This paper presents an enhanced haptic-enabled master-slave teleoperation system which can be used to provide force feedback to surgeons in minimally invasive surgery (MIS). One of the research goals was to develop a combined-control architecture framework that included both direct force reflection (DFR) and position-error-based (PEB) control strategies. To achieve this goal, it was essential to measure accurately the direct contact forces between deformable bodies and a robotic tool tip. To measure the forces at a surgical tool tip and enhance the performance of the teleoperation system, an optical force sensor was designed, prototyped, and added to a robot manipulator. The enhanced teleoperation architecture was formulated by developing mathematical models for the optical force sensor, the extended slave robot manipulator, and the combined-control strategy. Human factor studies were also conducted to (a) examine experimentally the performance of the enhanced teleoperation system with the optical force sensor, and (b) study human haptic perception during the identification of remote object deformability. The first experiment was carried out to discriminate deformability of objects when human subjects were in direct contact with deformable objects by means of a laparoscopic tool. The control parameters were then tuned based on the results of this experiment using a gain-scheduling method. The second experiment was conducted to study the effectiveness of the force feedback provided through the enhanced teleoperation system. The results show that the force feedback increased the ability of subjects to correctly identify materials of different deformable types. In addition, the virtual force feedback provided by the teleoperation system comes close to the real force feedback experienced in direct MIS. The experimental results provide design guidelines for choosing and validating the control architecture and the optical force sensor.

Clustering is a process for partitioning datasets. This technique is very useful for optimum solution. k-means is one of the simplest and the most famous methods that is based on square error criterion. This algorithm depends on initial states and converges to local optima. Some recent researches show that k-means algorithm has been successfully applied to combinatorial optimization problems for clustering. In this paper, we purpose a novel algorithm that is based on combining two algorithms of clustering; k-means and Modify Imperialist Competitive Algorithm. It is named hybrid K-MICA. In addition, we use a method called modified expectation maximization (EM) to determine number of clusters. The experimented results show that the new method carries out better results than the ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and k-means.