Adaptive Control of a Master-Slave Based Robotic Surgical System With Haptic Feedback

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

During traditional surgery, the surgeons' hands are in direct contact with organs, and surgeons rely on the sense of touch to perform surgery. In teleoperated robotic systems, all physical connections between the surgeon and both the robot and patient, are absent. The surgeon must estimate the force exerted on organs, based only on visual deformation of tissues he is pulling, pushing, gripping, or suturing. It is hard to imagine how to operate with no haptic sensations, and it is surprising that commercially available robots didn't include until now any Haptic Feedback, despite reports about tissue injury, and inability to perform complex manipulation. The sense of touch must be created by stimuli sensed by the surgeon. Haptic sensors are required to collect and send haptic information, and display them on the operator's side, creating telepresence, known as transparency. Multiple ways have been developed to improve transparency through force feedback and tactile feedback. However, this interferes with the stability of the closed-loop controlling interactions between master, robot and remote environment. Cutaneous feedback is more stable and less transparent; force feedback is more transparent and less stable. Thus, multimodal platforms of haptic feedback would try to find the best trade-off between both modalities.