David Doose

About

David Doose is a researcher specializing in the design, verification, and runtime management of software architectures for autonomous and robotic systems. His work sits at the intersection of real-time systems, component-based software engineering, and formal methods, with a particular focus on making robotic systems both dependable and adaptable in dynamic environments. Among his most influential contributions is the development of skill-based architectural frameworks for robotics, where robot capabilities are modeled as discrete, verifiable units called skills. His papers on skill formalization and dependable skill-based design (2020, 2022) have collectively attracted over 40 citations, establishing him as a key voice in this emerging paradigm. His MAUVE framework and associated runtime middleware demonstrate his commitment to bridging theoretical architecture modeling with practical real-time reconfiguration, enabling robots to recover from sensor failures and unforeseen disturbances autonomously. Doose has also made notable contributions to real-time analysis methodology, proposing measurement-based and component-aware techniques to formally guarantee timing constraints in robotic software — a critical safety concern as robots increasingly operate alongside humans. More recently, his use of Petri nets for predictive runtime verification (2022–2023) reflects his growing interest in proactive safety assurance. With nearly 130 total citations, his work offers essential tools for engineers building robust, verifiable autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Skill-based design of dependable robotic architectures

   23 citations · 2022
2. 2
   Formalization of Robot Skills with Descriptive and Operational Models

   20 citations · 2020
3. 3
   A Modeling Framework for Software Architecture Specification and Validation

   19 citations · 2014
4. 4
   A Design and Analysis Methodology for Component-Based Real-Time Architectures of Autonomous Systems

   18 citations · 2018
5. 5
   MAUVE Runtime: A Component-Based Middleware to Reconfigure Software Architectures in Real-Time

   18 citations · 2017
6. 6
   Measurement-based real-time analysis of robotic software architectures

   11 citations · 2016
7. 7
   SkiNet, A Petri Net Generation Tool for the Verification of Skillset-based Autonomous Systems

   8 citations · 2022
8. 8
   Predictive Runtime Verification of Skill-based Robotic Systems using Petri Nets

   7 citations · 2023
9. 9
   Periodic state-machine aware real-time analysis

   3 citations · 2015
10. 10
    Mauve: a Component-based Modeling Framework for Real-time Analysis of Robotic Applications

    3 citations · 2014

Key Collaborators

Contact & Links