A flexible assembly strategy for a 6-DOF robotic arm based on deep reinforcement learning
To address the problems of the current 3C assembly methods,such as high assembly complexity,high flexibility and excessive parts,we build a 3C assembly system for robotic arm by employing a deep reinforcement learning method.First,the MuJoCo physics engine is used to model the assembly task of the robot arm and build a simulation environment.The task is modeled as a Markov process problem.The state space and action space of the system are designed and the problem of sparse reward is alleviated through the design of reward function.A series of simulation experiments show our proposed assembly system efficiently performs the 3C assembly task and achieves good accuracy and stability.Moreover,the assembly success rate reaches 93%.Meanwhile,after the reinforcement learning strategy training,the contact force reaches the set range and achieves flexible assembly,greatly improving the production efficiency and product quality.
deep reinforcement learning3C assemblymanipulator control
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