Trajectory planning of robotic arm based on the gaussian decaying noise DDPG algorithm
The issue of DDPG algorithm training failure caused by inappropriate values of Gaussian noise standard deviation in the trajectory planning task of agricultural picking robotic arms was investigated.To address this problem,a decaying normal noise DDPG algorithm was proposed,where the Gaussian standard deviation σ decreases as the number of training episodes increases.Multiple simulation training sessions using the Mujoco physics engine were conducted to verify the advantages of decaying normal noise over traditional normal noise in trajectory planning tasks.The results showed that the improved algorithm was more effective in completing the trajectory planning task of the picking robotic arm,successfully solving the problem.
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