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Virtual-Fixtures for Robotic-Assisted Bi-manual Cutting using Vector-Field Inequalities

Master—slave robotic systems have enabled operators to perform complex tasks with high precision. Some tasks can be further enhanced by adding virtual fixtures, which can guide user motion or constrain it within a safety envelope. In this study, the focus is on the generation of virtual fixtures for robotic-assisted bi-manual cutting, with possible applications in medicine and industry. Two control methods are proposed: forbidden-plane constraints (for increased safety) and an angle-ellipsoid constraint (for increased performance) which guide the user to safe and efficient bi-manual cutting. The proposed methods were evaluated in a user experiment using a pathology grossing master-slave robotic system. The results show that the forbidden-plane constraints enhanced the safety of the task, while the angle-ellipsoid constraint reduced cutting error by more than 58% (p<0.05).