Synthetic Data Platform for Autonomous Defense Robotics

SynDAB provides a synthetic data generation and validation system specifically suited to the development and certification of autonomous ground systems in defense applications. Through simulated mission environments, the system supports AI model training for robotic platforms used in reconnaissance, logistics, surveillance, and explosive ordnance disposal (EOD), while ensuring traceable performance metrics and risk minimization.

• Enables safe and repeatable AI training in combat-like environments
• Supports complex, high-risk scenarios such as urban combat zones or minefields
• Modular interface compatible with military-grade sensor suites
• Reduces training and validation costs while maintaining mission realism
• Enhances AI explainability and operational robustness under uncertainty

• Unmanned ground vehicles (UGVs) for logistics, surveillance, or EOD
• AI-based target recognition and terrain classification
• Military robotics used in urban, desert, or forested zones
• Simulation-based mission rehearsal and system certification

Autonomous and semi-autonomous defense systems, such as unmanned ground vehicles (UGVs), must operate under extreme and unpredictable conditions that are ethically and logistically prohibitive to reproduce in the real world. Scenarios may include complex terrain, reduced visibility, electronic interference, and adversarial actions. Current validation techniques lack the scalability and risk coverage needed for mission-critical deployment. Regulatory and military certification processes demand reproducibility, scenario diversity, and functional traceability,requirements that simulation-based synthetic data generation can uniquely fulfill.

This invention originates from the Institute of Industrial Automation and Software Engineering (IAS) at the University of Stuttgart, led by Prof. Michael Weyrich (Institute Director) and Prof. Christof Ebert. Their research focuses on AI-based testing, verification, and quality assurance of autonomous and safety-critical systems in complex environments.

The work builds upon years of research on simulation-driven validation and software reliability and extends these methods to urban autonomous systems. These include service robots, delivery shuttles, and autonomous vehicles operating in unpredictable and dynamic urban contexts.

Defense robotics and unmanned systems must be validated under extreme and often classified conditions that are difficult, expensive, or unsafe to replicate in the real world. Critical edge cases, such as electronic warfare, terrain loss, or signal degradation, are rarely encountered during standard testing yet essential for operational reliability.
Current testing approaches lack scalability, reproducibility, and scenario diversity, making regulatory and tactical certification challenging. There is a growing demand for intelligent, simulation-based systems that can provide traceable, standards-aligned validation for AI-enabled defense platforms in high-risk environments.

The University of Stuttgart’s patented validation system provides a synthetic simulation environment tailored for defense applications. It enables AI-based generation of test scenarios such as tactical navigation, degraded GPS, sensor spoofing, and unstructured terrain—scenarios that are critical for unmanned ground vehicles (UGVs) but rarely reproducible in training fields.

Using risk-informed AI selection, the system creates a Minimum Viable Test Set (MVTS) aligned with mission profiles and functional safety expectations. Sensor simulations can include thermal imaging, radar, and degraded vision, enabling holistic testing of autonomous defense platforms.

This technology supports integration with defense-grade digital twins and aligns with safety frameworks including DEF STAN 00-056 (UK MOD), MIL-STD-882 (US DoD), and ISO 13849. It facilitates transparent, scalable, and cost-effective testing of defense systems while meeting classified operational constraints and ethical boundaries.

The technology can be transferred to industrial partners via licensing, providing an immediate route for integration into testing frameworks and safety validation platforms. In addition, the inventors’ team at the University of Stuttgart can offer customization through cooperation or consulting projects, leveraging their implemented software modules for specific safety-critical validation use cases, certification readiness, or auditing processes.

Ebert, C. et al. (2023). AI-Based Testing for Autonomous Vehicles. ResearchGate link
IAS (2022). Testing Software Systems. University of Stuttgart. Link
Synthetic data generation for the continuous development and testing of autonomous construction machinery, Alexander Schuster, Raphael Hagmanns, Iman Sonji, Andreas Löcklin, Janko Petereit, Christof Ebert, and Michael Weyrich. Link