability; and 3) utilizing the principle of alternating-direction-method of multipliers to divide a complex optimization task into multiple simple subtasks, each of which is solved based on the results of previously solved ones. Empirical studies on two SHiDS matrices demonstrate that ASNL obtains higher prediction accuracy for their missing data than state-of-the-art models with competitive computational efficiency.
{"title":"Alternating-direction-method of Multipliers-Based Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks","authors":"Yurong Zhong, Xin Luo","doi":"10.1109/CASE49439.2021.9551403","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551403","url":null,"abstract":"Large-scale undirected weighted networks are frequently encountered in real applications. They can be described by a Symmetric, High-Dimensional and Sparse (SHiDS) matrix, whose sparse and symmetric data should be addressed with care. However, existing models either fail to handle its sparsity effectively, or fail to correctly describe its symmetry. For addressing these issues, this study proposes an Alternating-direction-method-of-multipliers-based Symmetric Nonnegative Latent Factor Analysis (ASNL) model. Its main idea is three-fold: 1) introducing an equality constraint into a data density-oriented learning objective for a flexible and effective learning process; 2) confining an augmented term to be data density-oriented to enhance generalization the model's ability; and 3) utilizing the principle of alternating-direction-method of multipliers to divide a complex optimization task into multiple simple subtasks, each of which is solved based on the results of previously solved ones. Empirical studies on two SHiDS matrices demonstrate that ASNL obtains higher prediction accuracy for their missing data than state-of-the-art models with competitive computational efficiency.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130273623","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551553
Francesca Palermo, Liz Katherine Rincon Ardila, Changjae Oh, K. Althoefer, S. Poslad, G. Venture, I. Farkhatdinov
We present and validate a method to detect surface cracks with visual and tactile sensing. The proposed algorithm localises cracks in remote environments through videos/photos taken by an on-board robot camera. The identified areas of interest are then explored by a robot with a tactile sensor. Faster R-CNN object detection is used for identifying the location of potential cracks. Random forest classifier is used for tactile identification of the cracks to confirm their presences. Offline and online experiments to compare vision only and combined vision and tactile based crack detection are demonstrated. Two experiments are developed to test the efficiency of the multi-modal approach: online accuracy detection and time required to explore a surface and localise a crack. Exploring a cracked surface using combined visual and tactile modalities required four times less time than using the tactile modality only. The accuracy of detection was also improved with the combination of the two modalities. This approach may be implemented also in extreme environments since gamma radiation does not interfere with the sensing mechanism of fibre optic-based sensors.
{"title":"Multi-modal robotic visual-tactile localisation and detection of surface cracks","authors":"Francesca Palermo, Liz Katherine Rincon Ardila, Changjae Oh, K. Althoefer, S. Poslad, G. Venture, I. Farkhatdinov","doi":"10.1109/CASE49439.2021.9551553","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551553","url":null,"abstract":"We present and validate a method to detect surface cracks with visual and tactile sensing. The proposed algorithm localises cracks in remote environments through videos/photos taken by an on-board robot camera. The identified areas of interest are then explored by a robot with a tactile sensor. Faster R-CNN object detection is used for identifying the location of potential cracks. Random forest classifier is used for tactile identification of the cracks to confirm their presences. Offline and online experiments to compare vision only and combined vision and tactile based crack detection are demonstrated. Two experiments are developed to test the efficiency of the multi-modal approach: online accuracy detection and time required to explore a surface and localise a crack. Exploring a cracked surface using combined visual and tactile modalities required four times less time than using the tactile modality only. The accuracy of detection was also improved with the combination of the two modalities. This approach may be implemented also in extreme environments since gamma radiation does not interfere with the sensing mechanism of fibre optic-based sensors.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436484","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551549
Raunak Sengupta, R. Nagi
We consider a set of indivisible operations and a set of uniformly related agents, i.e., agents with different speeds. Our aim is to develop a task allocation algorithm that minimizes the makespan in a decentralized manner. To achieve this, we first present the Operation Trading Algorithm. We show that the algorithm guarantees a worst case approximation factor of 1.618 for the 2 agent case and $frac{1+sqrt{4n-3}}{2}$ for the general n agent case. Further, we prove that the algorithm guarantees a near-optimal makespan for real-life scenarios with large number of operations under the assumption of a fully connected network of agents. The algorithm also guarantees an approximation factor less than 2 for any number of identical agents. Following this, we present a Decentralized random Group Formation protocol which enables the agents to implement OTA(n) in a decentralized manner in presence of communication failures. Finally, using numerical results, we show that the algorithm generates near optimal allocations even in the presence of communication failures. Additionally, the algorithm is parameter free and allows fast re-planning, making it robust to machine failures and changes in the environment.
{"title":"Decentralized Makespan Minimization for Uniformly Related Agents","authors":"Raunak Sengupta, R. Nagi","doi":"10.1109/CASE49439.2021.9551549","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551549","url":null,"abstract":"We consider a set of indivisible operations and a set of uniformly related agents, i.e., agents with different speeds. Our aim is to develop a task allocation algorithm that minimizes the makespan in a decentralized manner. To achieve this, we first present the Operation Trading Algorithm. We show that the algorithm guarantees a worst case approximation factor of 1.618 for the 2 agent case and $frac{1+sqrt{4n-3}}{2}$ for the general n agent case. Further, we prove that the algorithm guarantees a near-optimal makespan for real-life scenarios with large number of operations under the assumption of a fully connected network of agents. The algorithm also guarantees an approximation factor less than 2 for any number of identical agents. Following this, we present a Decentralized random Group Formation protocol which enables the agents to implement OTA(n) in a decentralized manner in presence of communication failures. Finally, using numerical results, we show that the algorithm generates near optimal allocations even in the presence of communication failures. Additionally, the algorithm is parameter free and allows fast re-planning, making it robust to machine failures and changes in the environment.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125345386","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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