Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

<table class="table-group" id="tab8"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td class="align_left"> </td><td class="align_center">Mean (mm)</td><td class="align_center">Standard deviation (mm)</td><td class="align_center">Maximum (mm)</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">Before BSO-MLPNN calibration</td><td class="align_center">0.9262</td><td class="align_center">0.1632</td><td class="align_center">1.7435</td></tr><tr><td class="align_left">After BSO-MLPNN calibration</td><td class="align_center">0.5907</td><td class="align_center">0.0760</td><td class="align_center">0.8834</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Absolute positioning accuracy of the SIASUN SR210D manipulator of measurement points.</div>

Complexity

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Table 8

Table 8: Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors 

Table 8 | Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors 