Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649736
Constantin Wanninger, Sebastian Rossi, Martin Schörner, A. Hoffmann, Alexander Poeppel, Christian Eymueller, W. Reif
The Robot Operating System (ROS) offers developers a large number of ready-made packages for developing robot programs. The multitude of packages and the different interfaces or adapters is also the reason why ROS projects often tend to become confusing. Concepts of model-driven software development using a domain-specific modeling language could counteract this and at the same time speed up the development process of such projects. This is investigated in this paper by transferring the core concepts from ROS 2 into a graphical programming interface. Elements of established graphical programming tools are compared and approaches from modeling languages such as UML are used to create a novel approach for graphical development of ROS projects. The resulting interface is evaluated through the development of a project built on ROS, and the approach shows promise towards facilitating work with the Robot Operating System.
{"title":"ROSSi A Graphical Programming Interface for ROS 2","authors":"Constantin Wanninger, Sebastian Rossi, Martin Schörner, A. Hoffmann, Alexander Poeppel, Christian Eymueller, W. Reif","doi":"10.23919/ICCAS52745.2021.9649736","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649736","url":null,"abstract":"The Robot Operating System (ROS) offers developers a large number of ready-made packages for developing robot programs. The multitude of packages and the different interfaces or adapters is also the reason why ROS projects often tend to become confusing. Concepts of model-driven software development using a domain-specific modeling language could counteract this and at the same time speed up the development process of such projects. This is investigated in this paper by transferring the core concepts from ROS 2 into a graphical programming interface. Elements of established graphical programming tools are compared and approaches from modeling languages such as UML are used to create a novel approach for graphical development of ROS projects. The resulting interface is evaluated through the development of a project built on ROS, and the approach shows promise towards facilitating work with the Robot Operating System.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130897857","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 : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649947
Chanthawit Anuntasethakul, Kantapong Leungrungwason, D. Banjerdpongchai
This paper presents a design of supervisory model predictive control (SMPC) for a building heating-ventilation-air-conditioning (HVAC) system. The control objectives are to minimize the total operating cost (TOC) and the thermal comfort cost (TCC). According to practical realization, a coefficient of performance (COP) is a time-varying parameter of HVAC system and depends on environment conditions. Therefore, we employ an artificial neural network (ANN) with k-means clustering to predict the COP. We design the SMPC to determine the optimal set-point temperature for the HVAC system which serves our control objectives. We utilize the predicted mean vote (PMV) to handle thermal comfort of occupants and to indicate an acceptable bound of the optimal set-point temperature. We formulate the SMPC with the predicted COP integration as two quadratic programs. The first quadratic program is a supervisory control problem for optimal set-point searching problem and the other is an MPC problem for optimal control input searching problem. Our results reveal that the root-mean-square error (RMSE) of the predicted COP is reduced by 34% using the clustered-ANN. When the SMPC is applied to the time-varying HVAC system, the TOC decreases by 14.53% compared to that of the nominal operation. Moreover, the maximum electrical power of the HVAC system is reduced by 15.66% resulting from smoothly shaved electrical power profile.
{"title":"Design of Supervisory Model Predictive Control for Building HVAC System with Time-Varying Coefficient of Performance","authors":"Chanthawit Anuntasethakul, Kantapong Leungrungwason, D. Banjerdpongchai","doi":"10.23919/ICCAS52745.2021.9649947","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649947","url":null,"abstract":"This paper presents a design of supervisory model predictive control (SMPC) for a building heating-ventilation-air-conditioning (HVAC) system. The control objectives are to minimize the total operating cost (TOC) and the thermal comfort cost (TCC). According to practical realization, a coefficient of performance (COP) is a time-varying parameter of HVAC system and depends on environment conditions. Therefore, we employ an artificial neural network (ANN) with k-means clustering to predict the COP. We design the SMPC to determine the optimal set-point temperature for the HVAC system which serves our control objectives. We utilize the predicted mean vote (PMV) to handle thermal comfort of occupants and to indicate an acceptable bound of the optimal set-point temperature. We formulate the SMPC with the predicted COP integration as two quadratic programs. The first quadratic program is a supervisory control problem for optimal set-point searching problem and the other is an MPC problem for optimal control input searching problem. Our results reveal that the root-mean-square error (RMSE) of the predicted COP is reduced by 34% using the clustered-ANN. When the SMPC is applied to the time-varying HVAC system, the TOC decreases by 14.53% compared to that of the nominal operation. Moreover, the maximum electrical power of the HVAC system is reduced by 15.66% resulting from smoothly shaved electrical power profile.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130879399","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 : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649985
Samarth Singh, K. Kishore, S. A. Akbar
In the present work a Neuro-Evolution based approach has been used to train a neural network for control of some sample systems. This method makes use of Genetic algorithm, here it is generating a population of neural networks and introduces mutation for producing better off-springs for the next generation. The approach is kind of black box optimization and do not require any back propagation for training. It makes use of fitness function to evaluate performance of off-springs, this fitness function is based on a novel reward function which allows for quick and smooth settling of the sample system towards set point. In order to address dynamics of the system's time sequenced error has been taken as exogenous input for the neural network. The method has been tested on a linear first order system and a system having non linearity.
{"title":"Neuro-evolutionary based controller design for linear and non-linear systems","authors":"Samarth Singh, K. Kishore, S. A. Akbar","doi":"10.23919/ICCAS52745.2021.9649985","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649985","url":null,"abstract":"In the present work a Neuro-Evolution based approach has been used to train a neural network for control of some sample systems. This method makes use of Genetic algorithm, here it is generating a population of neural networks and introduces mutation for producing better off-springs for the next generation. The approach is kind of black box optimization and do not require any back propagation for training. It makes use of fitness function to evaluate performance of off-springs, this fitness function is based on a novel reward function which allows for quick and smooth settling of the sample system towards set point. In order to address dynamics of the system's time sequenced error has been taken as exogenous input for the neural network. The method has been tested on a linear first order system and a system having non linearity.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129747091","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 : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649954
K. Oh, Sechan Oh, Jongmin Lee, K. Yi
This paper proposes a human-like learning frame for data-driven adaptive control algorithm of automated driving. Generally, driving control algorithms for automated vehicles need environment information and relatively accurate system information like mathematical model and system parameters. Because there are unexpected uncertainties and changes in environment and system dynamic, derivation of relatively accurate mathematical model or dynamic parameters information is not easy in real world and it can have a negative impact on driving control performance. Therefore, this study proposes data-driven feedback control method for automated driving based on human-like learning frame in order to address the aforementioned limitation. The human-like learning frame is based on finite-memory like human and is divided into two parts such as control and decision parts. In the control part, it is designed that feedback gains are derived based on least squares method using saved error states and gains in finite-memory. And the control input has been computed using the derived feedback gains. After control input is used for driving control, it is designed that current error states and the used feedback gains are saved in the finite-memory real-time in the decision part if the time-derivative of cost function has a negative value. If the time-derivative of the cost function has greater than or equal to zero, it is designed that the feedback gains are updated using gradient descent method with sensitivity estimation and the used error states and gains are saved in the memory as a new data. The performance evaluation has been conducted using the Matlab/Simulink and CarMaker software for reasonable evaluation.
{"title":"Development of a Human-Like Learning Frame for Data-Driven Adaptive Control Algorithm of Automated Driving","authors":"K. Oh, Sechan Oh, Jongmin Lee, K. Yi","doi":"10.23919/ICCAS52745.2021.9649954","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649954","url":null,"abstract":"This paper proposes a human-like learning frame for data-driven adaptive control algorithm of automated driving. Generally, driving control algorithms for automated vehicles need environment information and relatively accurate system information like mathematical model and system parameters. Because there are unexpected uncertainties and changes in environment and system dynamic, derivation of relatively accurate mathematical model or dynamic parameters information is not easy in real world and it can have a negative impact on driving control performance. Therefore, this study proposes data-driven feedback control method for automated driving based on human-like learning frame in order to address the aforementioned limitation. The human-like learning frame is based on finite-memory like human and is divided into two parts such as control and decision parts. In the control part, it is designed that feedback gains are derived based on least squares method using saved error states and gains in finite-memory. And the control input has been computed using the derived feedback gains. After control input is used for driving control, it is designed that current error states and the used feedback gains are saved in the finite-memory real-time in the decision part if the time-derivative of cost function has a negative value. If the time-derivative of the cost function has greater than or equal to zero, it is designed that the feedback gains are updated using gradient descent method with sensitivity estimation and the used error states and gains are saved in the memory as a new data. The performance evaluation has been conducted using the Matlab/Simulink and CarMaker software for reasonable evaluation.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129792945","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 : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649972
Rohit Kumar, Rahul Meel, B. Sandeep Reddy
The PIT (Precision Immobilization Technique) maneuver is a pursuit tactic used by law-enforcement officials to handle dangerous vehicle pursuit situations, wherein a pursuing car can force a fleeing car to turn sideways abruptly, causing the driver to lose control and stop. Such maneuvers have pitfalls, in that they require sufficient skill on the part of the driver to be carried out. Furthermore, given the utilization of electronic stabilizing control systems in modern vehicles' manual PIT maneuvers require more precision to be carried out. However, the previous decades have also undergone significant changes, in particular the development of autonomous driving technologies, which are now available in commercial vehicles. Autonomous PIT maneuvers could be the next step in handling dangerous vehicle pursuits of fugitives. But for autonomous execution of such maneuvers, accurate path planning and obstacle avoidance must be carried out first. This work presents the implementation of path optimization technique along with obstacle avoidance in simulation environment as well as in physical environment for execution of autonomous PIT maneuvering. Path optimization ensures that the bullet vehicle arrives at the specific point where the PIT maneuver is carried out. An important path optimization method has been utilized to accomplish the goal. Furthermore, obstacle avoidance is integrated with path optimization to avoid mid-way stationary obstacles, thereby adding a more realistic scenario for the same. This paper makes a simplifying assumption that the target vehicle is stationary or moving at a constant velocity, which can be extended by future work to a more realistic scenario involving accelerated target vehicle.
{"title":"Integration of Path Optimization and Obstacle avoidance for Autonomous Precision Immobilization Technique Maneuver","authors":"Rohit Kumar, Rahul Meel, B. Sandeep Reddy","doi":"10.23919/ICCAS52745.2021.9649972","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649972","url":null,"abstract":"The PIT (Precision Immobilization Technique) maneuver is a pursuit tactic used by law-enforcement officials to handle dangerous vehicle pursuit situations, wherein a pursuing car can force a fleeing car to turn sideways abruptly, causing the driver to lose control and stop. Such maneuvers have pitfalls, in that they require sufficient skill on the part of the driver to be carried out. Furthermore, given the utilization of electronic stabilizing control systems in modern vehicles' manual PIT maneuvers require more precision to be carried out. However, the previous decades have also undergone significant changes, in particular the development of autonomous driving technologies, which are now available in commercial vehicles. Autonomous PIT maneuvers could be the next step in handling dangerous vehicle pursuits of fugitives. But for autonomous execution of such maneuvers, accurate path planning and obstacle avoidance must be carried out first. This work presents the implementation of path optimization technique along with obstacle avoidance in simulation environment as well as in physical environment for execution of autonomous PIT maneuvering. Path optimization ensures that the bullet vehicle arrives at the specific point where the PIT maneuver is carried out. An important path optimization method has been utilized to accomplish the goal. Furthermore, obstacle avoidance is integrated with path optimization to avoid mid-way stationary obstacles, thereby adding a more realistic scenario for the same. This paper makes a simplifying assumption that the target vehicle is stationary or moving at a constant velocity, which can be extended by future work to a more realistic scenario involving accelerated target vehicle.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126248451","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 : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649854
Y. Quan, Jin Sung Kim, C. Chung
In this paper, we propose a Robust Model Predictive Control combined with Control Barrier Function (RMPC-CBF) for a nonholonomic robot with obstacle avoidance subject to additive input disturbances. Both Input-to-State Stability (ISS) and Input-to-State Safety (ISSf) are provided to theoretically guarantee the system's stability and safety. CBF-based safety conditions are formulated as constraints inside a robust MPC strategy. Robust satisfaction of the constraints is ensured by tightening the state constraint set. With admissible disturbances under a certain bound, ISS and robust recursive feasibility are guaranteed by computing the terminal region and state constraint set. For obstacle avoidance, Input-to-State Safe Control Barrier Function (ISSf-CBF) is chosen to provide robust set safety for the dynamic systems under input disturbances, which always guarantees that states stay inside or close to the safe set. With the proposed method, the future state prediction is taken into consideration and optimal performance is accomplished via MPC, and the system's safety is ensured via CBF. Numerical simulation results confirm the effectiveness and validity of the proposed approach.
{"title":"Robust Model Predictive Control with Control Barrier Function for Nonholonomic Robots with Obstacle Avoidance","authors":"Y. Quan, Jin Sung Kim, C. Chung","doi":"10.23919/ICCAS52745.2021.9649854","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649854","url":null,"abstract":"In this paper, we propose a Robust Model Predictive Control combined with Control Barrier Function (RMPC-CBF) for a nonholonomic robot with obstacle avoidance subject to additive input disturbances. Both Input-to-State Stability (ISS) and Input-to-State Safety (ISSf) are provided to theoretically guarantee the system's stability and safety. CBF-based safety conditions are formulated as constraints inside a robust MPC strategy. Robust satisfaction of the constraints is ensured by tightening the state constraint set. With admissible disturbances under a certain bound, ISS and robust recursive feasibility are guaranteed by computing the terminal region and state constraint set. For obstacle avoidance, Input-to-State Safe Control Barrier Function (ISSf-CBF) is chosen to provide robust set safety for the dynamic systems under input disturbances, which always guarantees that states stay inside or close to the safe set. With the proposed method, the future state prediction is taken into consideration and optimal performance is accomplished via MPC, and the system's safety is ensured via CBF. Numerical simulation results confirm the effectiveness and validity of the proposed approach.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126580340","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 : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649997
Pavlo Vlastos, A. Hunter, R. Curry, Carlos Isaac Espinosa Ramirez, G. Elkaim
Gaussian process regression and ordinary kriging are effective methods for spatial estimation, but are generally not used in online trajectory-planning applications for autonomous vehicles. A common use for kriging is spatial estimation for exploration. Kriging is limited by the necessary covariance matrix inversion and its computational complexity of O(n3), where $n$ represents the number of measurements taken in a sparsely-sampled field. Using the Sherman-Morison matrix inversion lemma, the complexity can be reduced to O(n2). This work focuses on further improving the computational time required to conduct spatial estimation with partitioned ordinary kriging (POK) for online trajectory-planning. A recursive algorithm is introduced to quickly subdivide a field for local kriging, reducing the computation time. We show computational time decreases between ordinary kriging with a regular inverse (OK), the iterative inverse ordinary kriging (IIOK), and POK with the iterative inverse method. Computation times are also compared between OK, IIOK, and POK methods for trajectory planning using a highest variance criterion and linear trajectory segments.
{"title":"Partitioned Gaussian Process Regression for Online Trajectory Planning for Autonomous Vehicles","authors":"Pavlo Vlastos, A. Hunter, R. Curry, Carlos Isaac Espinosa Ramirez, G. Elkaim","doi":"10.23919/ICCAS52745.2021.9649997","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649997","url":null,"abstract":"Gaussian process regression and ordinary kriging are effective methods for spatial estimation, but are generally not used in online trajectory-planning applications for autonomous vehicles. A common use for kriging is spatial estimation for exploration. Kriging is limited by the necessary covariance matrix inversion and its computational complexity of O(n3), where $n$ represents the number of measurements taken in a sparsely-sampled field. Using the Sherman-Morison matrix inversion lemma, the complexity can be reduced to O(n2). This work focuses on further improving the computational time required to conduct spatial estimation with partitioned ordinary kriging (POK) for online trajectory-planning. A recursive algorithm is introduced to quickly subdivide a field for local kriging, reducing the computation time. We show computational time decreases between ordinary kriging with a regular inverse (OK), the iterative inverse ordinary kriging (IIOK), and POK with the iterative inverse method. Computation times are also compared between OK, IIOK, and POK methods for trajectory planning using a highest variance criterion and linear trajectory segments.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127177030","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 : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649965
J. W. Lee, H. J. Yoon, H. S. Woo
This paper presents a prosthetic leg using a two-way hydraulic cylinder. Depending on the walking pattern of people and the walking environment required, it is necessary to adjust the prosthetic leg according to the conditions. The two-way hydraulic cylinder can adjust the tension and compression force separately, and therefore it can be fine-tuned according to the walking conditions. The two-way hydraulic cylinder is actively controlled through the stepping motor so that the human with the developed prosthetic leg can walk similar to a temporarily able-bodied person.
{"title":"Design and Fabrication of a Robotic Knee-Type Prosthetic Leg with a Two-Way Hydraulic Cylinder","authors":"J. W. Lee, H. J. Yoon, H. S. Woo","doi":"10.23919/ICCAS52745.2021.9649965","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649965","url":null,"abstract":"This paper presents a prosthetic leg using a two-way hydraulic cylinder. Depending on the walking pattern of people and the walking environment required, it is necessary to adjust the prosthetic leg according to the conditions. The two-way hydraulic cylinder can adjust the tension and compression force separately, and therefore it can be fine-tuned according to the walking conditions. The two-way hydraulic cylinder is actively controlled through the stepping motor so that the human with the developed prosthetic leg can walk similar to a temporarily able-bodied person.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114235784","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 : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649813
Deok-Won Lee, A. Elsharkawy, Kooksung Jun, Yundong Lee, Seungjun Kim, M. Kim
As the number of elderly persons increases, greater attention must be given to how they or their caregivers deal with emergency situations. This paper describes an automated tracking, fall detection, and emergency recovery system for elderly persons, and shows that efficient a Socially Assistive Robot (SAR) can resolve emergency situations and abnormal behaviors for at-risk populations. Our assistant robot uses position data provided by Ultra-WideBand (UWB) wireless network and motion sensor information to detect potentially dangerous situations for elderly persons. In this context, a deep neural network-based double-check method has been developed to detect and confirm fall situation with high accuracy using in-house developed sensory hardware. We then simulated four typical emergency scenarios using SILBOT-3 robot. Interaction scenarios were demonstrated to 28 caregivers, who were then invited to complete a short questionnaire regarding benefits and improvements for our system. Caregivers responded positively to our system's performance and stated that they would accept an assistant robot that could notify them quickly about a dangerous situation or possibly resolve the situation autonomously.
{"title":"24/7 Elderly Guard Robot: Emergency Detecting, Reacting, and Reporting","authors":"Deok-Won Lee, A. Elsharkawy, Kooksung Jun, Yundong Lee, Seungjun Kim, M. Kim","doi":"10.23919/ICCAS52745.2021.9649813","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649813","url":null,"abstract":"As the number of elderly persons increases, greater attention must be given to how they or their caregivers deal with emergency situations. This paper describes an automated tracking, fall detection, and emergency recovery system for elderly persons, and shows that efficient a Socially Assistive Robot (SAR) can resolve emergency situations and abnormal behaviors for at-risk populations. Our assistant robot uses position data provided by Ultra-WideBand (UWB) wireless network and motion sensor information to detect potentially dangerous situations for elderly persons. In this context, a deep neural network-based double-check method has been developed to detect and confirm fall situation with high accuracy using in-house developed sensory hardware. We then simulated four typical emergency scenarios using SILBOT-3 robot. Interaction scenarios were demonstrated to 28 caregivers, who were then invited to complete a short questionnaire regarding benefits and improvements for our system. Caregivers responded positively to our system's performance and stated that they would accept an assistant robot that could notify them quickly about a dangerous situation or possibly resolve the situation autonomously.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122509657","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 propose a simple controller design method for a level control system subject to valve stiction. The proposed method is based on fictitious reference iterative tuning (FRIT) technique which can be adopted to tune parameters of simple controllers such as PID controllers by using a one-shot experimental data. In our framework, a simple model of the valve stiction is constructed using a piecewise-linear function, whose inverse model is easily obtained and used as a stiction compensator. The proposed method is applied to a double-tank liquid level system with several types of PID controllers. Numerical simulation is performed to verify effectiveness of the proposed approach.
{"title":"Controller design for a level control system with valve stiction: A data-driven approach","authors":"Jakkarawut Khamkaew, Krittapas Phannachet, Tanagorn Jennawasin","doi":"10.23919/ICCAS52745.2021.9649769","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649769","url":null,"abstract":"We propose a simple controller design method for a level control system subject to valve stiction. The proposed method is based on fictitious reference iterative tuning (FRIT) technique which can be adopted to tune parameters of simple controllers such as PID controllers by using a one-shot experimental data. In our framework, a simple model of the valve stiction is constructed using a piecewise-linear function, whose inverse model is easily obtained and used as a stiction compensator. The proposed method is applied to a double-tank liquid level system with several types of PID controllers. Numerical simulation is performed to verify effectiveness of the proposed approach.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122840227","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：