Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551554
Yukun Jiang, Chang-Ray Chen, Xiaojun Liu
With the increasing complexity of mechanical products, assembly, as the last step of product manufacturing, is becoming more and more complicated. Therefore, the problems in the actual assembly process are difficult to be considered at the beginning of the design. In addition, the information measured at assembly site is also very important for the improvement of the assembly process design. The emergence of digital twin solves this problem well and provides the possibility for the transmission and feedback of assembly site information. However, the current digital twin data storage side mainly uses the traditional database, which leads to data redundancy. In recent years, the popular knowledge graph has powerful knowledge representation and reasoning ability, which can solve the above problems. In this paper, a digital twin system structure of assembly process based on knowledge graph is proposed, which is used to record actual process data. It provides a possibility for the combination of knowledge graph technology and digital twin technology.
{"title":"Assembly Process Knowledge Graph for Digital Twin","authors":"Yukun Jiang, Chang-Ray Chen, Xiaojun Liu","doi":"10.1109/CASE49439.2021.9551554","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551554","url":null,"abstract":"With the increasing complexity of mechanical products, assembly, as the last step of product manufacturing, is becoming more and more complicated. Therefore, the problems in the actual assembly process are difficult to be considered at the beginning of the design. In addition, the information measured at assembly site is also very important for the improvement of the assembly process design. The emergence of digital twin solves this problem well and provides the possibility for the transmission and feedback of assembly site information. However, the current digital twin data storage side mainly uses the traditional database, which leads to data redundancy. In recent years, the popular knowledge graph has powerful knowledge representation and reasoning ability, which can solve the above problems. In this paper, a digital twin system structure of assembly process based on knowledge graph is proposed, which is used to record actual process data. It provides a possibility for the combination of knowledge graph technology and digital twin technology.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"47 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":"132584240","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/CASE49439.2021.9551592
Giulia Pedrielli, Hao Huang, Z. Zabinsky
Manufacturing, aerospace, energy and several other industries have witnessed a steep growth of increasingly complex, information rich, devices and systems of devices requiring simulation-based approaches. In fact, most modern systems have such complex behavior that their performance can only be evaluated through, usually computationally expensive, simulations. In such settings, it is of paramount importance to provide solutions with quality guarantees. In this manuscript, we focus on algorithms capable of identifying a level set of solutions in proximity of the global optimum, and specifically on the Probabilistic Branch and Bound (PBnB) method. We propose a new way to automate branching decisions by coupling this method with Gaussian process (GP) estimation. The result is PBnB-GP, where, at each iteration a collection of GPs is used to decide how to branch the input space. PBnB-GP not only returns an estimate of the regions with near-optimal reward (using the power of PBnB), but also a “collection of Gaussian processes” that can produce point estimations for any location in the input space, thus harnessing the power of model-based black-box optimization. We present PBnB-GP for the first time together with preliminary numerical results.
{"title":"Using Gaussian Processes to Automate Probabilistic Branch & Bound for Global Optimization","authors":"Giulia Pedrielli, Hao Huang, Z. Zabinsky","doi":"10.1109/CASE49439.2021.9551592","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551592","url":null,"abstract":"Manufacturing, aerospace, energy and several other industries have witnessed a steep growth of increasingly complex, information rich, devices and systems of devices requiring simulation-based approaches. In fact, most modern systems have such complex behavior that their performance can only be evaluated through, usually computationally expensive, simulations. In such settings, it is of paramount importance to provide solutions with quality guarantees. In this manuscript, we focus on algorithms capable of identifying a level set of solutions in proximity of the global optimum, and specifically on the Probabilistic Branch and Bound (PBnB) method. We propose a new way to automate branching decisions by coupling this method with Gaussian process (GP) estimation. The result is PBnB-GP, where, at each iteration a collection of GPs is used to decide how to branch the input space. PBnB-GP not only returns an estimate of the regions with near-optimal reward (using the power of PBnB), but also a “collection of Gaussian processes” that can produce point estimations for any location in the input space, thus harnessing the power of model-based black-box optimization. We present PBnB-GP for the first time together with preliminary numerical results.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"45 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":"126582224","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}
As an essential operation, spindle acceleration occurs frequently in the machining process, the energy consumption of which has an important impact on the energy efficiency of machine tools, cannot be ignored. However, due to its energy characteristics of short duration, high power peak and complex electromechanical operating of the spindle motor, the energy consumption of the spindle acceleration process is difficult to calculate accurately. To fill this gap, a data-driven method for machine tool spindle acceleration energy prediction is proposed in this paper. Firstly, the energy characteristics of spindle acceleration are studied, and a dataset for the energy prediction is determined. Secondly, an automatic extraction algorithm is developed to extract the time data of power peak, and then a framework for data collection and preprocessing is proposed. Thirdly, a spindle acceleration energy prediction model is established with Back-propagation Neural Network based on the Genetic Algorithm (GA-BP), and the network structure and the operation process are also studied. Finally, a case study of spindle acceleration is given to verify the validity of the proposed approach and model, and the accuracy is also verified with other algorithms.
{"title":"Data-driven method for predicting energy consumption of machine tool spindle acceleration","authors":"Binbin Huang, Guozhang Jiang, W. Yan, Zhigang Jiang, Chenxun Lu, Hua Zhang","doi":"10.1109/CASE49439.2021.9551682","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551682","url":null,"abstract":"As an essential operation, spindle acceleration occurs frequently in the machining process, the energy consumption of which has an important impact on the energy efficiency of machine tools, cannot be ignored. However, due to its energy characteristics of short duration, high power peak and complex electromechanical operating of the spindle motor, the energy consumption of the spindle acceleration process is difficult to calculate accurately. To fill this gap, a data-driven method for machine tool spindle acceleration energy prediction is proposed in this paper. Firstly, the energy characteristics of spindle acceleration are studied, and a dataset for the energy prediction is determined. Secondly, an automatic extraction algorithm is developed to extract the time data of power peak, and then a framework for data collection and preprocessing is proposed. Thirdly, a spindle acceleration energy prediction model is established with Back-propagation Neural Network based on the Genetic Algorithm (GA-BP), and the network structure and the operation process are also studied. Finally, a case study of spindle acceleration is given to verify the validity of the proposed approach and model, and the accuracy is also verified with other algorithms.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"254 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":"134457237","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/CASE49439.2021.9551681
Shibiao Shao, F. Gao, Jiang Wu, Q. Zhai, X. Tian
In order to overcome the shortcoming that the dual distributed sub-gradient optimization methods need to construct a feasible solution, a novel distributed sub-gradient optimization method based on primal decomposition is proposed in this paper and used to solve the joint dynamic economic dispatch (JDED) problem of multi-area interconnected power systems (MAIPSs). Firstly, the centralized optimization model is established and decomposed into multiple independent local areas' optimization and a global coordinator's optimization by splitting area power grids and cross-area tie-lines. Moreover, the slack variables and corresponding penalties are introduced into the local optimization to ensure feasibility and optimality. Secondly, a distributed sub-gradient optimization method is proposed to solve the decomposed model, in which the sub-gradient is calculated by using the dual multipliers from local optimization. Furthermore, in order to get better convergence, the heuristic updating rules for step size and penalty factor are designed. Finally, the numerical tests are carried out on two interconnected systems of different scales, and results show that the proposed method can obtain a good feasible solution directly and has high computational efficiency.
{"title":"A Distributed Sub-Gradient Optimal Scheduling Method Based on Primal Decomposition with Application to Multi-Area Interconnected Power Systems","authors":"Shibiao Shao, F. Gao, Jiang Wu, Q. Zhai, X. Tian","doi":"10.1109/CASE49439.2021.9551681","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551681","url":null,"abstract":"In order to overcome the shortcoming that the dual distributed sub-gradient optimization methods need to construct a feasible solution, a novel distributed sub-gradient optimization method based on primal decomposition is proposed in this paper and used to solve the joint dynamic economic dispatch (JDED) problem of multi-area interconnected power systems (MAIPSs). Firstly, the centralized optimization model is established and decomposed into multiple independent local areas' optimization and a global coordinator's optimization by splitting area power grids and cross-area tie-lines. Moreover, the slack variables and corresponding penalties are introduced into the local optimization to ensure feasibility and optimality. Secondly, a distributed sub-gradient optimization method is proposed to solve the decomposed model, in which the sub-gradient is calculated by using the dual multipliers from local optimization. Furthermore, in order to get better convergence, the heuristic updating rules for step size and penalty factor are designed. Finally, the numerical tests are carried out on two interconnected systems of different scales, and results show that the proposed method can obtain a good feasible solution directly and has high computational efficiency.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"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":"115528352","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}
We present an automated assembly approach to forming 3D mesostructures using guided mechanical buckling of patterned thin films. This task requires accurate positioning of mesostructures over large distances. We use an industrial robot with a high degree of repeatability and large reach. We utilize image-guided localization and positioning to enable accurate pick and place of mesoscale thin films, dispensing of nanoliter adhesive in targeted regions, and automatic 2D to 3D shape transformation via mechanical buckling. We achieved the positioning accuracy of 80 µm, as demonstrated in the example of automated mechanical assembly of 3D mesostructures. The positioning accuracy could be further improved by enhancing the positioning accuracy of the robot, increasing the image resolution and optimizing the assembly process. The use of industrial robots with image-guided localization and positioning provides potential opportunities for high-accuracy, low-cost, and complex robotic manipulation at meso- and microscale.
{"title":"Automated Robotic Assembly of 3D Mesostructure via Guided Mechanical Buckling","authors":"Ying Cai, Zhonghao Han, Trey Cranney, Hangbo Zhao, Satyandra K. Gupta","doi":"10.1109/CASE49439.2021.9551609","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551609","url":null,"abstract":"We present an automated assembly approach to forming 3D mesostructures using guided mechanical buckling of patterned thin films. This task requires accurate positioning of mesostructures over large distances. We use an industrial robot with a high degree of repeatability and large reach. We utilize image-guided localization and positioning to enable accurate pick and place of mesoscale thin films, dispensing of nanoliter adhesive in targeted regions, and automatic 2D to 3D shape transformation via mechanical buckling. We achieved the positioning accuracy of 80 µm, as demonstrated in the example of automated mechanical assembly of 3D mesostructures. The positioning accuracy could be further improved by enhancing the positioning accuracy of the robot, increasing the image resolution and optimizing the assembly process. The use of industrial robots with image-guided localization and positioning provides potential opportunities for high-accuracy, low-cost, and complex robotic manipulation at meso- and microscale.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"18 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":"114603608","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/CASE49439.2021.9551429
Vincent Lequertier, Tao Wang, J. Fondrevelle, V. Augusto, S. Polazzi, A. Duclos
Hospital beds management is critical for the quality of patient care, while length of inpatient stay is often estimated empirically by physicians or chief nurses of medical wards. Providing an efficient method for forecasting the length of stay (LOS) is expected to improve resources and discharges planning. Predictions should be accurate and work for as many patients as possible, despite their heterogeneous profiles. In this work, a LOS prediction method based on deep learning and embeddings is developed by using generic hospital administrative data from a French national hospital discharge database, as well as emergency care. Data concerned 497 626 stays of 304 931 patients from 6 hospitals in Lyon, France, from 2011 to 2019. Results of a 5-fold cross-validation showed an accuracy of 0.73 and a kappa score of 0.67 for the embeddings method. This outperformed the baseline which used the raw input features directly.
{"title":"Predicting length of stay with administrative data from acute and emergency care: an embedding approach","authors":"Vincent Lequertier, Tao Wang, J. Fondrevelle, V. Augusto, S. Polazzi, A. Duclos","doi":"10.1109/CASE49439.2021.9551429","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551429","url":null,"abstract":"Hospital beds management is critical for the quality of patient care, while length of inpatient stay is often estimated empirically by physicians or chief nurses of medical wards. Providing an efficient method for forecasting the length of stay (LOS) is expected to improve resources and discharges planning. Predictions should be accurate and work for as many patients as possible, despite their heterogeneous profiles. In this work, a LOS prediction method based on deep learning and embeddings is developed by using generic hospital administrative data from a French national hospital discharge database, as well as emergency care. Data concerned 497 626 stays of 304 931 patients from 6 hospitals in Lyon, France, from 2011 to 2019. Results of a 5-fold cross-validation showed an accuracy of 0.73 and a kappa score of 0.67 for the embeddings method. This outperformed the baseline which used the raw input features directly.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"40 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":"114722783","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/CASE49439.2021.9551608
Jun Ouyang, Zhigang Jiang, Shuo Zhu
For the uncertainty problem of remanufacturing blanks, an active remanufacturing timing decision method that considers reliability and environmental impact is proposed in this paper. In this method, the reliability of the product in the service stage is firstly used to characterize the change in its quality. In addition, an improved average rank method is proposed to improve the accuracy of reliability prediction, so as to preliminarily determine the time range of active remanufacturing. Then, the environmental impact of the whole life cycle of used products is quantitatively analyzed, the function of average annual energy consumption and annual waste discharge are applied as indicators. The multi-objective optimization problem is solved with genetic algorithm (GA), and the best time for active remanufacturing is determined. A case study on remanufacturing a used engine is demonstrated to validate the proposed method.
{"title":"A Timing Decision-making Method for Active Remanufacturing Considering Reliability and Environmental Impact","authors":"Jun Ouyang, Zhigang Jiang, Shuo Zhu","doi":"10.1109/CASE49439.2021.9551608","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551608","url":null,"abstract":"For the uncertainty problem of remanufacturing blanks, an active remanufacturing timing decision method that considers reliability and environmental impact is proposed in this paper. In this method, the reliability of the product in the service stage is firstly used to characterize the change in its quality. In addition, an improved average rank method is proposed to improve the accuracy of reliability prediction, so as to preliminarily determine the time range of active remanufacturing. Then, the environmental impact of the whole life cycle of used products is quantitatively analyzed, the function of average annual energy consumption and annual waste discharge are applied as indicators. The multi-objective optimization problem is solved with genetic algorithm (GA), and the best time for active remanufacturing is determined. A case study on remanufacturing a used engine is demonstrated to validate the proposed method.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"9 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":"116897544","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/CASE49439.2021.9551589
Yiruo Lu, Yongpei Guan, Xiang Zhong, J. Fishe, T. Hogan
Health care systems are at the front line to fight the COVID-19 pandemic. Emergent questions for each hospital are how many general ward and intensive care unit beds are needed, and additionally, how to optimally allocate these resources during demand surge to effectively save lives. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a hybrid computer simulation approach, with a system dynamic model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization and subsequently determine bed allocations. Two control policies, the type-dependent admission control policy and the early step-down policy, based on patient risk profiling, were proposed to lower the overall death rate of the patient population in need of intensive care. The model was validated using historical patient census data from the University of Florida Health Jacksonville, Jacksonville, FL. The allocation of hospital beds to low-risk and high-risk arrival patients to achieve the goal of reducing the death rate, while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge.
{"title":"Hospital Beds Planning and Admission Control Policies for COVID-19 Pandemic: A Hybrid Computer Simulation Approach","authors":"Yiruo Lu, Yongpei Guan, Xiang Zhong, J. Fishe, T. Hogan","doi":"10.1109/CASE49439.2021.9551589","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551589","url":null,"abstract":"Health care systems are at the front line to fight the COVID-19 pandemic. Emergent questions for each hospital are how many general ward and intensive care unit beds are needed, and additionally, how to optimally allocate these resources during demand surge to effectively save lives. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a hybrid computer simulation approach, with a system dynamic model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization and subsequently determine bed allocations. Two control policies, the type-dependent admission control policy and the early step-down policy, based on patient risk profiling, were proposed to lower the overall death rate of the patient population in need of intensive care. The model was validated using historical patient census data from the University of Florida Health Jacksonville, Jacksonville, FL. The allocation of hospital beds to low-risk and high-risk arrival patients to achieve the goal of reducing the death rate, while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"40 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120855597","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/CASE49439.2021.9551586
Shiming Liu, S. Hennequin, Daniel M. Roy
This paper presents a part of an application platform dedicated to resources sharing between several enterprises. The physical resources are administered by an Industrial Internet of Things platform (IIoT) and a consortium blockchain platform. The interactions between enterprises and physical resources are controlled with the help of a multi-agent system. The blockchain platform allows decentralizing activities and management whereas the cloud architecture-based multi-agent system permits to centralize the management of the resources sharing application platform. In this paper, we describe all tools and more specifically the multi-agent system with the chosen agents and the matching process of resources sharing. We also explain the links between all tools and the functioning of our proposed resources sharing application platform (complete architecture and data exchange).
{"title":"Cloud architecture-based multi-agent system for a resources sharing application platform","authors":"Shiming Liu, S. Hennequin, Daniel M. Roy","doi":"10.1109/CASE49439.2021.9551586","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551586","url":null,"abstract":"This paper presents a part of an application platform dedicated to resources sharing between several enterprises. The physical resources are administered by an Industrial Internet of Things platform (IIoT) and a consortium blockchain platform. The interactions between enterprises and physical resources are controlled with the help of a multi-agent system. The blockchain platform allows decentralizing activities and management whereas the cloud architecture-based multi-agent system permits to centralize the management of the resources sharing application platform. In this paper, we describe all tools and more specifically the multi-agent system with the chosen agents and the matching process of resources sharing. We also explain the links between all tools and the functioning of our proposed resources sharing application platform (complete architecture and data exchange).","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"7 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":"125903555","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/CASE49439.2021.9551489
M. Murtaza, Bruce Wingo, Dan Kilanga, S. Hutchinson
In this paper, we present a distributed optimal control framework for a multi-agent robotics system based on coordinate descent optimization. Our framework exploits the underlying graph topology to compute the optimal control trajectory in a distributed manner. It only requires a modest amount of information exchange among the neighboring robot, and the computation depends on the underlying graph structure connecting the agents. Hence, if the underlying graph topology is sparse, e.g. a line graph, then it scales well with the problem's dimension, and any fast convergent algorithm can be used to ensure real-time computation. To show the efficacy of the framework, we apply it to a problem where a team of robots is tasked with establishing a communication link between source and destination while minimizing the overall system's mobility and communication energy. We analyzed its performance in simulation and on actual robots using an experimental robotic testbed, robotarium [1], and compare it to the centralized solution of the same problem. The results show that the distributed framework converges and outperforms its centralized version as the problem's dimension increases. While the aforementioned energy-balancing problem serves to motivate the paper, the algorithm is defined and presented in a more general setting, and its potential extensions to other types of systems are pointed out.
{"title":"Distributed Optimal Control Framework based on Coordinate Descent Optimization for Multi-Agent Robots","authors":"M. Murtaza, Bruce Wingo, Dan Kilanga, S. Hutchinson","doi":"10.1109/CASE49439.2021.9551489","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551489","url":null,"abstract":"In this paper, we present a distributed optimal control framework for a multi-agent robotics system based on coordinate descent optimization. Our framework exploits the underlying graph topology to compute the optimal control trajectory in a distributed manner. It only requires a modest amount of information exchange among the neighboring robot, and the computation depends on the underlying graph structure connecting the agents. Hence, if the underlying graph topology is sparse, e.g. a line graph, then it scales well with the problem's dimension, and any fast convergent algorithm can be used to ensure real-time computation. To show the efficacy of the framework, we apply it to a problem where a team of robots is tasked with establishing a communication link between source and destination while minimizing the overall system's mobility and communication energy. We analyzed its performance in simulation and on actual robots using an experimental robotic testbed, robotarium [1], and compare it to the centralized solution of the same problem. The results show that the distributed framework converges and outperforms its centralized version as the problem's dimension increases. While the aforementioned energy-balancing problem serves to motivate the paper, the algorithm is defined and presented in a more general setting, and its potential extensions to other types of systems are pointed out.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"47 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":"128684694","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：