The control strategy is tested in a real environment. We have carried out a large number of simulation experiments, and the error between the tracking of normal force and expected force is basically within ☐.5 N. We propose a constant force-tracking control method for dynamic environments and a modeling method that satisfies physical characteristics to simulate the dynamic breathing process and design an optimal reward function for the task of achieving efficient learning of the control strategy. Therefore, this paper investigates how to use deep reinforcement learning to solve dynamic medical auscultation tasks. Intelligent medical robots can effectively help doctors carry out a series of medical diagnoses and auxiliary treatments and alleviate the current shortage of social personnel.
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