An enhanced approach for joint configuration of a robot performing in a repetitive task

Abstract

Human-Centric Manufacturing considers the integration of the industrial automation systems with human capabilities. It combines the strength, speed, repeatability and precision of automation with the intelligence, flexibility and skill of human operators. This advanced manufacturing concept strongly emphasis on human-assisted robotic operation. Recently, the interest in the human-robot cooperative industrial robot which can work together with human through physical interaction in the same space has increased. The direct teaching technology for human-robot cooperation enables an operator who is not used to robot programming to control the robot easily by teaching the task trajectory directly. However, because of the industrial robot inverse kinematics characteristics, there are multiple different inverse kinematics solutions in configure space. The planned trajectory based on the track points selected by the operator may be not efficient enough for the task. Hence, optimizing the joint configurations of a robot in a repetitive task becomes one of the increasingly important issues. In this paper, a mathematical model of the robotic joint configurations based on graph theory is developed, which takes into account repeat task constraints. An enhanced approach for the joint configuration of a robot in a repetitive task is proposed. It leads to the reduction of computational complexity and computing time. A numerical experiment and a case study of the optimization of time consumption in an industrial repetitive task are reported to show the effectiveness of the proposed approach.