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.
peg-in-hole assemblyforce-position perceptionskill learningattention mechanismimpedance control
维普
万方数据
