Optimal robot task scheduling based on adaptive neuro-fuzzy system and genetic algorithms

Industrial manipulators should be able to execute difficult tasks in the minimum cycle time in order to increase performance in a robotic work cell. This paper is focused on determining the near optimum route of a manipulator's end-effector which is requested to reach a predefined set of demand points in a robotic work cell. Two subproblems are related with this goal: the motion planning problem and the task scheduling problem. A new approach is presented in this paper for simultaneously planning collision-free motion and scheduling time near optimum route along the demand points. A combination of a geometrical approach and an adaptive neuro-fuzzy system is employed to consider the multiple manipulator's configurations, while a special genetic algorithm is designed to solve the derived optimization problem. The experiments show that the proposed method has the capacity to determine both the near optimum manipulator configurations and the near optimum sequence of demand points.

Automated summary

This paper focuses on determining the near optimum route of a manipulator's end-effector to reach a predefined set of demand points in a robotic work cell. A new approach is presented for planning collision-free motion and scheduling time near optimum route along the demand points, utilizing a combination of a geometrical approach and an adaptive neuro-fuzzy system to consider multiple manipulator configurations, and a special genetic algorithm to solve the optimization problem. The experiments demonstrate that the proposed method can determine both the near optimum manipulator configurations and the near optimum sequence of demand points.