Mechanical arm path planning method based on velocity smoothing deterministic policy gradient

Abstract

The invention discloses a mechanical arm path planning method based on velocity smoothing deterministic policy gradient. The method comprises the steps that a mechanical arm simulation environment with job task feedback is established in a training stage; a previous step mechanical arm action vector is introduced during inputting of a deterministic policy gradient network, and a reinforced learning network framework based on the velocity smoothing deterministic policy gradient is established; network training parameters and the mechanical arm simulation environment are initialized; and samplesare obtained based on the velocity smoothing deterministic policy gradient network and the simulation environment, a training sample database is established, if the training sample quantity reaches the maximum sample quantity, training samples are drawn from the training sample database according to the single time training sample quantity, the velocity smoothing deterministic policy gradient network is trained, and otherwise, next step or the next time of simulation is performed. According to the mechanical arm path planning method provided by the invention, the previous step velocity vectoris added as the network input on the basis of the deterministic policy gradient network, the joint acceleration is effectively decreased, and mechanical arm jitter is reduced.

Description

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AI Technical Summary

Problems solved by technology

However, since there is no smoothing process, the output motion vector will be quite different at the front and rear moments

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