Almusawi A.R.J.Dülger, Lale CananKapucu S.2023-06-162023-06-1620192211-0984https://doi.org/10.1007/978-3-030-20131-9_182https://hdl.handle.net/20.500.14365/3359This study presents a novel controller design for robot-assisted surgery based on Artificial Neural Network (ANN) architecture. The motion of surgical robot is constrained by the kinematics of remote center of motion (RCM). A new ANN design for inverse kinematics of RCM is proposed. ANN compared with classical ANN design. The input pattern of new ANN has included feedback of previous joint angles of robotic arm as well as the position and orientation of the tool tip. A six DOF robotic arm with a tool prototype used to demonstrate a surgical robot. The experimental results proved applicability and efficiency of NN in robotics assisted minimally invasive surgery (RAMIS). © 2019, Springer Nature Switzerland AG.eninfo:eu-repo/semantics/closedAccessArtificial neural network (ANN)Remote center of motion (RCM)robot assisted surgery (RAS)robotic assisted minimally invasive surgery (RAMIS)Inverse kinematicsMachine designNeural networksRobotic armsRoboticsSurgerySurgical equipmentController designsInput patternsJoint angleMinimally invasive surgeryPosition and orientationsRemote center of motionsRobot-assisted surgerySix-DOFRobotic surgeryConference Object10.1007/978-3-030-20131-9_1822-s2.0-85067554906