Optimization of industrial manipulators cycle time based on genetic algorithms

High productivity requires that industrial robots should perform complex tasks in the minimum possible cycle time. The problem of determining the optimum route of a manipulator's end effector visiting a number of task points is an extension of the well-known travelling salesman problem (TSP). Adapting TSP to industrial manipulators task scheduling, the measure to be optimized is the time instead of the distance and the travel time between any two points is affected by the choice of the manipulator's configuration. Therefore, the multiple solutions of the inverse kinematics problem should be taken into account. A new method is introduced to solve the scheduling problem for a point-to-point manipulator motion and it can be applied to any non-redundant manipulator. This method is based on genetic algorithms and an innovative encoding is introduced to take into account the multiple solutions of the inverse kinematics. The results show that the method can determine the optimum sequence of a considerable number of task points for a 6-DOF manipulator.