Evaluation of Particle Swarm Optimization and Adaptive Genetic Algorithm for Motion Planning in Minimally Invasive Surgery

This paper evaluates Adaptive Genetic Algorithm (AGA) and Particle Swarm Optimization (PSO) to find a timeoptimal quadratic polynomial trajectory of an anthropomorphic manipulator. This robot that is used in minimallyinvasive surgery (MIS) must achieve motions under the constraints of displacement, velocity, acceleration and jerk ofeach joint. The modeling and resolution of the constraints are presented. PSO and different selection methods of thegenetic algorithm are evaluated and compared in order to define the best one according to convergence speed andoptimal time. These methods can solve the premature convergence and slow convergence problems in MIS.Simulation and experimental results for the grasper of a compact laparoscopic surgical robot prototype system validatethe algorithms

کلیدواژه ها:

Adaptive Genetic Algorithm ، Particle Swam Optimization ، Minimally Invasive Surgery ، Optimal Trajectory ، Motion Planning