Force-Sensor-Based Surface Recognition With Surface-Property-Dependent Walking-Speed Adjustment of Humanoid Robot

被引:3
作者
Bhattacharya, Sandip [1 ]
Luo, Aiwen [1 ,2 ]
Dutta, Sunandan [1 ]
Miura-Mattausch, Mitiko [1 ]
Mattausch, Hans Jurgen [1 ]
机构
[1] Hiroshima Univ, HiSIM Res Ctr, Hiroshima 7398530, Japan
[2] Jinan Univ, Coll Informat Sci & Technol, Guangzhou 510000, Peoples R China
来源
IEEE ACCESS | 2020年 / 8卷 / 08期
关键词
Legged locomotion; Robot sensing systems; Humanoid robots; Rough surfaces; Surface roughness; Force sensors; Humanoid robot; force sensor; microcomputer; Euclidean distance; application specific motion file (ASMF); MYOELECTRIC CONTROL; CLASSIFICATION; SCHEME;
D O I
10.1109/ACCESS.2020.3023995
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We report the development of a biped-robot system with real-time surface recognition and walking-speed adjustment to control the robot motion during walking on different types of surfaces. Four types of test surfaces (i.e. rough foam (RF), smooth foam (SF), thin carpet (TC) and smooth table (ST)) are considered in the system verification. For surface-property recognition we use ultra-thin-membrane force sensors, mounted under the robot feet, and a classification circuit, implemented on an Arduino Uno board. The walking-speed adjustment is performed with an external control circuit, which receives the surface-recognition signal from the classification circuit and sends a feedback signal to the robot controller (i.e. RCB-4HV) for adjusting the walking speed accordingly. We applied the nearest-neighbor-classification algorithm with the Euclidean-distance measure and a set of reference data, to distinguish between the four test surfaces based on the robot's real-time walking pattern. The mean absolute value (MAV) feature descriptor is used to generate four different types of reference walking pattern, corresponding to the four different surfaces. In our experiments it is observed, that the ST surface performs best in terms of average surface-recognition latency (SRL) (similar to 3.6 sec) during walking on same surface. On the other hand, the surface transition from TC to SF showed minimum surface-transition latency (STL) (similar to 8.2 sec) with correct speed change from 135 to 160 robot-motor-configuration frames per stride (frames/stride), while the transition from SF to TC surfaces showed maximum STL (similar to 11.6 sec) including speed change from 160 to 135 frames/stride. The obtained results are useful for development of the next generation of surface-recognition and speed-adjustment systems, implemented in humanoid robots to enable balanced and stable walking in environments with multiple changed surface properties.
引用
收藏
页码:169640 / 169651
页数:12
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