Fuente: PubMed "swarm" Neural Netw. 2026 Jul 23;205(Pt A):109425. doi: 10.1016/j.neunet.2026.109425. Online ahead of print.ABSTRACTIndustrial robots are a key component of intelligent manufacturing because they improve productivity, precision, and operational reliability. However, long-term operation inevitably introduces wear and other error sources that reduce absolute positioning accuracy and limit precision tasks. To address this issue, this paper develops a two-stage calibrator that combines the advanced social memory optimization algorithm with a neural network optimized by a gradient-based particle swarm optimization scheme, denoted ASMO-GPSONN. In the proposed framework, ASMO identifies robot kinematic errors through memory-guided global exploration, whereas GPSONN compensates the remaining nonlinear residual errors through gradient-corrected swarm refinement. Experiments on two robot calibration datasets, including a real ABB IRB1100 robot, show that the proposed method achieves the best overall calibration accuracy among the compared algorithms. On the held-out test sets, ASMO-GPSONN attains RMSE values of 0.43 mm on D1 and 0.47 mm on D2, demonstrating its practical effectiveness for robot calibration.PMID:42526152 | DOI:10.1016/j.neunet.2026.109425