RCA562: Control Architecture for Parallel Kinematic Robots

Dietrich, Franz; Maaß, Jochen; Raatz, Annika; Hesselbach, Jürgen

doi:10.1007/978-3-642-16785-0_18

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…The descriptions give constraints similar to Ambler and Popplestone that are used to find remaining degrees-of-freedom. HighLAP uses the task frame formalism to execute force-controlled motion [9]. Tasks are executed by a "net" of skill primitives, i.e.…”

Section: Related Workmentioning

confidence: 99%

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

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Aertbeliën

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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Specifying assembly tasks in CAD-level is a promising approach to intuitively program complex robot skills. In this paper, 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 while the process layer consists of an 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 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 paper deals with the aspect of translation from CAD-level assembly specification to executable robot motion, also called skills. The paper 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-related and processrelated parameters. Different inference techniques are employed to address each parameter category. Experimental results show that the proposed system is able to successfully generate and execute robot skills for assembly scenarios of an air compressor and an electric motor.

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Section: Related Workmentioning

confidence: 99%

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

Pane

Arbo

Aertbeliën

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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“…The descriptions give constraints similar to Ambler and Popplestone that are used to find remaining degrees-of-freedom. HighLap uses the task frame formalism to execute force-controlled motion [5]. Neto et al argued that integration of robot path programming in CAD software would benefit small and medium-sized enterprises where programming costs hinder automation [6].…”

Section: Introductionmentioning

confidence: 99%

A System Architecture for Constraint-Based Robotic Assembly with CAD Information

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Aertbeliën

et al. 2018

2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)

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A system architecture is presented to generate sensor-controlled robot tasks from knowledge encoded in a CAD model. This architecture consists of an application layer where the user annotates assembly tasks in the CAD software. A process layer infers the specific robot skills and parameters from the CAD model and annotated data. A control layer executes the complex, force-controlled tasks. A proof-of-concept implementation is made, consisting of an application layer implemented in FreeCAD and a process layer that focuses on using fuzzy inference to generate appropriate skill-dependent process parameters from the geometric CAD information and annotations in the CAD model. In the control layer, a constraintbased control framework is used to robustly execute the assembly tasks. The system is validated on a challenging assembly task involving the assembly of screw compressor parts.

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