Medical robotic arm often encounters multi-source and nonlinear errors during the calibration process, making it difficult for traditional mathematical modeling methods to fully characterize system error features, thereby limiting further improvement in calibration accuracy. In this study, a robotic arm parameter error identification model is established, and a calibration method based on an adaptive long short-term memory (ALSTM) neural network is proposed. The method incorporates a particle swarm optimization (PSO) algorithm to optimize the weights of each layer of the LSTM neural network, enabling more effective fitting of robotic arm kinematic errors and ultimately yielding more accurate Denavit-Hartenberg (D-H) parameters. To validate the proposed approach, 110 sets of experimental data are collected using the HSR-JR680 robotic arm calibration system. Experimental results demonstrate that the ALSTM model reduces the root mean square error (RMSE) by 23.07%-80.39% compared to traditional calibration methods, and shortens the convergence time by 32.44% compared to a standard LSTM model. The optimized D-H parameters obtained meet the high-precision calibration requirements of medical robotic arm, confirming the effectiveness of the proposed method.
Calibration ; Neural Networks, Computer ; Algorithms ; Robotics ; Robotic Surgical Procedures ; Models, Theoretical

Characteristics of two major categories of RA equipment which defined in the standard are interpreted firstly. Few representative RA equipment in current market and their key product features are introduced. Then, classifications of different indexes of spatial positioning accuracy are declared, the difficulties of performing testing process on each indexes are further explained. Meanwhile, different kinds of three dimensional coordinate measuring equipment that are cutting edge at present stage are introduced with their main methods of use explained. According to characteristics of three dimensional coordinate measuring equipment on the market, proper measuring equipment for testing certain index of spatial positioning accuracy and corresponding experiment method are introduced.
Robotics/standards* ; Robotic Surgical Procedures/instrumentation*

Objective: To investigate the effect of free abdominal flap in repairing soft tissue defects of the foot. Methods: From 2011.10 to 2017.11, there were 5 patients whose foot had soft tissue defect, in order to repairing, we had to do debridment at the early time, then made the anterior iliac artery or the inferior epigastric artery as the axial vessel. Results: After the flap transplantation there was no vascular crisis in the 5 patients , followed up for 3 months to 1 years, the blood supply, texture, elasticity and the foot was well in shape and feeling, the patients were satisfied, and the feet were functionally normal. Conclusions The application of free abdominal skin flap covers the soft tissue defect of the foot, the wound surface, the blood vessel the donor site is consistent, the flap is easy to prepare, and the area can be sutured directly, this is an effective treatment method.