A System Architecture for CAD-Based Robotic Assembly With Sensor-Based Skills

Specifying assembly tasks in computer-aided design (CAD) level is a promising approach to intuitively program complex robot skills. In this article, a three-layered system architecture is presented to generate sensor-based robot skills from an assembly task instance. The architecture consists of an application layer where the user instantiates assembly tasks by specifying CAD constraints between geometric primitives pairs. A process layer infers the most suitable robot skills and their appropriate parameters. This inference is made possible by reasoning on a knowledge database represented as an ontology. The ontology contains semantic models of relevant classes such as tasks, skills, and geometric primitives as well as the relations between them. A control layer executes the sensor-based skills in real time using the eTaSL programming framework. A software implementation for the three layers is presented. The application layer is implemented in FreeCAD, whereas the process layer consists of a Web ontology language (OWL) ontology, a Prolog-based reasoner, and fuzzy inference to correctly select the skill and generate its parameters. In the control layer, the instantiated eTaSL skills execute the assembly tasks by sending an optimized control command to the robot. The system is validated on two challenging assembly cases with two distinct robot types, thus demonstrating the system’s capability across different scenarios.
Note to Practitioners
—The widespread use of computer-aided design (CAD) models for describing parts assembly has motivated the research community to create systems that automatically generate robot programs satisfying the assembly goal. While most of the existing literature focuses on generating the assembly sequence, this article deals with the aspect of translation from CAD-level assembly specification to executable robot motion, also called skills. This article systematically addresses the problem by dividing it into different layers and solving them separately. Parameters that influence the successful execution of an assembly task are identified and categorized into application- and process-related parameters. Different inference techniques are employed to address each parameter category. Experimental results show that the proposed system can successfully generate and execute robot skills for assembly scenarios of an air compressor and an electric motor.