Pub Date : 2018-08-01DOI: 10.1109/COASE.2018.8560471
Christian Lieberoth-Leden, J. Fischer, J. Fottner, B. Vogel‐Heuser
The modularization of hard- and software is one approach to handle the demand for increasing flexibility and changeability of automated material flow systems that are, for example, utilized in flexible production systems. In such automated material flow systems, autonomous modules communicate with each other to coordinate and execute transport tasks. The modules are able to detect neighbouring modules and configure interfaces. A control architecture with a central coordination instance is proposed to efficiently communicate topology, state and planning information in a multi-agent material flow system. Furthermore, a planning and scheduling concept for the material flow control is introduced which optimizes traffic and fulfils material flow requirements such as sequencing.
{"title":"Control Architecture and Transport Coordination for Autonomous Logistics Modules in Flexible Automated Material Flow Systems","authors":"Christian Lieberoth-Leden, J. Fischer, J. Fottner, B. Vogel‐Heuser","doi":"10.1109/COASE.2018.8560471","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560471","url":null,"abstract":"The modularization of hard- and software is one approach to handle the demand for increasing flexibility and changeability of automated material flow systems that are, for example, utilized in flexible production systems. In such automated material flow systems, autonomous modules communicate with each other to coordinate and execute transport tasks. The modules are able to detect neighbouring modules and configure interfaces. A control architecture with a central coordination instance is proposed to efficiently communicate topology, state and planning information in a multi-agent material flow system. Furthermore, a planning and scheduling concept for the material flow control is introduced which optimizes traffic and fulfils material flow requirements such as sequencing.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"93 1","pages":"736-743"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79437119","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 : 2018-08-01DOI: 10.1109/COASE.2018.8560464
Lianlian Zhang, F. Qiao, Junkai Wang
With the rapid development of Internet-of-Things and big data, health assessment of equipment has become a hot spot in recent years. It is critical to bridge the gap between real-time factory data and health status evaluation, which helps decide appropriate maintenance time by quantitative fault-early warning. For this purpose, this paper proposes a framework to realize real-time equipment health management. The framework begins with principal component analysis (PCA) for feature reduction and support vector data description (SVDD) method for identifying abnormal observations. To promote the computational efficiency of the static health assessment model, an improved incremental learning SVDD method based on KKT (Karush-Kuhn-Tucker) condition (KISVDD) is proposed. Then health degree (HD) is defined derived from deviation degree (DD) based on Euclidean distance. Subsequently, a fault-early warning threshold setting method based on sliding window is established to realize quantitative maintenance time prediction. Thereafter, the proposed scheme is compared with different types of algorithms in a case study to demonstrate the effectiveness of the proposed model using actual production data. The results show that the proposed model outperforms traditional ones in accuracy and computational efficiency.
{"title":"Equipment health assessment and fault-early warning algorithm based on improved SVDD","authors":"Lianlian Zhang, F. Qiao, Junkai Wang","doi":"10.1109/COASE.2018.8560464","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560464","url":null,"abstract":"With the rapid development of Internet-of-Things and big data, health assessment of equipment has become a hot spot in recent years. It is critical to bridge the gap between real-time factory data and health status evaluation, which helps decide appropriate maintenance time by quantitative fault-early warning. For this purpose, this paper proposes a framework to realize real-time equipment health management. The framework begins with principal component analysis (PCA) for feature reduction and support vector data description (SVDD) method for identifying abnormal observations. To promote the computational efficiency of the static health assessment model, an improved incremental learning SVDD method based on KKT (Karush-Kuhn-Tucker) condition (KISVDD) is proposed. Then health degree (HD) is defined derived from deviation degree (DD) based on Euclidean distance. Subsequently, a fault-early warning threshold setting method based on sliding window is established to realize quantitative maintenance time prediction. Thereafter, the proposed scheme is compared with different types of algorithms in a case study to demonstrate the effectiveness of the proposed model using actual production data. The results show that the proposed model outperforms traditional ones in accuracy and computational efficiency.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"56 12","pages":"716-721"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91501659","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 : 2018-08-01DOI: 10.1109/COASE.2018.8560499
A. Petitti, Donato Di Paola, R. Colella, A. Milella, E. Stella, Antonio Coratelli, D. Naso
The field of multi-robot systems is one of the main research topics in robotics, as robot networks offer great advantages in terms of reliability and efficiency in many application domains. This paper focuses on the problem of mutual localization and 3D cooperative environment mapping using a heterogeneous multi-robot team. The proposed algorithm relies on the exchange of local maps and is totally distributed; no assumption on a common reference frame is done. The developed strategy is robust to failures, scalable with the number of the robots in the network, and has been validated through an experimental campaign.
{"title":"A Distributed Map Building Approach for Mobile Robotic Networks","authors":"A. Petitti, Donato Di Paola, R. Colella, A. Milella, E. Stella, Antonio Coratelli, D. Naso","doi":"10.1109/COASE.2018.8560499","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560499","url":null,"abstract":"The field of multi-robot systems is one of the main research topics in robotics, as robot networks offer great advantages in terms of reliability and efficiency in many application domains. This paper focuses on the problem of mutual localization and 3D cooperative environment mapping using a heterogeneous multi-robot team. The proposed algorithm relies on the exchange of local maps and is totally distributed; no assumption on a common reference frame is done. The developed strategy is robust to failures, scalable with the number of the robots in the network, and has been validated through an experimental campaign.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"109 5 1","pages":"116-121"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89737139","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 : 2018-08-01DOI: 10.1109/COASE.2018.8560424
Imtiaz Ahmed, A. Dagnino, Alessandro Bongiovi, Yu Ding
A hydropower generation plant is a complex system and composed of numerous physical components. To monitor the health of different components it is necessary to detect anomalous behavior in time. Establishing a performance guideline along with identification of the critical variables causing anomalous behavior can help the maintenance personnel to detect any potential shift in the process timely. To establish any guideline for future control, at first a mechanism is needed to differentiate anomalous observations from the normal ones. In our work we have employed three different approaches to detect the anomalous observations and compared their performances using a historical data set received from a hydropower plant. The outliers detected are verified by the domain experts. Making use of a decision tree and feature selection process, we have identified some critical variables which are potentially linked to the presence of the outliers. We further developed a one-class classifier using the outlier cleaned dataset, which defines the normal working condition, and therefore, violation of the normal conditions could identify anomalous observations in future operations.
{"title":"Outlier Detection for Hydropower Generation Plant","authors":"Imtiaz Ahmed, A. Dagnino, Alessandro Bongiovi, Yu Ding","doi":"10.1109/COASE.2018.8560424","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560424","url":null,"abstract":"A hydropower generation plant is a complex system and composed of numerous physical components. To monitor the health of different components it is necessary to detect anomalous behavior in time. Establishing a performance guideline along with identification of the critical variables causing anomalous behavior can help the maintenance personnel to detect any potential shift in the process timely. To establish any guideline for future control, at first a mechanism is needed to differentiate anomalous observations from the normal ones. In our work we have employed three different approaches to detect the anomalous observations and compared their performances using a historical data set received from a hydropower plant. The outliers detected are verified by the domain experts. Making use of a decision tree and feature selection process, we have identified some critical variables which are potentially linked to the presence of the outliers. We further developed a one-class classifier using the outlier cleaned dataset, which defines the normal working condition, and therefore, violation of the normal conditions could identify anomalous observations in future operations.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"23 1","pages":"193-198"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90412317","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 : 2018-08-01DOI: 10.1109/COASE.2018.8560431
David Tseng, David Wang, Carolyn L. Chen, Lauren Miller, W. Song, J. Viers, S. Vougioukas, Stefano Carpin, J. A. Ojea, Ken Goldberg
Recent advances in unmanned aerial vehicles suggest that collecting aerial agricultural images can be cost-efficient, which can subsequently support automated precision irrigation. To study the potential for machine learning to learn local soil moisture conditions directly from such images, we developed a very fast, linear discrete-time simulation of plant growth based on the Richards equation. We use the simulator to generate large datasets of synthetic aerial images of a vineyard with known moisture conditions and then compare seven methods for inferring moisture conditions from images, in which the “uncorrelated plant” methods look at individual plants and the “correlated field” methods look at the entire vineyard: 1) constant prediction baseline, 2) linear Support Vector Machines (SVM), 3) Random Forests Uncorrelated Plant (RFUP), 4) Random Forests Correlated Field (RFCF), 5) two-layer Neural Networks (NN), 6) Deep Convolutional Neural Networks Uncorrelated Plant (CNNUP), and 7) Deep Convolutional Neural Networks Correlated Field (CNNCF). Experiments on held-out test images show that a globally-connected CNN performs best with normalized mean absolute error of 3.4%. Sensitivity experiments suggest that learned global CNNs are robust to injected noise in both the simulator and generated images as well as in the size of the training sets. In simulation, we compare the agricultural standard of flood irrigation to a proportional precision irrigation controller using the output of the global CNN and find that the latter can reduce water consumption by up to 52% and is also robust to errors in irrigation level, location, and timing. The first-order plant simulator and datasets are available at https://github.com/BerkeleyAutomation/RAPID.
{"title":"Towards Automating Precision Irrigation: Deep Learning to Infer Local Soil Moisture Conditions from Synthetic Aerial Agricultural Images","authors":"David Tseng, David Wang, Carolyn L. Chen, Lauren Miller, W. Song, J. Viers, S. Vougioukas, Stefano Carpin, J. A. Ojea, Ken Goldberg","doi":"10.1109/COASE.2018.8560431","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560431","url":null,"abstract":"Recent advances in unmanned aerial vehicles suggest that collecting aerial agricultural images can be cost-efficient, which can subsequently support automated precision irrigation. To study the potential for machine learning to learn local soil moisture conditions directly from such images, we developed a very fast, linear discrete-time simulation of plant growth based on the Richards equation. We use the simulator to generate large datasets of synthetic aerial images of a vineyard with known moisture conditions and then compare seven methods for inferring moisture conditions from images, in which the “uncorrelated plant” methods look at individual plants and the “correlated field” methods look at the entire vineyard: 1) constant prediction baseline, 2) linear Support Vector Machines (SVM), 3) Random Forests Uncorrelated Plant (RFUP), 4) Random Forests Correlated Field (RFCF), 5) two-layer Neural Networks (NN), 6) Deep Convolutional Neural Networks Uncorrelated Plant (CNNUP), and 7) Deep Convolutional Neural Networks Correlated Field (CNNCF). Experiments on held-out test images show that a globally-connected CNN performs best with normalized mean absolute error of 3.4%. Sensitivity experiments suggest that learned global CNNs are robust to injected noise in both the simulator and generated images as well as in the size of the training sets. In simulation, we compare the agricultural standard of flood irrigation to a proportional precision irrigation controller using the output of the global CNN and find that the latter can reduce water consumption by up to 52% and is also robust to errors in irrigation level, location, and timing. The first-order plant simulator and datasets are available at https://github.com/BerkeleyAutomation/RAPID.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"58 1","pages":"284-291"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90714005","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 : 2018-08-01DOI: 10.1109/coase.2018.8560392
{"title":"Technical Program Contents List","authors":"","doi":"10.1109/coase.2018.8560392","DOIUrl":"https://doi.org/10.1109/coase.2018.8560392","url":null,"abstract":"","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"394 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76680362","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 : 2018-08-01DOI: 10.1109/COASE.2018.8560418
D. Tomzik, X. Xu
Conventional control systems for machine tools and manufacturing systems are often limited in their computational power, connectivity and interoperability. Cloud-based control systems are a solution that addresses these issues. Advantages of the cloud are the elasticity of computational power (Infrastructure as a Service) and a plethora of development tools (Platform as a Service). The developed solutions are based on a local control system with an additional connection to the cloud. Communication and control of the field level run centralised through this control system. To try for more flexibility, we propose an approach where individual components at the field level are directly connected to the cloud. They are equipped with computational resources, connected directly to a TCP/IP network and communicate with each other and perform control tasks. This had been made possible by ever-shrinking integrated circuits at lower prices. In this paper, a possible use scenario, hardware candidates, and firmware aspects are presented. For an initial examination, the findings were compared against requirements for cloud-based control in the application area of soft-tissue interaction. This proposed architecture will be the basis for a prototype in the future.
{"title":"Architecture of a Cloud-Based Control System Decentralised at Field Level","authors":"D. Tomzik, X. Xu","doi":"10.1109/COASE.2018.8560418","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560418","url":null,"abstract":"Conventional control systems for machine tools and manufacturing systems are often limited in their computational power, connectivity and interoperability. Cloud-based control systems are a solution that addresses these issues. Advantages of the cloud are the elasticity of computational power (Infrastructure as a Service) and a plethora of development tools (Platform as a Service). The developed solutions are based on a local control system with an additional connection to the cloud. Communication and control of the field level run centralised through this control system. To try for more flexibility, we propose an approach where individual components at the field level are directly connected to the cloud. They are equipped with computational resources, connected directly to a TCP/IP network and communicate with each other and perform control tasks. This had been made possible by ever-shrinking integrated circuits at lower prices. In this paper, a possible use scenario, hardware candidates, and firmware aspects are presented. For an initial examination, the findings were compared against requirements for cloud-based control in the application area of soft-tissue interaction. This proposed architecture will be the basis for a prototype in the future.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"28 1","pages":"353-358"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85784557","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 : 2018-08-01DOI: 10.1109/COASE.2018.8560702
Hsieh-Yu Li, Ishara Paranawithana, Liangjing Yang, U-Xuan Tan
There is an increasing number of applications in physical human-robot interaction (pHRI) where the end-effector of the robot is compliant in response to the force exerted by the human. The force sensor is normally mounted with an instrument on the end-effector to measure the human operational force. However, when the robot is in contact with the human and an environment simultaneously, the force sensor reading includes both the human and the environmental force resulting in ineffective contacting interaction within these three objects (robot, human and environment). In addition, if the environment is moving, it is more challenging for the operator to track the target with the robot. Therefore, in this paper, we address the issue of pHRI coupled with a moving environment. More specifically, we use a collaborative robot with an ultrasound probe as an illustration due to its sophisticated condition: the operator needs to contact the environment using a sufficient force to get clearer images and track the moving target. The proposed control scheme is employed using only one force sensor to guarantee a stable physical interaction within three objects and provide the compliant and intuitive operation for human. Experiments with a collaborative robot are conducted to evaluate the effectiveness of the proposed controller.
{"title":"Physical Human-Robot Interaction Coupled with a Moving Environment or Target: Contact and Track","authors":"Hsieh-Yu Li, Ishara Paranawithana, Liangjing Yang, U-Xuan Tan","doi":"10.1109/COASE.2018.8560702","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560702","url":null,"abstract":"There is an increasing number of applications in physical human-robot interaction (pHRI) where the end-effector of the robot is compliant in response to the force exerted by the human. The force sensor is normally mounted with an instrument on the end-effector to measure the human operational force. However, when the robot is in contact with the human and an environment simultaneously, the force sensor reading includes both the human and the environmental force resulting in ineffective contacting interaction within these three objects (robot, human and environment). In addition, if the environment is moving, it is more challenging for the operator to track the target with the robot. Therefore, in this paper, we address the issue of pHRI coupled with a moving environment. More specifically, we use a collaborative robot with an ultrasound probe as an illustration due to its sophisticated condition: the operator needs to contact the environment using a sufficient force to get clearer images and track the moving target. The proposed control scheme is employed using only one force sensor to guarantee a stable physical interaction within three objects and provide the compliant and intuitive operation for human. Experiments with a collaborative robot are conducted to evaluate the effectiveness of the proposed controller.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"17 1","pages":"43-49"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86889790","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 : 2018-08-01DOI: 10.1109/COASE.2018.8560382
Jiajun Xu, Linsen Xu, Jinfu Liu, Xiaohu Li, Xuan Wu
In this paper, a multi-mode biomimetic wall-climbing robot is represented, which employs spiny wheels, adhesive treads, spiny treads and a suction cup, and it can switch different modes with self-adapting to different terrains. The robot employs spiny wheels and spiny treads while meeting with rough surfaces and employs adhesive treads while encountering smooth surfaces. And a suction cup is applied all the time for assistive adhesive function. The adhesion property of the adhesive materials is analyzed, and their high reliability is proved. Moreover, the prototype of the robot is manufactured, and some experiments are completed.
{"title":"A Multi-Mode Biomimetic Wall-Climbing Robot","authors":"Jiajun Xu, Linsen Xu, Jinfu Liu, Xiaohu Li, Xuan Wu","doi":"10.1109/COASE.2018.8560382","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560382","url":null,"abstract":"In this paper, a multi-mode biomimetic wall-climbing robot is represented, which employs spiny wheels, adhesive treads, spiny treads and a suction cup, and it can switch different modes with self-adapting to different terrains. The robot employs spiny wheels and spiny treads while meeting with rough surfaces and employs adhesive treads while encountering smooth surfaces. And a suction cup is applied all the time for assistive adhesive function. The adhesion property of the adhesive materials is analyzed, and their high reliability is proved. Moreover, the prototype of the robot is manufactured, and some experiments are completed.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"39 1","pages":"514-519"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87231274","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 : 2018-08-01DOI: 10.1109/COASE.2018.8560587
Fadi Assad, E. Rushforth, Mus'ab H. Ahmad, B. Ahmad, R. Harrison
In today's manufacturing industry, higher productivity and sustainability should go hand-in-hand. This practice is motivated by governmental regulations as well as customers' awareness. For the current time, one of the inexpensive solutions is motion planning for an improved energy consumption. This paper introduces a general approach that is valid for testing and optimising energy consumption of the input motion profile. The Particle Swarm Optimisation method (PSO) is used because of its mathematical simplicity and quick convergence. Being commonly used, s-curve motion profile is reconstructed and optimised for a better energy consumption. The results show potential energy reduction and better positioning for the system configured according to the optimised s-curve.
{"title":"An Approach of Optimising S-curve Trajectory for a Better Energy Consumption","authors":"Fadi Assad, E. Rushforth, Mus'ab H. Ahmad, B. Ahmad, R. Harrison","doi":"10.1109/COASE.2018.8560587","DOIUrl":"https://doi.org/10.1109/COASE.2018.8560587","url":null,"abstract":"In today's manufacturing industry, higher productivity and sustainability should go hand-in-hand. This practice is motivated by governmental regulations as well as customers' awareness. For the current time, one of the inexpensive solutions is motion planning for an improved energy consumption. This paper introduces a general approach that is valid for testing and optimising energy consumption of the input motion profile. The Particle Swarm Optimisation method (PSO) is used because of its mathematical simplicity and quick convergence. Being commonly used, s-curve motion profile is reconstructed and optimised for a better energy consumption. The results show potential energy reduction and better positioning for the system configured according to the optimised s-curve.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"33 1","pages":"98-103"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91300965","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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