DOI: 10.3390/machines14080879 ISSN: 2075-1702

Dynamics Estimation for the da Vinci Research Kit: A Review of Model-Based and Learning-Based Approaches

Zhonghao Zhang, Haoying Zhou, Hao Yang, Gregory S. Fischer, Peter Kazanzides

Robot-assisted surgery is now well established in clinical practice and has become a key technology for modern minimally invasive procedures. The da Vinci Surgical System plays an important role in this area and, through the da Vinci Research Kit (dVRK), supports advances in surgical robotics research. However, one longstanding challenge is the accurate modeling of the da Vinci system’s complex dynamics, which is relevant to research in areas such as external force estimation and control. This paper provides a structured narrative review and classification of dynamic modeling methods for the da Vinci Surgical System. We cover model-based and learning-based methods and discuss hybrid approaches as an emerging research direction. We further identify the major technical challenges and promising directions for future research.

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