Research On Master-slave Control Of A Spinal Surgical Robot With Force Feedback

In clinical,traditional spinal surgery is open,which causes great pain to the patient and takes a long time to recover.In order to reduce patient's pain and shorten recovery time,minimally invasive spinal surgery based on radiographic images emerged.While minimally invasive surgery brings good news to patients,it also puts forward higher requirements for doctors,which increases the risk of surgery.Spinal surgery assisted by robots can give full play to the advantages of high precision,stability,reliability and non-fatigue of robots,which is the future development direction.Robots are developing towards the direction of intelligence.However,robots are not capable of completing complex tasks independently nowadays.As another development direction,man-machine cooperation plays an important role in giving full play to the advantages of robots and improving their efficiency.Master-slave robots with force feedback function have good man-machine interaction performance.Combined with sensing technology,it can also realize the function of early warning and automatic stop.If these advantages are applied in clinical practice,it can greatly reduce the burden of doctors and reduce the risk of surgery.The precision of mechanical system is the basis and precondition for realizing master-slave motion following and master-hand feedback.In order to improve the precision,the position errors of master manipulator and slave manipulator are analyzed,in view of the spine surgery robot developed by the research group.Then the error model is established and verified.Using particle swarm optimization(PSO),the parameters in the error model are identified,and the validity of the error identification method is verified by simulation.In order to achieve master-slave following,an incremental master-slave control method based on Jacobian mapping is adopted.The master-slave mapping mechanism is established and the Jacobian matrix of the master manipulator and the slave robot is derived.As for the following error caused by Jacobian mapping,feedforward-neural-network compensation is established.In order to eliminate accumulative error,position-feedback compensation strategy is adopted.When the additional force of the master manipulator is added to the feedback force,it will cause distortion of operator's force feeling.In order to compensate the additional force of the master manipulator,the dynamic model of master manipulator is established,simplified and verified.Then real-time compensation for additional force is realized through dynamic solving.After that,force feedback is realized.Aiming at the master-slave following error before surgery,the error is translated into force to caution the operator,preventing the error from widening further.The impedance model including feedback force and master-slave following error is established.And the impedance parameters are adjusted by fuzzy reasoning.Finally,the experimental platform is established.And the algorithms including kinematics calibration,master-slave motion control and force feedback mentioned in the paper are verified by experiment.By comparing the robot precision before and after calibration,the validity of kinematics calibration method is verified.The validity of the error compensation algorithm is verified by comparing the following error before and after the addition of compensation.In order to verify the effectiveness of the force feedback control algorithm,the comparison of contact force and feedback force in the process of grinding bovine spine is carried out.