Feature article: Reengineering the avionics of an unmanned aerial vehicle

Fernández, José María Pérez; López, Juan Sáez; Gómez, J P Fernando

doi:10.1109/maes.2016.150048

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…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.…”

Section: Metacontrol Of An Underwater Robotmentioning

confidence: 99%

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

Corbato

Milosevic

Olivares

et al. 2019

Advances in Intelligent Systems and Computing

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Autonomous underwater robots, such as the UX-1 developed in the UNEXMIN project, need to maintain reliable autonomous operation in hazardous and unknown environments. Because of the lack of any kind of real-time communications with a human operated command and control station, the control architecture needs to be enhanced with mission-level self-diagnosis and self-adaptation properties an additional provided by some kind of supervisory or "metacontrol" component to ensure its reliability. In this paper, we propose an ontological implementation of such component based on Web Ontology Language (OWL) and the Semantic Web Rule Language (SWRL). The solution is based on an ontology of the functional architecture of autonomous robots, which allows inferring the effects of the performance of its constituents components in the functions required during the robot mission, and generate the reconfigurations needed to maintain operation reliably. The concept solution has been validated using a hypothetical set of scenarios implemented in an OWL ontology and an OWLAPI-based reasoner, which we aim at validating by integrating the metacontrol reasoning with a realistic simulation of the underwater robot.

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Section: Metacontrol Of An Underwater Robotmentioning

confidence: 99%

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

Corbato

Milosevic

Olivares

et al. 2019

Advances in Intelligent Systems and Computing

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“…Following the OM Engineering Process, the robot control architecture was designed and modeled using Table 1 Average time and standard deviation for the robot to navigate the route and reconfigure its control architecture over 6 trials for each of the following scenarios: i) using Architecture 1, ii) using Architecture 2, iii) when the metacontrol adapts the architecture from 1 to 2 to recover a permanent laser failure the ISE&PPOOA methodology [16] for Model-Based Systems Engineering. Figure 6 shows a simplified version of the base capabilities in the mobile robot.…”

Section: Mobile Robot Control Developmentmentioning

confidence: 99%

A self-adaptation framework based on functional knowledge for augmented autonomy in robots

Hernández

Bermejo–Alonso

Sanz

2018

ICA

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Robot control software endows robots with advanced capabilities for autonomous operation, such as navigation, object recognition or manipulation, in unstructured and dynamic environments. However, there is a steady need for more robust operation, where robots should perform complex tasks by reliably exploiting these novel capabilities. Mission-level resilience is required in the presence of component faults through failure recovery. To address this challenge, a novel self-adaptation framework based on functional knowledge for augmented autonomy is presented. A metacontroller is integrated on top of the robot control system, and it uses an explicit run-time model of the robot's controller and its mission to adapt to operational changes. The model is grounded on a functional ontology that relates the robot's mission with the robot's architecture, and it is generated during the robot's development from its engineering models. Advantages are discussed from both theoretical and practical viewpoints. An application example in a real autonomous mobile robot is provided. In this example, the generic metacontroller uses the robot's functional model to adapt the control architecture to recover from a sensor failure.

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“…The ISE&PPOOA method can be used as a re-engineering process for software intensive autonomous systems [5]. Traditional functional based systems engineering is combined with MBSE using the functional paradigm to represent the system behavior.…”

Section: The Iseandppooa Mbse Processmentioning

confidence: 99%

Model-based systems engineering to design collaborative robotics applications

Hernández

Sanchez

2017

2017 IEEE International Systems Engineering Symposium (ISSE)

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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.

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Feature article: Reengineering the avionics of an unmanned aerial vehicle

Cited by 4 publications

References 6 publications

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

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

A self-adaptation framework based on functional knowledge for augmented autonomy in robots

Model-based systems engineering to design collaborative robotics applications

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