Pub Date : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268358
C. Yeoh, D. Kim, Y. B. Won, S. R. Lee, H. Yi
Robot Operating System, (ROS) is one of the open-source, meta-operating system, which is now widely used as the robotic software platform and can be applicable for anyone who wanted to build their robot from scratch. For the credit of the beneficial of ROS, the work of this paper describes all the process and structure of the package construction for the legged robot simulation in Gazebo. There are five mains folders consisted in the package, which are configuration file (config), launch file (launch), meshes folder (meshes), script folder (script), Universal robot definition format folder (urdf), and worlds folder (worlds). In this research, Pseudo-inverse Jacobian was implemented to obtain the optimal angular joint for every step walking during the simulation. Result of the walking robot simulation are shown to have the least error range around 0.0365 m to 0.0867 m differ from the actual target position.
{"title":"Constructing ROS Package for Legged Robot in Gazebo Simulation from Scratch","authors":"C. Yeoh, D. Kim, Y. B. Won, S. R. Lee, H. Yi","doi":"10.23919/ICCAS50221.2020.9268358","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268358","url":null,"abstract":"Robot Operating System, (ROS) is one of the open-source, meta-operating system, which is now widely used as the robotic software platform and can be applicable for anyone who wanted to build their robot from scratch. For the credit of the beneficial of ROS, the work of this paper describes all the process and structure of the package construction for the legged robot simulation in Gazebo. There are five mains folders consisted in the package, which are configuration file (config), launch file (launch), meshes folder (meshes), script folder (script), Universal robot definition format folder (urdf), and worlds folder (worlds). In this research, Pseudo-inverse Jacobian was implemented to obtain the optimal angular joint for every step walking during the simulation. Result of the walking robot simulation are shown to have the least error range around 0.0365 m to 0.0867 m differ from the actual target position.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"1 1","pages":"94-99"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74654237","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 : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268228
In-Deok Park, Sungho Kim
In recent image processing, beyond the object recognition problem, deep learning has been used in various aspects such as object detection, semantic segmentation. In addition, classic technique-based detection has been performed variously. These technologies are applied in various systems such as factory automation systems, automatic target recognition (ATR) systems, autonomous driving systems, etc. Object detection is performed in various categories such as people, vehicles and animals, etc. And it is operated for various situations which contain different object size, image size, distance range from near to remote, changeable environment, etc. For the situation analysis, indicators need to be used appropriately. And when researchers make some algorithm for object detection, if there are no any evaluation indicators, the algorithm can’t be demonstrated. So, it is important to know about performance indicators of object detection. Various indicators are used in object detection. As a result, this paper introduces performance indicators of object detection. The main purpose of the survey is that researchers find the proper performance indicator for object detection. And It can help to compare the detection result with a different algorithm result, exactly and effectively.
{"title":"Performance Indicator Survey for Object Detection","authors":"In-Deok Park, Sungho Kim","doi":"10.23919/ICCAS50221.2020.9268228","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268228","url":null,"abstract":"In recent image processing, beyond the object recognition problem, deep learning has been used in various aspects such as object detection, semantic segmentation. In addition, classic technique-based detection has been performed variously. These technologies are applied in various systems such as factory automation systems, automatic target recognition (ATR) systems, autonomous driving systems, etc. Object detection is performed in various categories such as people, vehicles and animals, etc. And it is operated for various situations which contain different object size, image size, distance range from near to remote, changeable environment, etc. For the situation analysis, indicators need to be used appropriately. And when researchers make some algorithm for object detection, if there are no any evaluation indicators, the algorithm can’t be demonstrated. So, it is important to know about performance indicators of object detection. Various indicators are used in object detection. As a result, this paper introduces performance indicators of object detection. The main purpose of the survey is that researchers find the proper performance indicator for object detection. And It can help to compare the detection result with a different algorithm result, exactly and effectively.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"45 1","pages":"284-288"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78694471","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 : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268307
L. Ventura, S. Malan
Emission regulations are becoming more and more stringent, especially on NOx pollutants, making diesel engines with their embedded control systems more and more complex. To ensure a correct and clean engine functioning, all the control strategies related to aftertreatment, fuel injection and air-path have to exploit or target the intake manifold O2 concentration. The O2 concentration is strictly related to engine-out NOx emissions and an accurate model, to be implemented in emission control systems, is essential. The paper addresses the modeling of the intake O2 concentration in a turbocharged diesel engine by means of a Recurrent Neural Network with simulation focus and fed with four inputs. The inputs are engine load, engine speed and the position of Exhaust Gas Recirculation and Variable Geometry Turbochargers valves. Training and validation data are generated using the engine simulation tool GT-Power implementing a detailed model of the engine while the training procedure is performed in MATLAB environment through NNSYSID toolbox. The performances of the obtained model are satisfactory in different tests and the model is able to account for the engine nonlinearities during transients.
{"title":"Recurrent Neural Network to Estimate Intake Manifold O2 Concentration in a Diesel Engine","authors":"L. Ventura, S. Malan","doi":"10.23919/ICCAS50221.2020.9268307","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268307","url":null,"abstract":"Emission regulations are becoming more and more stringent, especially on NOx pollutants, making diesel engines with their embedded control systems more and more complex. To ensure a correct and clean engine functioning, all the control strategies related to aftertreatment, fuel injection and air-path have to exploit or target the intake manifold O2 concentration. The O2 concentration is strictly related to engine-out NOx emissions and an accurate model, to be implemented in emission control systems, is essential. The paper addresses the modeling of the intake O2 concentration in a turbocharged diesel engine by means of a Recurrent Neural Network with simulation focus and fed with four inputs. The inputs are engine load, engine speed and the position of Exhaust Gas Recirculation and Variable Geometry Turbochargers valves. Training and validation data are generated using the engine simulation tool GT-Power implementing a detailed model of the engine while the training procedure is performed in MATLAB environment through NNSYSID toolbox. The performances of the obtained model are satisfactory in different tests and the model is able to account for the engine nonlinearities during transients.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"129 1","pages":"715-720"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77275885","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 : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268347
Jee-seong Kim, Chul-hong Kim, Yong-Min Shin, Ilsoo Cho, D. Cho
An outdoor environment is challenging for the localization of a mobile robot. For robust visual odometry, accurate feature matching and triangulation are essential. The features extracted from the windows of buildings and car surfaces lead to wrong triangulation results due to reflective features. The landmarks at short-distances affect the feature matching performance and the landmarks at long-distances cause triangulation errors. Inaccurate feature matching and triangulation error lead to the localization error of the robot pose. In this paper, an outdoor monocular visual odometry using the pre-trained depth estimation network and semantic segmentation network is proposed. By using the pre-trained semantic segmentation network, a semantic label is predicted for every pixel. Also, by using the pre-trained depth map estimation network, the depth of every pixel is predicted. Using semantic constraints for feature matching and depth constraint for triangulation, the accuracy of these procedures is enhanced. Additionally, pose graph optimization is performed on every estimated robot pose and landmark position. The performance of the proposed method is evaluated using dataset-based experiments. The experiments showed that the proposed algorithm is more accurate than the visual odometry algorithm that uses Oriented FAST and rotated BRIEF (ORB) features.
{"title":"Outdoor Monocular Visual Odometry Enhancement Using Depth Map and Semantic Segmentation","authors":"Jee-seong Kim, Chul-hong Kim, Yong-Min Shin, Ilsoo Cho, D. Cho","doi":"10.23919/ICCAS50221.2020.9268347","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268347","url":null,"abstract":"An outdoor environment is challenging for the localization of a mobile robot. For robust visual odometry, accurate feature matching and triangulation are essential. The features extracted from the windows of buildings and car surfaces lead to wrong triangulation results due to reflective features. The landmarks at short-distances affect the feature matching performance and the landmarks at long-distances cause triangulation errors. Inaccurate feature matching and triangulation error lead to the localization error of the robot pose. In this paper, an outdoor monocular visual odometry using the pre-trained depth estimation network and semantic segmentation network is proposed. By using the pre-trained semantic segmentation network, a semantic label is predicted for every pixel. Also, by using the pre-trained depth map estimation network, the depth of every pixel is predicted. Using semantic constraints for feature matching and depth constraint for triangulation, the accuracy of these procedures is enhanced. Additionally, pose graph optimization is performed on every estimated robot pose and landmark position. The performance of the proposed method is evaluated using dataset-based experiments. The experiments showed that the proposed algorithm is more accurate than the visual odometry algorithm that uses Oriented FAST and rotated BRIEF (ORB) features.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"171 1","pages":"1040-1045"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77706230","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 : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268238
Jaekuk Choi, Dongsun Lim, Sangwon Choi, Jeonghyeon Kim, Jong-Heon Kim
This paper is on the implementation of a system that monitors and controls a smart farm using Arduino and DC motors. Light control in traditional smart farms uses artificial light such as LED lights to control brightness. However, this traditional method has high maintenance cost for continuously turning on artificial light. In this paper, we develop a system to control the amount of light inflow using the angle control of the reflector. Also, we monitor environmental information such as temperature, humidity, carbon dioxide(Co2), and light value for the optimum smart farm environment. The temperature is controlled using the ventilator and heater. Also, environmental data can be uploaded to the server in real-time to check the accumulated data on a chart, and we accumulate the optimal reflector angle data for more than one year. Since solar motion repeats every year, we can control the reflector according to this accumulated data. This system has been implemented as a server and mobile application that provides various sensors for environmental control, Arduino, Wemos for Wifi server upload, and a monitoring UI.
{"title":"Light Control Smart Farm Monitoring System with Reflector Control","authors":"Jaekuk Choi, Dongsun Lim, Sangwon Choi, Jeonghyeon Kim, Jong-Heon Kim","doi":"10.23919/ICCAS50221.2020.9268238","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268238","url":null,"abstract":"This paper is on the implementation of a system that monitors and controls a smart farm using Arduino and DC motors. Light control in traditional smart farms uses artificial light such as LED lights to control brightness. However, this traditional method has high maintenance cost for continuously turning on artificial light. In this paper, we develop a system to control the amount of light inflow using the angle control of the reflector. Also, we monitor environmental information such as temperature, humidity, carbon dioxide(Co2), and light value for the optimum smart farm environment. The temperature is controlled using the ventilator and heater. Also, environmental data can be uploaded to the server in real-time to check the accumulated data on a chart, and we accumulate the optimal reflector angle data for more than one year. Since solar motion repeats every year, we can control the reflector according to this accumulated data. This system has been implemented as a server and mobile application that provides various sensors for environmental control, Arduino, Wemos for Wifi server upload, and a monitoring UI.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"59 1","pages":"69-74"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78025489","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 : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268273
Seyeong Cheon, Gyujin Na, Y. Eun
Disturbance Observer (DOB) is a well known tool for control systems capable of compensating model un-certainty and rejecting exogenous disturbances. It has been reported previously that zero mean measurement noise may induce tracking error in DOB based control systems with a saturation nonlinearity due to saturating actuators. In this work, DOB based control systems with two saturation constraints are considered and the effect of measurement noise is investigated. Such system architecture arises from Robust Transient DOB (RTDOB) which intentionally bounds amplitude of disturbance compensation in order to avoid peaking phenomenon that can possibly worsen transient response of the closed loop system. Systems using RTDOB have two saturation constraints, one that naturally arises from saturating actuator, and the other artificially inserted to limit the amplitude of disturbance compensation. We show that zero mean measurement noise may induce tracking error in RTDOB based systems as well, analyze the amplitude of the error with systems parameters, and discuss its severity compared to that of a regular DOB based system. The accuracy of proposed analysis is demonstrated through simulations.
{"title":"Effect of Measurement Noise in Disturbance Observer Based Control Systems with Two Saturation Constraints","authors":"Seyeong Cheon, Gyujin Na, Y. Eun","doi":"10.23919/ICCAS50221.2020.9268273","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268273","url":null,"abstract":"Disturbance Observer (DOB) is a well known tool for control systems capable of compensating model un-certainty and rejecting exogenous disturbances. It has been reported previously that zero mean measurement noise may induce tracking error in DOB based control systems with a saturation nonlinearity due to saturating actuators. In this work, DOB based control systems with two saturation constraints are considered and the effect of measurement noise is investigated. Such system architecture arises from Robust Transient DOB (RTDOB) which intentionally bounds amplitude of disturbance compensation in order to avoid peaking phenomenon that can possibly worsen transient response of the closed loop system. Systems using RTDOB have two saturation constraints, one that naturally arises from saturating actuator, and the other artificially inserted to limit the amplitude of disturbance compensation. We show that zero mean measurement noise may induce tracking error in RTDOB based systems as well, analyze the amplitude of the error with systems parameters, and discuss its severity compared to that of a regular DOB based system. The accuracy of proposed analysis is demonstrated through simulations.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"233 1","pages":"244-250"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80289838","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 : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268272
Ryo Yonemoto, T. Hirogaki, E. Aoyama
This study investigates an improved method for super finishing of a die surface by means of a robot that holds fixed abrasive grains. A low-pressure grinding method was previously investigated to exploit the characteristics of the fine grinding stone by means of a voice coil motor (VCM). However, the VCM proved disadvantageous in that the grinding pressure changed when the height of the grinding surface was changed or the volume of the grinding stone was reduced. To solve this problem, a feedback control mechanism of the force was employed in this study, such that a constant pressure could be achieved for long periods of polishing. It was found that this allowed for the stable execution of long-term polishing, along with the previously set low pressure being maintained simultaneously with high accuracy. In addition, increased improvement of surface roughness was also achieved because polishing can be carried out at high pressures for a long period of time without any decrease in pressure occurring.
{"title":"Development of abrasive super finishing method with a five-axis closed-link compact robot and fine diamond stone","authors":"Ryo Yonemoto, T. Hirogaki, E. Aoyama","doi":"10.23919/ICCAS50221.2020.9268272","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268272","url":null,"abstract":"This study investigates an improved method for super finishing of a die surface by means of a robot that holds fixed abrasive grains. A low-pressure grinding method was previously investigated to exploit the characteristics of the fine grinding stone by means of a voice coil motor (VCM). However, the VCM proved disadvantageous in that the grinding pressure changed when the height of the grinding surface was changed or the volume of the grinding stone was reduced. To solve this problem, a feedback control mechanism of the force was employed in this study, such that a constant pressure could be achieved for long periods of polishing. It was found that this allowed for the stable execution of long-term polishing, along with the previously set low pressure being maintained simultaneously with high accuracy. In addition, increased improvement of surface roughness was also achieved because polishing can be carried out at high pressures for a long period of time without any decrease in pressure occurring.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"91 1","pages":"88-93"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80500460","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 : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268257
Ja-Ram Jang, J. Park
In human-robot collaborative manipulation of an object, if the robot knows the intention of the human, the efficiency of the work would greatly increase. For the robot to know of the human intention, it should have the information of the force applied by the human, which can be more accurately if it can estimate the inertial and dimensional parameters online. However, the force applied by the human will disturb the parameter identification process. This paper presents a strategy to identify the inertial and dimensional parameters of an unknown object online for physical human-robot interactions. Extended Kalman filter is used for identification under the assumption that the force applied by the human is an unknown external disturbance. This approach was evaluated in simulations of physical human-robot object manipulation task.
{"title":"Parameter Identification of an Unknown Object in Human-Robot Collaborative Manipulation","authors":"Ja-Ram Jang, J. Park","doi":"10.23919/ICCAS50221.2020.9268257","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268257","url":null,"abstract":"In human-robot collaborative manipulation of an object, if the robot knows the intention of the human, the efficiency of the work would greatly increase. For the robot to know of the human intention, it should have the information of the force applied by the human, which can be more accurately if it can estimate the inertial and dimensional parameters online. However, the force applied by the human will disturb the parameter identification process. This paper presents a strategy to identify the inertial and dimensional parameters of an unknown object online for physical human-robot interactions. Extended Kalman filter is used for identification under the assumption that the force applied by the human is an unknown external disturbance. This approach was evaluated in simulations of physical human-robot object manipulation task.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"211 1","pages":"1086-1091"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79399699","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 : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268434
N. Reginald, J. Seo, Abdullah Rasul
The Excavator is one of the key equipment utilized for earthmoving tasks at construction sites. This paper aims to provide an integrative tracking control strategy comprising of position, contour, and force tracking controls for excavation tasks. A non-linear proportional-integral controller was applied for position control of hydraulic actuators and the contour control strategy was added to create an optimal path of the bucket tip minimizing contour errors. The force control was finally considered to compensate for the ground resistive force. A multiphysics simulation model was developed for an evaluation of the designed controller’s performance through co-simulation. Experimental results obtained from a test platform show that the developed control algorithms provide good tracking performance for soil digging.
{"title":"Development of an Integrated Tracking Control Algorithm for Digging Operations of an Excavator","authors":"N. Reginald, J. Seo, Abdullah Rasul","doi":"10.23919/ICCAS50221.2020.9268434","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268434","url":null,"abstract":"The Excavator is one of the key equipment utilized for earthmoving tasks at construction sites. This paper aims to provide an integrative tracking control strategy comprising of position, contour, and force tracking controls for excavation tasks. A non-linear proportional-integral controller was applied for position control of hydraulic actuators and the contour control strategy was added to create an optimal path of the bucket tip minimizing contour errors. The force control was finally considered to compensate for the ground resistive force. A multiphysics simulation model was developed for an evaluation of the designed controller’s performance through co-simulation. Experimental results obtained from a test platform show that the developed control algorithms provide good tracking performance for soil digging.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"88 24 1","pages":"195-200"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84072313","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 : 2020-10-13DOI: 10.23919/ICCAS50221.2020.9268404
Panashe Sabau, J. Chong, A. Jafari, Subham Agrawal, C. Semasinghe, Appolinaire C. Etoundi
In the past century many medical advancements in prosthetics have been achieved, however, discomfort in prosthetic socket remains one of the toughest challenges faced by both amputees and prosthetists. Wearing an uncomfortable socket can lead to users discontinuing use of their socket and subsequently reducing their long-term mobility; negatively impact their psychological health; and prolong rehabilitation. This paper continues the research conducted in earlier publications [1], [2], which introduced the concept of an automated ISO standard robotic testing rig to test a full artificial limb prosthesis (a bio-inspired transfemoral prosthetic socket attached to robotic prosthetic joints and an ankle joint). This paper presents an automated method of designing the bio-inspired socket using artificial intelligence to reduce discomfort and the design time of new or existing full artificial lower limbs using qualitative and quantitative data. The socket will be tested in a gait simulation shown in the figure 7, to safely achieve desirable walking velocities, step length, safety and comfort while consequentially reducing the physical testing on patients and consequentially reduce physical testing on patients.
{"title":"Application of Machine Learning Towards Design Optimisation of Bio-inspired Transfemoral Prosthetic Socket for Robotic Leg Test Rig","authors":"Panashe Sabau, J. Chong, A. Jafari, Subham Agrawal, C. Semasinghe, Appolinaire C. Etoundi","doi":"10.23919/ICCAS50221.2020.9268404","DOIUrl":"https://doi.org/10.23919/ICCAS50221.2020.9268404","url":null,"abstract":"In the past century many medical advancements in prosthetics have been achieved, however, discomfort in prosthetic socket remains one of the toughest challenges faced by both amputees and prosthetists. Wearing an uncomfortable socket can lead to users discontinuing use of their socket and subsequently reducing their long-term mobility; negatively impact their psychological health; and prolong rehabilitation. This paper continues the research conducted in earlier publications [1], [2], which introduced the concept of an automated ISO standard robotic testing rig to test a full artificial limb prosthesis (a bio-inspired transfemoral prosthetic socket attached to robotic prosthetic joints and an ankle joint). This paper presents an automated method of designing the bio-inspired socket using artificial intelligence to reduce discomfort and the design time of new or existing full artificial lower limbs using qualitative and quantitative data. The socket will be tested in a gait simulation shown in the figure 7, to safely achieve desirable walking velocities, step length, safety and comfort while consequentially reducing the physical testing on patients and consequentially reduce physical testing on patients.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"39 1","pages":"396-401"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84169013","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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