A Small Opening Workspace Control Strategy for Redundant Manipulator Based on RCM Method

Kunlin Guo, Hang Su, Chenguang Yang

Abstract: This article presents a method for redundant manipulators working in the small opening workspace without collision. To achieve this aim, we began with an improved incremental radial basis function (RBF) neural network (RBFNN) method to estimate manipulator dynamic parameters, and then with the help of Lyapunov function, the control strategy could converge within a fixed time. To avoid the collision of workspace and constrain the posture of end-effector, we proposed a safety region convolutional neural network (CNN) method adapted with the Remote Center of Motion method inspired by the minimally invasive surgical manipulator. Torque observer is also implied to estimate the external force to resist external interference. Experiments on Baxter, a seven-degrees of freedom (DoF) redundant manipulator, demonstrate the feasibility of the proposed control strategy.