{"title":"力传感装置的校准、故障检测和恢复","authors":"Yifang Zhang;Arash Ajoudani;Nikos G. Tsagarakis","doi":"10.1109/LRA.2024.3458807","DOIUrl":null,"url":null,"abstract":"Ground reaction force information, which includes the location of the center of pressure (COP) and vertical ground reaction force (vGRF), has various applications, such as in the gait assessment of patients post-injury or in the control of robot prostheses and exoskeleton devices. At the beginning of this work, we introduce a newly developed force-sensing device for measuring the COP and vGRF. Then, a model-free calibration method is proposed, leveraging Gaussian process regression (GPR) to extract COP and vGRF from raw sensor data. This approach yields remarkably low normalized root mean squared errors (NRMSEs) of 0.029 and 0.020 for COP in the mediolateral and anteroposterior directions, respectively, and 0.024 for vGRF. However, in general, learning-based calibration methods are sensitive to abnormal readings from sensing elements. To improve the robustness of the measurement, a GPR-based fault detection network is outlined for evaluating the sensing state within the fault in individual sensing elements of the force-sensing device. Moreover, a GPR-based recovery method is proposed to retrieve the sensing device's function under the fault conditions. In validation experiments, the effect of the scale factor of the threshold in the fault detection network is experimentally analyzed. The fault detection network can achieve over 90% success rate with a lower than 5 seconds delay on average in detecting the fault when the scale factor is between 1.68 and 1.90. The engagement of GPR-based recovery models under fault conditions demonstrates a substantial enhancement in COP (up to 85.0% improvement) and vGRF (up to 84.8% improvement) estimation accuracy.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675440","citationCount":"0","resultStr":"{\"title\":\"On the Calibration, Fault Detection and Recovery of a Force Sensing Device\",\"authors\":\"Yifang Zhang;Arash Ajoudani;Nikos G. Tsagarakis\",\"doi\":\"10.1109/LRA.2024.3458807\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ground reaction force information, which includes the location of the center of pressure (COP) and vertical ground reaction force (vGRF), has various applications, such as in the gait assessment of patients post-injury or in the control of robot prostheses and exoskeleton devices. At the beginning of this work, we introduce a newly developed force-sensing device for measuring the COP and vGRF. Then, a model-free calibration method is proposed, leveraging Gaussian process regression (GPR) to extract COP and vGRF from raw sensor data. This approach yields remarkably low normalized root mean squared errors (NRMSEs) of 0.029 and 0.020 for COP in the mediolateral and anteroposterior directions, respectively, and 0.024 for vGRF. However, in general, learning-based calibration methods are sensitive to abnormal readings from sensing elements. To improve the robustness of the measurement, a GPR-based fault detection network is outlined for evaluating the sensing state within the fault in individual sensing elements of the force-sensing device. Moreover, a GPR-based recovery method is proposed to retrieve the sensing device's function under the fault conditions. In validation experiments, the effect of the scale factor of the threshold in the fault detection network is experimentally analyzed. The fault detection network can achieve over 90% success rate with a lower than 5 seconds delay on average in detecting the fault when the scale factor is between 1.68 and 1.90. The engagement of GPR-based recovery models under fault conditions demonstrates a substantial enhancement in COP (up to 85.0% improvement) and vGRF (up to 84.8% improvement) estimation accuracy.\",\"PeriodicalId\":13241,\"journal\":{\"name\":\"IEEE Robotics and Automation Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675440\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Robotics and Automation Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10675440/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10675440/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
On the Calibration, Fault Detection and Recovery of a Force Sensing Device
Ground reaction force information, which includes the location of the center of pressure (COP) and vertical ground reaction force (vGRF), has various applications, such as in the gait assessment of patients post-injury or in the control of robot prostheses and exoskeleton devices. At the beginning of this work, we introduce a newly developed force-sensing device for measuring the COP and vGRF. Then, a model-free calibration method is proposed, leveraging Gaussian process regression (GPR) to extract COP and vGRF from raw sensor data. This approach yields remarkably low normalized root mean squared errors (NRMSEs) of 0.029 and 0.020 for COP in the mediolateral and anteroposterior directions, respectively, and 0.024 for vGRF. However, in general, learning-based calibration methods are sensitive to abnormal readings from sensing elements. To improve the robustness of the measurement, a GPR-based fault detection network is outlined for evaluating the sensing state within the fault in individual sensing elements of the force-sensing device. Moreover, a GPR-based recovery method is proposed to retrieve the sensing device's function under the fault conditions. In validation experiments, the effect of the scale factor of the threshold in the fault detection network is experimentally analyzed. The fault detection network can achieve over 90% success rate with a lower than 5 seconds delay on average in detecting the fault when the scale factor is between 1.68 and 1.90. The engagement of GPR-based recovery models under fault conditions demonstrates a substantial enhancement in COP (up to 85.0% improvement) and vGRF (up to 84.8% improvement) estimation accuracy.
期刊介绍:
The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.