Adaptive neuro-fuzzy inference system–based path planning of 5-degrees-of-freedom spatial manipulator for medical applications

Over the last few decades, medical-assisted robots have been considered by many researchers, within the research domain of robotics. In this article, a 5-degrees-of-freedom spatial medical manipulator is analyzed for path planning, based on inverse kinematic solutions. Analytical methods have generally employed for finding the inverse kinematic solutions in earlier studies. However, this method is only appreciable in case of closed-form solutions. The unusual joint configurations of considered manipulator result in more complexity to attain the closed-form solutions, analytically. To overcome with shortcomings of analytical method, a non-traditional approach named adaptive neuro-fuzzy inference system is proposed under the class of artificial intelligent techniques. This article presents this neuro-fuzzy approach for desired path generation by 5-degrees-of-freedom manipulator. The estimation of percentage error between actual path and adaptive neuro-fuzzy inference system–generated path is done with respect to x, y, and z directions, respectively. Furthermore, the error between actual and predicted values regarding joint parameters is calculated for a certain arm matrix. The prototype of 5-degrees-of-freedom medical-assisted manipulator is developed at CSIR-CSIO Laboratory Chandigarh, which is also termed as patient-side manipulator to be utilized in robot-assisted surgery. Through the simulation runs, in this work, it is found that the results from adaptive neuro-fuzzy inference system approach are quite satisfactory and acceptable.

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