Network optimization method for oblique-tip flexible needle path tracking based on deep reinforcement learning
A technology of reinforcement learning and optimization methods, applied in neural learning methods, biological neural network models, informatics, etc., can solve the problems of single puncture path and limited puncture range, and achieve the effect of avoiding data overflow and reducing tracking errors.
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[0053] figure 1 It is a flow chart of the optimization method of the action network for path tracking of oblique-tipped flexible needles based on deep reinforcement learning in the present invention. Such as figure 1 As shown, the method includes the following steps:
[0054] 1) Construct the simulation environment on the basis of the bicycle model of the oblique tip flexible needle proposed by Webster (Webster, and J.R.."Nonholonomic Modeling of Needle Steering."The International Journal of Robotics Research 25.5-6(2006):509-525. ). The simulation environment includes: a human tissue model, a model of a flexible needle with an oblique tip, a model of a motor used to control the rotation of the flexible needle with an oblique tip, and a model of a slide rail for pushing the flexible needle with an oblique tip forward. Among them, the inclined-tipped flexible needle is connected with the rotating motor, and the rotating motor is fixed on the slider in the guide rail.
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