Robotic manipulator path-planning: Cost-function approximation with fuzzy inference system

Abstract

This paper presents an offline path-planning method for robotic manipulators in static environment. The framework is based on the Transition-based Rapidly-exploring Random Tree (T-RRT) algorithm that requires a cost for each configurations. In this work, the calculation of this cost-function is based on the distance between the position and configurations that cause collisions. This function is evaluated with fuzzy function-approximation which lead to an efficient way to determine the cost all over the configuration space. The method is general, the only restriction is that the segments of the robot and the obstacles are modelled as convex polyhedrons. The approach is validated through simulations in MATLAB Simulink environment with Mitsubishi RV-2F-Q manipulator.