Development of Intelligent Control Systems for Assistive Robots

This chapter presents a method for controlling the trajectory of a redundant surgical robot in an in-air environment using a master–follower (M-F) system. Precise positioning of the robot’s tip is critical for medical procedures such as inspecting internal organs, performing radiosurgery on gastrointestinal tumors, and administering brachytherapy. The experimental setup involves a redundant follower robot with five degrees of freedom (DOF), controlled using techniques based on neural networks, manipulator kinematics, and dynamics. Numerical experiments show that the radial basis function network (RBFN)-based intelligent control algorithm outperforms the backpropagation network (BPN), closed-loop inverse kinematics (CLIK) with redundancy maximization and mechanical joint limit violation (MJLV), as well as manipulator dynamics-based inverse dynamics control and adaptive control. The RBFN-based method achieves a position error norm of 0.9 mm, highlighting its superior performance in tracking the accuracy, speed, and reachability. This approach offers significant potential to enhance trajectory tracking in robotic Natural Orifice Translumenal Endoscopic Surgery (NOTES) and other medical applications.