Context-Aware System Applied in Industrial Assembly Environment

Abstract

The objective of this paper is to present an ongoing development of a context-aware system used within industrial environments. The core of the system is so-called Cognitive Model for Robot Group Control. This model is based on well-known concepts of Ubiquitous Computing, and is used to control robot behaviours in specially designed industrial environments. By using sensors integrated within the environment, the system is able to track and analyse changes, and update its informational buffer appropriately. Based on freshly collected information, the Model is able to provide a transformation of high-level contextual information to lower-level information that is much more suitable and understandable for technical systems. The Model uses semantically defined knowledge to define domain of interest, and Bayesian Network reasoning to deal with the uncertain events and ambiguity scenarios that characterize our naturally unstructured world.

A context-aware approach in realization of socially intelligent industrial robots

“…While the Model is developed, an environment is simulated locally using a dedicated host PC Server. COMOR is also in a phase of implementation on real robots applied on industrial assembly assignments within a real environment in the Lab [3,[34][35][36].…”

“…The reactive system can be very fragile if something unexpected occurs and usually does not have any self-recovery capabilities to prevent or to correct errors aroused from unexpected situations. On the contrary, the system that is able to partially realize context can potentially do both: it can act reactively and it can comprehend the present and predict future results or actions [3]. It seems that human beings and animals are adapting to their environment in a similar way.…”

Measuring and Representing the Performance of Manufacturing Assembly Robots

“…In [19], Stipancic et al present a context-aware system used within industrial environments. Of interest to this paper is the way that they represent their knowledge about assembly tasks.…”

ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance Theory

“…The use case scenario for implementing ARTgrid consists of a blocks world used for assembly as depicted in Figures 1 and 2. The ARTgrid neural network is developed for the purpose of learning object relationships and object space structures in the previously established robotic framework [15,16]. An object space structure can be defined at two levels of granularity.…”