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In this paper a medical robot based on a 3-RPS parallel platform is presented for providing correct screw path in transpedicular fixation surgery. For improving the accuracy of the robot, a calibration approach for 3-RPS with CMM (Coordinate Measuring Instrument) data is developed. Sensitivity analysis of the error source had been done with Monte-Carlo techniques. The results show that the length errors of the joint-links are main contribution to the output error of the 3-RPS parallel manipulator; and the secondary factor is the radius error of the static and the moving platform. Based on above analysis, the calibration model of the inverse kinematics was established and the mono-branched calibration method was used. The presented method has high identification accuracy for length error of the chain. Based on the identification parameters, the results show that the method can improve the 3-RPS output accuracy.

Bone milling is one of the most common operations in various kinds of orthopedical surgeries, such as laminectomy surgery. For safety issue and efficacy, it is very important to recognize the states in milling operation. In this paper, an approach to recognize the states of bone milling is proposed, which identify the cortical tissue layer and cancellous tissue layer. Hilbert-Huang Transform (HHT) based on Empirical Mode Decomposition (EMD) is used to analysis and extract the features of the interactive force in milling operation. The instantaneous amplitude of the Intrinsic Mode Functions (IMF) are combined by means of linear weighting method to obtain one comprehensive evaluation index. The feature vector of the index consists of average amplitude, kurtosis, crest factor and average remaining of EMD. With the feature vector, states of cortical and cancellous layer in milling process are recognized based on Support Vector Machine (SVM). Finally, the milling experiment with pig scapula is performed to show the effectiveness of the proposed approach.