Genetic Algorithm Application in Continuum Robot Optimization

Presently, the Genetic Algorithm method, renowned as one of the prominent branches of AI, has transcended the realm of computer science and gained widespread adoption in various scientific domains, including engineering and medical sciences. This discussion highlights its application specifically in the optimization of a particular type of robot known as the Concentric Tube Continuum Robot (CTR). The CTR is composed of multiple curved tubes and poses unique challenges in terms of its inverse kinematics and control, primarily due to its redundancy. Consequently, the identification of an optimized solution becomes crucial. In this paper, the Genetic Algorithm, a type of AI method, is employed to optimize both the robot’s parameters and its inverse kinematic solution. Initially, the Genetic Algorithm (GA) is utilized to optimize the construction parameters of the robot, with a primary emphasis on accuracy. Accuracy holds paramount importance for robots like the CTR, especially in applications such as medical surgery, where precision is of utmost sensitivity. Furthermore, the length of the robot plays a significant role in ensuring its stability, which is considered during the optimization process. Subsequently, the focus shifts towards optimizing the robot parameters specifically for navigating according to different target points considering two assumptions. At first it is assumed that all tubes have constant curvature for reaching different target points and then, it is considered that the middle tube has variant curvature for different target points. The results showed that the second robot has a better performance.