  Multiple peg-in-hole assembly is one of the fundamental tasks in robotic
assembly. In the multiple peg-in-hole task for large-sized parts, it is
challenging for a single manipulator to simultaneously align multiple distant
pegs and holes, necessitating tightly coupled multi-manipulator systems. For
such Multi-manipulator Multiple Peg-in-Hole (MMPiH) tasks, we proposes a
collaborative visual servo control framework that uses only the monocular
in-hand cameras of each manipulator to reduce positioning errors. Initially, we
train a state classification neural network and a positioning neural network.
The former is used to divide the states of peg and hole in the image into three
categories: obscured, separated and overlapped, while the latter determines the
position of the peg and hole in the image. Based on these findings, we propose
a method to integrate the visual features of multiple manipulators using
virtual forces, which can naturally combine with the cooperative controller of
the multi-manipulator system. To generalize our approach to holes of different
appearances, we varied the appearance of the holes during the dataset
generation process. The results confirm that by considering the appearance of
the holes, classification accuracy and positioning precision can be improved.
Finally, the results show that our method achieves an 85% success rate in
dual-manipulator dual peg-in-hole tasks with a clearance of 0.2 mm.
