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="tab9"><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">0.9370</td><td class="align_center">0.1871</td><td class="align_center">1.7633</td></tr><tr><td class="align_left">After BSO-MLPNN calibration</td><td class="align_center">0.6522</td><td class="align_center">0.0887</td><td class="align_center">0.9834</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 validation points.</div>

Complexity

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Table 9

Table 9: 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 9 | 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 