Research on Micro-flexible Wire Assembly Technology Based on Deep Reinforcement Learning
Traditional robot control methods are limited to fixed types and relatively regular incoming materials,and the assembly is completed through the position relationship.Due to the large morphological variation of the wire,it is difficult to achieve grasping and au-tomatic assembly,and the assembly success rate and yield of the wire are low.In order to solve the problem of micro-flexible wire as-sembly with a width less than 2 mm,a set of intelligent control algorithm for micro-flexible wire assembly based on deep reinforcement learning was designed by using multi-modal fusion technologies such as machine 3D vision sensing,force sensing,tactile sensing and proprioceptive sensing.On this basis,a set of experimental equipment composed of cooperative robot,6D force sensor and 3D machine vision system was built,and the assembly feasibility of this method was verified under multi-environment and uncertain factors.Based on the assembly requirements of high precision micro-flexible wire,the deep reinforcement learning multi-modal control method greatly improves the reliability and the success rate of assembly,while the assembly efficiency can be improved by more than 15％than tradi-tional control condition.The assembly accuracy of this test system can reach±0.1 mm,and the assembly success rate can reach more than 98％.
robotdeep reinforcement learningmulti-mode fusion technologyintelligent control algorithm
NETL
NSTL
万方数据
