S. Bhattacharya, A. Luo, S. Dutta, M. Miura-Mattausch, H. Mattausch
{"title":"基于外部控制电路的人形机器人表面识别与速度调节","authors":"S. Bhattacharya, A. Luo, S. Dutta, M. Miura-Mattausch, H. Mattausch","doi":"10.1109/ISDCS49393.2020.9263013","DOIUrl":null,"url":null,"abstract":"We propose a surface-recognition-based speed- adjustment system for humanoid robot during walking on different surfaces. Two different types of surfaces are considered in the reported experiment (example, rough and smooth surface). Force-sensors, classification unit and external- controller-circuits are applied for surface-recognition and speed-adjustment. For surface-recognition the Euclidean distance is used to calculate the nearest-neighbor reference pattern for the feature of the waking pattern generated online. The mean-absolute-value (MAV) feature vector is used to classify two different surfaces. To distinguish two different surfaces, the hardware accelerated decision-signals are generated across LEDs in the form of analog voltages (maximum peak voltage 212 mV for rough-surface and 147 mV for smooth surfaces respectively with detection time 2.8 s and 1.5 s). The external-controller-circuit is used for speed- adjustment using decision-signal coming from LED. It is observed that, when robot is moving from rough-surfaces to smooth-surfaces, the speed of the robot motion changes from 190 frames/stride (i.e. slow-speed) to 160 frames/stride (i.e. medium-speed) with 4.9 s transition time, whereas from smooth- surface to rough-surface transitions, the transition time takes 4.5 s. The experimentally measurement results of speed- adjustment time after surface transition are useful for fast and stable recognition-system design.","PeriodicalId":177307,"journal":{"name":"2020 International Symposium on Devices, Circuits and Systems (ISDCS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Surface Recognition and Speed Adjustment of Humanoid Robot Using External Control Circuit\",\"authors\":\"S. Bhattacharya, A. Luo, S. Dutta, M. Miura-Mattausch, H. Mattausch\",\"doi\":\"10.1109/ISDCS49393.2020.9263013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a surface-recognition-based speed- adjustment system for humanoid robot during walking on different surfaces. Two different types of surfaces are considered in the reported experiment (example, rough and smooth surface). Force-sensors, classification unit and external- controller-circuits are applied for surface-recognition and speed-adjustment. For surface-recognition the Euclidean distance is used to calculate the nearest-neighbor reference pattern for the feature of the waking pattern generated online. The mean-absolute-value (MAV) feature vector is used to classify two different surfaces. To distinguish two different surfaces, the hardware accelerated decision-signals are generated across LEDs in the form of analog voltages (maximum peak voltage 212 mV for rough-surface and 147 mV for smooth surfaces respectively with detection time 2.8 s and 1.5 s). The external-controller-circuit is used for speed- adjustment using decision-signal coming from LED. It is observed that, when robot is moving from rough-surfaces to smooth-surfaces, the speed of the robot motion changes from 190 frames/stride (i.e. slow-speed) to 160 frames/stride (i.e. medium-speed) with 4.9 s transition time, whereas from smooth- surface to rough-surface transitions, the transition time takes 4.5 s. The experimentally measurement results of speed- adjustment time after surface transition are useful for fast and stable recognition-system design.\",\"PeriodicalId\":177307,\"journal\":{\"name\":\"2020 International Symposium on Devices, Circuits and Systems (ISDCS)\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Symposium on Devices, Circuits and Systems (ISDCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISDCS49393.2020.9263013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Symposium on Devices, Circuits and Systems (ISDCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDCS49393.2020.9263013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Surface Recognition and Speed Adjustment of Humanoid Robot Using External Control Circuit
We propose a surface-recognition-based speed- adjustment system for humanoid robot during walking on different surfaces. Two different types of surfaces are considered in the reported experiment (example, rough and smooth surface). Force-sensors, classification unit and external- controller-circuits are applied for surface-recognition and speed-adjustment. For surface-recognition the Euclidean distance is used to calculate the nearest-neighbor reference pattern for the feature of the waking pattern generated online. The mean-absolute-value (MAV) feature vector is used to classify two different surfaces. To distinguish two different surfaces, the hardware accelerated decision-signals are generated across LEDs in the form of analog voltages (maximum peak voltage 212 mV for rough-surface and 147 mV for smooth surfaces respectively with detection time 2.8 s and 1.5 s). The external-controller-circuit is used for speed- adjustment using decision-signal coming from LED. It is observed that, when robot is moving from rough-surfaces to smooth-surfaces, the speed of the robot motion changes from 190 frames/stride (i.e. slow-speed) to 160 frames/stride (i.e. medium-speed) with 4.9 s transition time, whereas from smooth- surface to rough-surface transitions, the transition time takes 4.5 s. The experimentally measurement results of speed- adjustment time after surface transition are useful for fast and stable recognition-system design.