Jordy Senden

PhD in Robotics | Knowledge Valorisation Officer and Project Manager for the AIMatters project at Eindhoven University of Technology | Owner at Senden Systems Solutions

Engineering Autonomy: How Systems Engineering Can Shape the
Next Generation of Intelligent Robotic Systems

Bio.

Jordy Senden works at Eindhoven University of Technology (TU/e), where he leads the TU/e team in the European AI-Matters project and co-founded the Robotics Technology Center (RTC) together with Wouter Kuijpers. He holds a BSc and MSc in Mechanical Engineering and an EngD (formerly PDEng) in Mechatronic Systems Design, where he first encountered systems engineering, an approach that has shaped his work ever since. He then completed a PhD in robotics at TU/e on world models for robust robotic systems, studying how robots can build and use an understanding of their environment to act reliably in real-world conditions. Within AI-Matters, which establishes Test and Experimentation Facilities to lower the barrier for SMEs to adopt AI and robotics and to make the manufacturing sector more robust, his team focuses on autonomous robots that perform relevant industrial skills. With the RTC, he aims to bridge the valorisation gap between robotics research and industry, accelerating innovation and the uptake of AI and robotics.
He is convinced systems engineering holds the key to making autonomous robots dependable enough for industry.

Abstract.

knowledge representation, task models, reusable skills,
resource models, planning, execution monitoring and
reasoning.

Special attention is given to the role of semantic world
models as the explicit representation of the operational
context of an autonomous system. Such models provide the
foundation for reasoning, decision-making, monitoring and
adaptation, while enabling traceability between system
objectives, operational knowledge and system behaviour. The
workshop further discusses the separation of Task, Skill
and Resource as an architectural principle that promotes
modularity, reuse and interoperability across robotic
platforms.

These concepts are positioned within a broader
Systems-of-Systems perspective, where AI components are
integrated as specialised capabilities within a modular
system architecture rather than acting as the architecture
itself. This architectural approach supports runtime
adaptation while maintaining explicit interfaces,
verifiable behaviour and lifecycle manageability.

The workshop provides an interactive forum to discuss how
classical systems engineering principles—including
abstraction, decomposition, interface definition and
model-based engineering—can be extended to support the
design and engineering of trustworthy autonomous systems.