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Sensors and Materials, Volume 31, Number 12(3) (2019)
Copyright(C) MYU K.K.
pp. 4113-4133
S&M2074 Research Paper of Special Issue
Published: December 26, 2019

Robot Collision Detection and Distinction Based on Convolution Filtering Dynamic Model [PDF]

Zhijing Li, Jinhua Ye, and Haibin Wu

(Received April 12, 2019; Accepted August 1, 2019)

Keywords: robot safety, force sensing, convolution filtering, collision detection, distinction method

With the increasing application of human–robot interaction, collision detection between robot and unknown environments, along with further distinction from the intentional contact between human and robot, have become urgent problems to be solved. In this paper, a new collision detection algorithm is proposed, and a collision distinction method is further designed on the basis of this algorithm. The generalized momentum and the convolution method are used to develop the robot convolution filtering dynamic model. Then, the force-sensing observer that uses only proprioceptive sensors is designed to observe the torque deviation of the joint online to realize robot collision detection. At the same time, the performance of the forcesensing observer is improved by compensating for joint friction. The proposed algorithm does not need any external sensors; it overcomes the disadvantage of calculation errors owing to the acquisition of joint acceleration information. The filter can be flexibly selected in the algorithm according to the actual application of the robot. Moreover, two force-sensing observers are adopted in the collision distinction method. The contact or collision between a human and a robot can be further distinguished after setting the appropriate thresholds and filtering parameters. The collision detection algorithm can be easily adapted to different types of robot to ensure human safety, and the proposed collision distinction method can be used to improve the work efficiency of the robot. External force sensing experiments show that the low-pass and bandpass observers work well and different force signals can be observed. The collision detection and human–robot interaction experiments are performed to verify that the collision detection algorithm and collision distinction method are reasonable and effective.

Corresponding author: Haibin Wu

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Cite this article
Zhijing Li, Jinhua Ye, and Haibin Wu, Robot Collision Detection and Distinction Based on Convolution Filtering Dynamic Model, Sens. Mater., Vol. 31, No. 12, 2019, p. 4113-4133.

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