A robot assembly framework with “perception-action” mapping cognitive learning

The assembly process is a motion constrained by geometry and environment. The whole assembly process can be described as a series of transitions between contact states. There are many uncertain factors in the actual robot assembly environment, such as parts, robot motion and sensor information. The method with contact state recognition is widely used for assembly. At present, most work is independent for state recognition and action execution. On the one hand, the method of analysis and statistics is used to improve the recognition rate of state without the execution of assembly action. On the other hand, a variety of optimization methods are used to improve the control strategy. In this paper, a cognitive learning framework of “perception-action” mapping learning is proposed, which integrates contact state recognition and assembly action. The cognitive learning model of knowledge description of perception action mapping is constructed. The robot perceives and recognizes the contact state online, and updates the “state-action” experience knowledge base in time. The validity of the algorithm is verified by the example of low-voltage electrical appliance plastic shell assembly. The results show that the cognitive learning method based on “perception-action” mapping can sense the contact state of assembly online, which could accumulate and update experience knowledge base in time.