Signal Processing and Application of Six-axis Force/Torque Sensor Integrated in Humanoid Robot Foot

Yan, Qingquan; Luo, Jianfei; Zhang, Wu

doi:10.1007/s11265-013-0796-5

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…However, this is not always practical. In Wu et al ( 2014 ), a comparison amongst different filters to alleviate the noise effects on F/T signals is presented. A performance measure, called stability index, was used to evaluate those different filters.…”

Section: Methodsmentioning

confidence: 99%

Symbolic-Based Recognition of Contact States for Learning Assembly Skills

Al-Yacoub

Zhao²,

Lohse

et al. 2019

Front. Robot. AI

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Imitation learning is gaining more attention because it enables robots to learn skills from human demonstrations. One of the major industrial activities that can benefit from imitation learning is the learning of new assembly processes. An essential characteristic of an assembly skill is its different contact states (CS). They determine how to adjust movements in order to perform the assembly task successfully. Humans can recognize CSs through haptic feedback. They execute complex assembly tasks accordingly. Hence, CSs are generally recognized using force and torque information. This process is not straightforward due to the variations in assembly tasks, signal noise and ambiguity in interpreting force/torque (F/T) information. In this research, an investigation has been conducted to recognize the CSs during an assembly process with a geometrical variation on the mating parts. The F/T data collected from several human trials were pre-processed, segmented and represented as symbols. Those symbols were used to train a probabilistic model. Then, the trained model was validated using unseen datasets. The primary goal of the proposed approach aims to improve recognition accuracy and reduce the computational effort by employing symbolic and probabilistic approaches. The model successfully recognized CS based only on force information. This shows that such models can assist in imitation learning.

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Section: Methodsmentioning

confidence: 99%

Symbolic-Based Recognition of Contact States for Learning Assembly Skills

Al-Yacoub

Zhao²,

Lohse

et al. 2019

Front. Robot. AI

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“…If faults are found only in some actuators, then multiple simultaneous faults can be isolated from many actuators. Wu (2014) analyzed output signals and developed an anti-interference processing method for force and torque sensors using various signal-processing methods to stabilize the control of the ankle of a humanoid robot (Wu et al, 2014). To verify the feasibility of this method, the authors carried out some experiments on the dynamic walking motion of the humanoid robot platform.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of a selective compliance assembly robot arm manipulator based on the end joint motion analysis: Threshold algorithm and experiments

Fei

Yang

et al. 2017

Transactions of the Institute of Measurement and Control

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This paper presented a method to carry out fault diagnosis via analyzing the motion signals of a SCARA. To analyze the motion signals of the end joint of a selective compliance assembly robot arm (SCARA) and carry out fault diagnosis. A model parameter-based threshold algorithm is proposed in this study to improve the efficiency of the fault diagnosis on the end joint of a SCARA manipulator. The operation state of the robot is determined by comparing the speed curve of the end joint of the robot with the threshold using the proposed algorithm. Firstly, the threshold range of the system output is estimated using the speed observer constructed via parameter separation. Secondly, the acceleration signals of the end joint of the robot are collected at various operational angular speeds by a single acceleration sensor installed at the end joint of the manipulator. The operation state of the robot is evaluated by analyzing the trend and vibration characteristics of its acceleration. Finally, experiments are conducted at three different speeds: 2.4rad/s, 3.12rad/s and 3.6rad/s. Some robot malfunctions are detected by comparing the actual speed with the threshold. Thus, the proposed method can be used to monitor the variation signal in each robot joint through a single accelerometer mounted on the top of the manipulator.

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“…Noise and disturbances occurring at high frequencies can be greatly reduced using classical filters such as lowpass filter [19]. Furthermore, disturbances due to robot motion can not be omitted especially in high speed applications or when heavy robot tools are used.…”

Section: Introductionmentioning

confidence: 99%