Research on Industrial Robot Positioning Error Compensation Based on Improved SSA-DNN
Aiming at the low positioning accuracy of industrial robots and the complexity of traditional error compensation methods,an improved SSA-DNN neural network positioning error compensation model for industrial robots was proposed.Firstly,optimize the spatial grid segmentation sampling planning in Cartesian space to obtain the pattern of target point position and its positioning error.Secondly,a ISSA algorithm based on Tent chaotic mapping is proposed,which combines the Levy flight mechanism to enhance the search ability and accelerate the convergence speed of the SSA algorithm.Finally,an ISSA-DNN positioning error compensation model was established.To verify the effectiveness of the compensation model,a comparative experiment was conducted on a degree of freedom industrial robot with other models to achieve compensation for the actual point position of the robot and improve its positioning accuracy.The results show that in terms of robot positioning error compensation,compared with neural network models such as DNN and SSA-DNN,the ISSA-DNN neural network model has higher compensation accuracy and stability.
NETL
NSTL
万方数据
