Abstract: Collaborative industrial robot is becoming more and more important in the manufacturing industry. Thanks for a variety of high-precision sensors integrated by the Cyber-Physical System, the collaborative industrial robot can model the working environment and know its own state in real time. Because of this, CPS enables the robot to plan a feasible path in a virtual simulation environment. In this paper, a two-dimensional working space and a three-dimensional working space is constructed and set as a virtual environment model constructed by CPS. Q-learning algorithm is used to plan a path in the working space. A feasible path is found by adjusting the number of iteration times of the algorithm. Further more, the learning rate α of the Q-learning algorithm is also adjusted and the results demonstrate that the increase of α will accelerate the convergence speed of the algorithm within the set range.
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