LEARNING-TASK STRATEGIES IN ROBOTIC ASSEMBLY SYSTEMS

Abstract

This paper presents a practical method for generating task strategies applicable to chamferless and high-precision assembly. The difficulties in devising reliable assembly strategies result from various forms of uncertainty such as imperfect knowledge on the parts being assembled and functional limitations of the assembly devices. In order to cope with these problems, the robot is provided with the capability of learning the corrective motion in response to the force signal through iterative task execution. The strategy is realized by adopting a learning algorithm and is represented in a binary tree-type database. To verify the effectiveness of the proposed algorithm, a series of experiments are carried out under simulated real production conditions. The experimental results show that sensory signal-to-robot action mapping can be acquired effectively and, consequently, the assembly task can be performed successfully.