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A proposed framework for regulatory, ethical, and legal discussions identifies six levels of autonomy for medical robotics.

The reduced access conditions of minimally invasive surgery and therapy (MIST) impair or completely eliminate the feel of tool—tissue interaction forces. Many researchers have been working actively on the development of force sensors and sensing techniques to address this problem. The goal of this survey article is to summarize the state of the art in force sensing techniques for medical interventions in order to identify existing limitations and future directions. A literature search was performed from January to July 2009 using a combination of keywords relevant to the area, including force, sensor, sensing, haptics, and minimally invasive surgery. The literature search resulted in 126 articles with valuable content. This article presents a summary of the force sensing technologies, design specifications for force sensors in clinical applications, force sensors and sensing instruments that have been developed for MIST, and the experiments performed to determine the need for force information. Open areas of research include force sensor design, development of alternative methods of sensing, assessment of the impact of force information on performance, determination of the benefits of haptic information, and evaluation of the human factors involved in the processing and use of force information.

A stable neural network (NN)-based observer for general multivariable nonlinear systems is presented in this paper. Unlike most previous neural network observers, the proposed observer uses a nonlinear-in-parameters neural network (NLPNN). Therefore, it can be applied to systems with higher degrees of nonlinearity without any a priori knowledge about system dynamics. The learning rule for the neural network is a novel approach based on the modified backpropagation (BP) algorithm. An e-modification term is added to guarantee robustness of the observer. No strictly positive real (SPR) or any other strong assumption is imposed on the proposed approach. The stability of the recurrent neural network observer is shown by Lyapunov's direct method. Simulation results for a flexible-joint manipulator are presented to demonstrate the enhanced performance achieved by utilizing the proposed neural network observer.