Peg-in-hole compliant assembly method based on skill learning of force-position perception

doi:10.11996/JG.j.2095-302X.2024020250

Abstract

Abstract:

Traditional methods for robot peg-in-hole assembly face challenges in constructing accurate geometric contact models and learning methods that require large samples with a high initial attitude deviation leading to a low assembly success rate. A compliant robot peg-in-hole assembly method was proposed based on the skill learning of force-position perception. During the hole search stage, the force and torque sample data for the peg missing the hole were uniformly collected, constructing a force-action dataset. A multi-layer perceptron and an attention module network were constructed for supervised learning, generating a discriminant model for mapping force to action. Based on the six-dimensional force signal in the assembly process, the method predicted the next assembly action, while reducing both the angle and distance between the peg center line and hole center line to achieve proper alignment of the peg and the hole. During the hole insertion stage, a compliance control algorithm was designed with position control as its inner loop. By setting desired contact forces on the end face of the peg, real-time adjustments were made to both the position and orientation of peg parts using active compliance techniques based on feedback from a six-dimensional force sensor. To validate its effectiveness, 100 sets of assembly experiments were conducted using a single axle hole with a minimum clearance of 0.1 mm. The method achieved an average success rate of 94% within an average time of 15.1 seconds. Comparative analysis with other assembly algorithms demonstrated that the force-position perception assembly method based on skill learning significantly enhanced efficiency and success rate in peg-in-hole assemblies.

Key words: peg-in-hole assembly, force-position perception, skill learning, attention mechanism, impedance control

CLC Number:

TP391

LU Longfei, WANG Junfeng, ZHAO Shiwen, LI Guang, DING Xintao. Peg-in-hole compliant assembly method based on skill learning of force-position perception[J]. Journal of Graphics, 2024, 45(2): 250-258.

Figures/Tables 12

Fig. 1 Pose adjustment process of two-phase peg-in-hole assembly

Fig. 2 Peg-in-hole compliant assembly algorithm flow chart

Fig. 3 Force sample collection and direction label annotation ((a) Sample collection; (b) Label annotation)

Fig. 4 X-axis l force change curve during a sample collection

Fig. 5 Multilayer Perceptron and Self-attention networks

Fig. 6 Scaling dot product attention module

Fig. 7 Impedance control algorithm diagram

Fig. 8 Robot peg-in hole assembly system architecture diagram

Fig. 9 Composition of experimental platform

Fig. 10 Assembly process status ((a) Peg approaches hole; (b) Peg contacts hole; (c) Posture adjustment; (d) Position adjustment; (e) Peg and hole alignment; (f) Peg inserts hole)

Fig. 11 Curve of force and torque changing with time during peg and hole assembly process ((a) Force; (b) Torque)

Table 1 Experimental results of peg-in-hole assembly under different strategies

装配策略	装配成功率/%	平均时间/s	平均最大接触力/N
几何模型^[3]	91	26.4	12.47
SVM^[6]	86	17.8	21.62
GMR^[8]	84	16.9	14.56
本文方法	94	15.1	16.35

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