Design optimization of minimally invasive surgical robot

Abstract

In minimally invasive robotic surgery (MIRS), a surgeon teleoperates a robotic arm from a master console. This arm operates inside the patient's body through a small orifice which constrains the end-effector's translation along two axes. The workspace of such a robotic arm depends on its design as well as orifice location. Conventionally, the design of such an arm is optimized for large workspace and high dexterity. However, this large workspace might be reachable through only a few orifices, thus making the workspace volume and operation quite sensitive to the orifice location. To overcome this problem, we optimized the design of a 3 degrees of freedom serial robotic arm to attain multiple adjacent (desired number of) possible orifice locations, through which a planar workspace of pre-specified geometry can be traced. To achieve this goal, an algorithm was developed to relate the design of such an MIRS arm to the possible orifice positions. The optimization problem was solved using several metaheuristics such as simulated annealing, Tabu search, artificial bee colonization and genetic algorithm, and their performance was compared.