Papers
5
Total Citations
145
H-Index
4
About
Zhirong Wang is a robotics researcher whose work spans industrial robot calibration, positioning accuracy optimization, and legged robot locomotion. His most significant contributions lie in developing advanced calibration methodologies that address both geometric and nongeometric error sources in robotic systems — a challenge of critical importance in precision manufacturing environments. Wang's most influential work, cited 56 times, introduced a novel calibration approach combining joint angle division with artificial neural networks to compensate for uneven error distributions caused by joint and link flexibility. Building on this foundation, his 2020 study (52 citations) proposed a hybrid calibration framework integrating model-based geometric parameter identification using the Product of Exponentials formula with an optimized neural network for nongeometric error compensation, substantially advancing heavy-load robot accuracy. His 2021 contribution further extended this work by incorporating laser tracking measurement to address dynamic parameter errors in industrial settings. Earlier in his career, Wang explored multi-legged robot locomotion, designing quadruped robots with closed-chain mechanism legs and hybrid-driven systems capable of complex movement patterns. With over 140 combined citations, his research has meaningfully shaped approaches to robotic precision, making him a notable contributor to applied robotics and intelligent manufacturing systems.
Research Focus
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