Automation of robotic assembly processes on the basis of an architecture of human cognition

Abstract

A novel concept to cognitive automation of robotic assembly processes is introduced. An experimental assembly cell with two robots was designed to verify and validate the concept. The cell's numerical control-termed a cognitive control unit (CCU)-is able to simulate human information processing at a rule-based level of cognitive control. To enable the CCU to work on a large range of assembly tasks expected of a human operator, the cognitive architecture SOAR is used. On the basis of a self-developed set of production rules within the knowledge base, the CCU can plan assembly processes autonomously and react to adhoc changes in assembly sequences effectively. Extensive simulation studies have shown that cognitive automation based on SOAR is especially suitable for random parts supply, which reduces planning effort in logistics. Conversely, a disproportional increase in processing time was observed for deterministic parts supply, especially for assemblies containing large numbers of identical parts.

Cognitively automated assembly processes: a simulation based evaluation of performance

“…However, the number of MTM-1 cycles did not show differences between the deterministic and stochastic part feed. Mayer et al [20] further reported that the desired target state was assembled correctly by the CCU in all 8,400 runs. No assembly errors or deadlocks did occur.…”

“…No explicit knowledge on how to solve the assembly problem is needed. To validate this approach a simulation environment was developed using a simplified model of the experimental assembly cell consisting of a robot with a gripping device, a queue for part feed, a buffer for part storage and a workplace for the assembly of a target product [20].…”

“…Whereas in the previous conducted studies [20] the feeding regime only differentiated between stochastic and deterministic part feed (in the sense of directly mountable part supply), the focus of the study presented in the following lies on the effect of phaseshifted part feed in case of a queue size of only one part. In case the parts needed in an assembly task are not supplied in the right order, this should have an effect on both dependent variables: processing time as well as performed MTM-1 cycles.…”

3D Assembly Group Analysis for Cognitive Automation

“…Even though the sequence of fundamental movements (e.g., reach, grasp, move, position, release) is explainable a posteriori, the sequence of parts positioned after another is not predictable a priori due to a lack of elaboration knowledge [22]. Odenthal et al [28] and Mayer et al [29] identified human assembly strategies that were formulated as production rules. When the reasoning component is enriched with these human heuristics, a significant increase of the robot's predictability when assembling the products can be achieved.…”

Effect of cognitive automation in a material handling system on manufacturing flexibility

“…However, system designers usually are focused on mechanical automaton in manufacturing systems (Parasuraman et al, 2000), and the limited interest in cognitive automation in manufacturing systems has been directed primarily to assembly systems (Fasth et al, 2011;Lindström and Winroth, 2010;Mayer et al, 2011) or manual material handling (Basile et al, 2012a). Thus, the importance of cognitive automation in material handling systems has not been fully recognized.…”