Pub Date : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453641
B. Sultana, K. Scicluna, Judie Attard, C. Seguna, J. Scerri
This paper presents the design of a Field Programmable Gate Array-based three-phase inverter intended for High-Frequency Injection as used with Low-speed Sensorless Control. Sensorless control includes a wide range of techniques used to control electrical machines without having a dedicated speed or position sensor. Several state-of-the-art techniques use High-Frequency Injection to obtain measurable HF currents which are position modulated.In this paper, a custom inverter was designed for use with an FPGA-based controller which generates both fundamental and High-Frequency rotating sinusoidal components. The use of an FPGA is recommended to increase both the HF signal and Pulse Width Modulation frequency to reduce acoustic noise and torque ripple. Experimental FPGA-based V/f control of a 12 V 400 W Permanent Magnet Synchronous Machine is described. Experimental phase fundamental and High-Frequency current results with different reference frequency setpoints are shown in both time and frequency domains.
{"title":"Design of a FPGA-based Inverter Drive for HF Injection Based Sensorless Control","authors":"B. Sultana, K. Scicluna, Judie Attard, C. Seguna, J. Scerri","doi":"10.1109/ICIT46573.2021.9453641","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453641","url":null,"abstract":"This paper presents the design of a Field Programmable Gate Array-based three-phase inverter intended for High-Frequency Injection as used with Low-speed Sensorless Control. Sensorless control includes a wide range of techniques used to control electrical machines without having a dedicated speed or position sensor. Several state-of-the-art techniques use High-Frequency Injection to obtain measurable HF currents which are position modulated.In this paper, a custom inverter was designed for use with an FPGA-based controller which generates both fundamental and High-Frequency rotating sinusoidal components. The use of an FPGA is recommended to increase both the HF signal and Pulse Width Modulation frequency to reduce acoustic noise and torque ripple. Experimental FPGA-based V/f control of a 12 V 400 W Permanent Magnet Synchronous Machine is described. Experimental phase fundamental and High-Frequency current results with different reference frequency setpoints are shown in both time and frequency domains.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121690185","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 : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453677
Jueming Liu, R. V. D. Vlist, Ellissa Verseput
Given the world’s current climate change challenge and residential gas consumption being a major end-use of energy, people more than ever need to minimize their household’s energy footprint. Personalised, actionable advice can give people tips on which actions they can take to reduce residential energy usage, such as lowering the thermostat temperature. For this advice to be relevant it is important to understand the quantitative impact of thermostat setpoints on daily gas usage for each individual household. In this article, this impact is estimated by comparing three machine learning approaches.Linear regression, deep learning and gradient boosting machine are applied to a multi-dimensional time series dataset for 300 Dutch households. The three approaches are compared based on three metrics: root mean square error (RMSE), explainability and scalability. The results of the best model (gradient boosting machine) are explained using a technique called SHapley Additive exPlanations (SHAP). This interpretation method can quantify the contribution of all inputs, among which thermostat setpoints, to the daily gas usage prediction of the model for different individual households.This article adds to the current state of the art by focusing on the impact of influenceable thermostat setpoints, as opposed to less actionable factors such as house size, insulation status of the house and weather. By applying SHAP, the personal impact and differences between individual households are estimated, in contrast to only learning trends. Moreover, a machine learning model, trained on a representative dataset, is applicable at scale to other households for estimating a personal, quantified impact of setpoint choices.
{"title":"Leveraging machine learning approaches to estimate the impact of thermostat setpoints on individual household gas consumption","authors":"Jueming Liu, R. V. D. Vlist, Ellissa Verseput","doi":"10.1109/ICIT46573.2021.9453677","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453677","url":null,"abstract":"Given the world’s current climate change challenge and residential gas consumption being a major end-use of energy, people more than ever need to minimize their household’s energy footprint. Personalised, actionable advice can give people tips on which actions they can take to reduce residential energy usage, such as lowering the thermostat temperature. For this advice to be relevant it is important to understand the quantitative impact of thermostat setpoints on daily gas usage for each individual household. In this article, this impact is estimated by comparing three machine learning approaches.Linear regression, deep learning and gradient boosting machine are applied to a multi-dimensional time series dataset for 300 Dutch households. The three approaches are compared based on three metrics: root mean square error (RMSE), explainability and scalability. The results of the best model (gradient boosting machine) are explained using a technique called SHapley Additive exPlanations (SHAP). This interpretation method can quantify the contribution of all inputs, among which thermostat setpoints, to the daily gas usage prediction of the model for different individual households.This article adds to the current state of the art by focusing on the impact of influenceable thermostat setpoints, as opposed to less actionable factors such as house size, insulation status of the house and weather. By applying SHAP, the personal impact and differences between individual households are estimated, in contrast to only learning trends. Moreover, a machine learning model, trained on a representative dataset, is applicable at scale to other households for estimating a personal, quantified impact of setpoint choices.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127794759","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 : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453661
Elie Hleihel, M. Fadel, H. Kanaan
In recent decades, the microgrid concept emerged as a solution to electrify remote areas and integrate renewable energy sources to mitigate environmental pollution. Owing to the evolution of power electronic devices, the ease of control, and the improved efficiency and reliability, DC microgrids are gaining increased interest. Many kinds of research address the hierarchical control in DC microgrids to accomplish multi-objectives. On the first and second levels, the control targets fast dynamic variables to achieve its objectives. Yet, on a third control level, general management functionalities are executed. Many of these management functionalities target the system variables with a slower dynamic and so, to prove the effectiveness of the proposed hierarchical control, a 24-hour model simulation is required. The wide time-range dynamics of the existing system variables make the 24-hour modeling subject a complicated matter especially, on standard computers with conventional performances. To overcome this problem, this paper proposes a 24-hour DC microgrid model which offers the best tradeoff between model precision, complexity, and simulation speed. The multi-objectives hierarchical control is adopted: on a first and second control level, several averaging techniques are introduced and compared to a detailed reference model in terms of accuracy and calculation step size. DC microgrid's general management strategy is adopted on the third control level. Simulation tests are performed on MATLAB/Simulink software to prove the viability of the proposed 24-hour model.
{"title":"Control and Power Management of a 24-Hour DC Microgrid Improved Model","authors":"Elie Hleihel, M. Fadel, H. Kanaan","doi":"10.1109/ICIT46573.2021.9453661","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453661","url":null,"abstract":"In recent decades, the microgrid concept emerged as a solution to electrify remote areas and integrate renewable energy sources to mitigate environmental pollution. Owing to the evolution of power electronic devices, the ease of control, and the improved efficiency and reliability, DC microgrids are gaining increased interest. Many kinds of research address the hierarchical control in DC microgrids to accomplish multi-objectives. On the first and second levels, the control targets fast dynamic variables to achieve its objectives. Yet, on a third control level, general management functionalities are executed. Many of these management functionalities target the system variables with a slower dynamic and so, to prove the effectiveness of the proposed hierarchical control, a 24-hour model simulation is required. The wide time-range dynamics of the existing system variables make the 24-hour modeling subject a complicated matter especially, on standard computers with conventional performances. To overcome this problem, this paper proposes a 24-hour DC microgrid model which offers the best tradeoff between model precision, complexity, and simulation speed. The multi-objectives hierarchical control is adopted: on a first and second control level, several averaging techniques are introduced and compared to a detailed reference model in terms of accuracy and calculation step size. DC microgrid's general management strategy is adopted on the third control level. Simulation tests are performed on MATLAB/Simulink software to prove the viability of the proposed 24-hour model.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126546051","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 : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453551
Feryel Zoghlami, M. Kaden, T. Villmann, G. Schneider, H. Heinrich
Sensor fusion has gained a lot of attention during the recent years. It is used as an application tool in different fields including semiconductor-, automotive-, medicine industries. However, finding the right sensor combination for the dedicated application is still very challenging. In this paper, we focus on applying the sensor fusion concept in reference to the prototype-based learning for object classification purposes. In fact, we present a bi-functional system architecture. The system has the feature to evaluate each sensor’s contribution in a predefined classification task. The developed system will preserve the effort and the time spent by engineers to collect a huge quantity of preprocessed samples from each sensor and to try different training configurations. Our approach consists of training a model. The model learns both the predefined classes and additional parameters that represent the contribution of each sensor used in the fusion system for fulfilling the predefined classification task. We illustrate the functionality of our developed system by referring to two different application scenarios. Results validate the dual functionality of our approach as well as the simplicity of the integration of our evaluation system in any further fusion application regardless sensors inputs and classification outputs.
{"title":"Sensors data fusion for smart decisions making: A novel bi-functional system for the evaluation of sensors contribution in classification problems","authors":"Feryel Zoghlami, M. Kaden, T. Villmann, G. Schneider, H. Heinrich","doi":"10.1109/ICIT46573.2021.9453551","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453551","url":null,"abstract":"Sensor fusion has gained a lot of attention during the recent years. It is used as an application tool in different fields including semiconductor-, automotive-, medicine industries. However, finding the right sensor combination for the dedicated application is still very challenging. In this paper, we focus on applying the sensor fusion concept in reference to the prototype-based learning for object classification purposes. In fact, we present a bi-functional system architecture. The system has the feature to evaluate each sensor’s contribution in a predefined classification task. The developed system will preserve the effort and the time spent by engineers to collect a huge quantity of preprocessed samples from each sensor and to try different training configurations. Our approach consists of training a model. The model learns both the predefined classes and additional parameters that represent the contribution of each sensor used in the fusion system for fulfilling the predefined classification task. We illustrate the functionality of our developed system by referring to two different application scenarios. Results validate the dual functionality of our approach as well as the simplicity of the integration of our evaluation system in any further fusion application regardless sensors inputs and classification outputs.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121756369","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 : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453568
C. Sauer, Eike Lyczkowski, M. Sliskovic, M. Schmidt
More and more mobile robots are used within modern production facilities. The interconnection of these robots and their connection to other systems is a major focus, when looking at trends like Industry 4.0. However, industrial environments are very challenging for any communications network, that utilizes wireless transmissions. High mobility, changing propagation channels, interference and highly utilized bandwidth are common occurrences. The dissemination of real-time alarm messages to mobile clients of an industrial network is a challenging use case under these circumstances.The often requested real-time guarantee for message delivery cannot be given in such a dynamic and unpredictable environment. Real-Time Alarm Dissemination System (RTADS) is implemented, which offers the following compromise: It can either transmit the message within a guaranteed time-slot or, inform the receiver, that the real-time connection is lost within the same time-slot. The system is additionally able to implement such connection in complex multi-hop networks with minimal impact on other communication.The RTADS was implemented and tested in different environments. Successful alarm transmissions within 100ms for up to 10 re-transmissions/relays were observed.
{"title":"Real-time Alarm Dissemination in Mobile Industrial Networks","authors":"C. Sauer, Eike Lyczkowski, M. Sliskovic, M. Schmidt","doi":"10.1109/ICIT46573.2021.9453568","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453568","url":null,"abstract":"More and more mobile robots are used within modern production facilities. The interconnection of these robots and their connection to other systems is a major focus, when looking at trends like Industry 4.0. However, industrial environments are very challenging for any communications network, that utilizes wireless transmissions. High mobility, changing propagation channels, interference and highly utilized bandwidth are common occurrences. The dissemination of real-time alarm messages to mobile clients of an industrial network is a challenging use case under these circumstances.The often requested real-time guarantee for message delivery cannot be given in such a dynamic and unpredictable environment. Real-Time Alarm Dissemination System (RTADS) is implemented, which offers the following compromise: It can either transmit the message within a guaranteed time-slot or, inform the receiver, that the real-time connection is lost within the same time-slot. The system is additionally able to implement such connection in complex multi-hop networks with minimal impact on other communication.The RTADS was implemented and tested in different environments. Successful alarm transmissions within 100ms for up to 10 re-transmissions/relays were observed.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121757140","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 : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453499
Shogo Harada, N. Uchiyama
Feed drive systems are widely used for industrial machines in factories all over the world, and further robust performance is expected for precision motion. This study considers an application of simple adaptive control to feed drive systems, which generally requires an almost strictly positive real property in a plant. Although a parallel feedforward compensator is a typical approach to achieve this property, it may intrinsically deteriorate the control performance. This study applies an augmented output signal consisting of position and velocity information to achieve the above property. In addition, an adaptive friction compensator is also designed to further improve the performance. Experimental results demonstrate the effectiveness of the proposed approach.
{"title":"Robust Simple Adaptive Control with Augmented Output Signal and Friction Compensation for Industrial Feed Drive Systems","authors":"Shogo Harada, N. Uchiyama","doi":"10.1109/ICIT46573.2021.9453499","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453499","url":null,"abstract":"Feed drive systems are widely used for industrial machines in factories all over the world, and further robust performance is expected for precision motion. This study considers an application of simple adaptive control to feed drive systems, which generally requires an almost strictly positive real property in a plant. Although a parallel feedforward compensator is a typical approach to achieve this property, it may intrinsically deteriorate the control performance. This study applies an augmented output signal consisting of position and velocity information to achieve the above property. In addition, an adaptive friction compensator is also designed to further improve the performance. Experimental results demonstrate the effectiveness of the proposed approach.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132020609","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 : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453604
Thomas Wagner, Jonathan Seitz, G. Schneider
In this paper, we present an approach to automate a legacy measurement device used for offline vibration measurement within automated material handling systems (AMHS) of semiconductor manufacturing plants by using a modern, state of the art IoT framework. After outlining the drawbacks of the existing, time-consuming procedure of offline measurement, the decision of automating the device using the IoT is explained and the necessary steps and framework services are introduced. Finally, the results and benefits of using an IoT framework as well as the new, automated workflow are documented.
{"title":"Vibration Measurement and Visualization in Semiconductor AMHS on the basis of IoT","authors":"Thomas Wagner, Jonathan Seitz, G. Schneider","doi":"10.1109/ICIT46573.2021.9453604","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453604","url":null,"abstract":"In this paper, we present an approach to automate a legacy measurement device used for offline vibration measurement within automated material handling systems (AMHS) of semiconductor manufacturing plants by using a modern, state of the art IoT framework. After outlining the drawbacks of the existing, time-consuming procedure of offline measurement, the decision of automating the device using the IoT is explained and the necessary steps and framework services are introduced. Finally, the results and benefits of using an IoT framework as well as the new, automated workflow are documented.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131737683","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 : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453595
R. González, C. Rojas, Leonardo Callegaro
The use of electric vehicles (EVs) has grown notably in the last years and with it new challenges for power electronics have appeared. Since typically the main energy storage system in EVs consists of batteries, one of these challenges is the efficient and reliable management of power flows in charging/discharging mode. This paper presents an electrical and thermal modelling of a three-level buck-boost DC-DC converter (TLBBC) with semiconductors based on gallium nitride (GaN) technology. Also an active thermal control (ATC) scheme to mitigate the thermal stress in the semiconductor is proposed, together with control schemes for DC-link voltage and voltage balance between capacitors in the TLBBC. The TLBBC is designed to operate in a boost mode at rated power of 25 kW, using a parallel design with GaN semiconductors. Proposed control schemes are implemented using linear controllers. Finally, comprehensive simulation results confirm and validate the proposed control schemes.
{"title":"Three-level DC-DC GaN-based Converter with Active Thermal Control for Powertrain applications in Electric Vehicles","authors":"R. González, C. Rojas, Leonardo Callegaro","doi":"10.1109/ICIT46573.2021.9453595","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453595","url":null,"abstract":"The use of electric vehicles (EVs) has grown notably in the last years and with it new challenges for power electronics have appeared. Since typically the main energy storage system in EVs consists of batteries, one of these challenges is the efficient and reliable management of power flows in charging/discharging mode. This paper presents an electrical and thermal modelling of a three-level buck-boost DC-DC converter (TLBBC) with semiconductors based on gallium nitride (GaN) technology. Also an active thermal control (ATC) scheme to mitigate the thermal stress in the semiconductor is proposed, together with control schemes for DC-link voltage and voltage balance between capacitors in the TLBBC. The TLBBC is designed to operate in a boost mode at rated power of 25 kW, using a parallel design with GaN semiconductors. Proposed control schemes are implemented using linear controllers. Finally, comprehensive simulation results confirm and validate the proposed control schemes.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"332 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132404342","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 : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453581
Julius Pettersson, P. Falkman
One way of potentially improving the use of robots in a collaborative environment is through prediction of human intention that would give the robots insight into how the operators are about to behave. An important part of human behaviour is arm movement and this paper presents a method to predict arm movement based on the operator’s eye gaze. A test scenario has been designed in order to gather coordinate based hand movement data in a virtual reality environment. The results shows that the eye gaze data can successfully be used to train an artificial neural network that is able to predict the direction of movement ~500ms ahead of time.
{"title":"Human Movement Direction Prediction using Virtual Reality and Eye Tracking","authors":"Julius Pettersson, P. Falkman","doi":"10.1109/ICIT46573.2021.9453581","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453581","url":null,"abstract":"One way of potentially improving the use of robots in a collaborative environment is through prediction of human intention that would give the robots insight into how the operators are about to behave. An important part of human behaviour is arm movement and this paper presents a method to predict arm movement based on the operator’s eye gaze. A test scenario has been designed in order to gather coordinate based hand movement data in a virtual reality environment. The results shows that the eye gaze data can successfully be used to train an artificial neural network that is able to predict the direction of movement ~500ms ahead of time.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115763502","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 : 2021-03-10DOI: 10.1109/ICIT46573.2021.9453704
Chengxi Yu, Xi Chen
Unmanned Aerial Vehicles (UAVs) with stereo cameras are usually utilized to serve in many engineering applications. Multi-UAVs in formation is an effective way to deal with the complicated industrial tasks, for example visual monitoring, to compensate the limitation of field of view (FOV) of the onboard camera of single UAV. In this paper, a leader-follower UAVs system of which the FOVs of onboard cameras are required to always being overlapped during the flight is proposed to solve the visual monitoring problem. Under this scenario, the images captured by onboard cameras have overlaps such that the entire scene of the environment could be reconstructed via image mosaic technique. To guarantee the visibility constraint, the cost functions related to the relative states of UAVs and then the gradient descent controllers are designed. And at last, a simulation example is given to demonstrate the effectiveness of the proposed algorithm.
{"title":"Leader-Follower Formation for UAVs with FOVs Constraint","authors":"Chengxi Yu, Xi Chen","doi":"10.1109/ICIT46573.2021.9453704","DOIUrl":"https://doi.org/10.1109/ICIT46573.2021.9453704","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) with stereo cameras are usually utilized to serve in many engineering applications. Multi-UAVs in formation is an effective way to deal with the complicated industrial tasks, for example visual monitoring, to compensate the limitation of field of view (FOV) of the onboard camera of single UAV. In this paper, a leader-follower UAVs system of which the FOVs of onboard cameras are required to always being overlapped during the flight is proposed to solve the visual monitoring problem. Under this scenario, the images captured by onboard cameras have overlaps such that the entire scene of the environment could be reconstructed via image mosaic technique. To guarantee the visibility constraint, the cost functions related to the relative states of UAVs and then the gradient descent controllers are designed. And at last, a simulation example is given to demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":193338,"journal":{"name":"2021 22nd IEEE International Conference on Industrial Technology (ICIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114402080","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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