Adaptive Neuro-Fuzzy Inference System Based Orientation Control of an Intra-operative Ultrasound Robot

2018 4th International Conference on Mechanical Engineering and Automation Science

机械制造;原子能学

Wang, Shuangyi^1 ; Housden, James^1 ; Singh, Davinder^2 ; Rhode, Kawal^1

School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom^1

Xtronics Ltd., Gravesend, Kent, United Kingdom^2

关键词: Adaptive neuro-fuzzy inference system;    Cardiac structure;    Forward kinematics;    Interventional procedures;    Joint parameters;    Orientation control;    Soft computing approaches;    Ultrasound system;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/470/1/012031/pdf
DOI  :  10.1088/1757-899X/470/1/012031

来源: IOP

【 摘 要 】

Trans-esophageal echocardiography (TEE) is a miniatured intra-operative ultrasound system, widely used in routine diagnosis and interventional procedure monitoring, to assess cardiac structures and functions. As a way to assist the operation of TEE remotely, we have developed an add-on robotic system to actuate a commercial TEE probe. For the proposed robot, understanding the inverse kinematics (IK) which relates the probe pose to the joint parameters is the fundamental step towards automatic control of the system. Rather than using conventional numerical-based techniques which may have problems with speed, convergence, and stability when applying to the TEE robot, this paper explores a soft computing approach by constructing an Adaptive Neuro-Fuzzy Inference System (ANFIS) to learn from training data generated by the forward kinematics (FK) and then computing the inverse kinematics in order to control the orientation of the TEE probe. With 1900 training data over 40 epochs, the minimum training error for each joint parameter was found to be less than 0.1 degree. Validation using a separate data set has indicated that the maximum error was less than 0.3 degree for each joint parameter. It is therefore concluded that the ANFIS-based approach is an effective way, with acceptable accuracy, to compute the inverse kinematics of the TEE robot.