Model-based systems engineering to design collaborative robotics applications

Abstract

Abstract-Novel robot technologies are becoming available to automate more complex tasks, more flexibly, and collaborating with humans. Methods and tools are needed in the automation and robotics industry to develop and integrate this new breed of robotic systems. In this paper, the ISE&PPOOA methodology for Model-Based Systems Engineering is applied for the development of robotic systems. The methodology is described through its application to reengineer a state-of-the-art collaborative robot application. The challenges that robotic systems present to model-based systems engineering are discussed, together with the benefits of MBSE methodologies.

Mastering Complexity for Indoor Inspection Drone Development

“…In [31] Hernandez and Fernandez-Sanchez present a method for the development of a collaborative robotic system. It has five main steps: identify operational scenarios, specify system capabilities and high level functional requirements, specify quality attributes and system non functional requirements, create system functional and physical architectures.…”

Meta-control and Self-Awareness for the UX-1 Autonomous Underwater Robot

“…3) is to leverage the engineer knowledge of the system in the form of a runtime model, to drive its runtime selfadaptation capabilities. This knowledge is captured as a model of the functional architecture during system development [7], by using the Integrated Systems Engineering and PPOOA (ISE&PPOOA) method for Model-Based Systems Engineering (MBSE) [6]. Following the functional approach in ISE&PPOOA allows to raise the level of abstraction in the system's representation and focus on representing the architectural properties that are particularly relevant for the system's capabilities required in the mission at hand.…”

“…Concretely, we take into account only four of the eight thrusters (the ones dedicated to the forward and backward movement), and we consider only the subsystem to control the motions of the underwater robot, including two low-level possible controllers (one PID and one fuzzy) tuned for different thruster configurations. We use the ISE&PPOOA [5,7] to develop the hypothetical model of the control architecture for the motion subsystem and its alternative configurations. This is represented in Fig.…”

Model‐Based Systems Engineering Simulation Framework for Robot Grasping

“…The current prospect of robots requires them to be safe, secure, and robust; operate in unstructured, dynamic environments; coexist and interact with humans 2 . Owing to this complexity, the robotics industry has also started placing importance on incorporating MBSE techniques and performing simulations to have better design, development, and deployment 2,3 . The ability to model and simulate every aspect of a robotic system, provides a lot of advantages -enables exploration of design alternatives for different components, expedites the lifecycle, and reduces the overall lifecycle cost 2 .…”