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First of all, geometric parameters are identified on the basis of the product of exponentials (POE) formula. The errors of the reduction ratio and the coupling ratio are identified at the same time. Then, joint stiffness identification is carried out by adding a load to the end-effector. Finally, residual errors caused by nongeometric parameters are compensated by a multilayer perceptron neural network (MLPNN) based on beetle swarm optimization algorithm. The calibration is implemented on a SIASUN SR210D robot manipulator. Results show that the proposed method possesses better performance in terms of faster convergence and higher precision.<\/jats:p>","DOI":"10.1155\/2020\/5896813","type":"journal-article","created":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T18:30:42Z","timestamp":1579199442000},"page":"1-13","source":"Crossref","is-referenced-by-count":26,"title":["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"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7020-6843","authenticated-orcid":true,"given":"Yuxiang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Zhejiang University, Hangzhou 310000, 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