CogniSafe3D
With the CogniSafe3D project, we are bridging the gap between deterministic industrial safety and the flexibility of AI-driven robotics.
Modern robotic systems increasingly rely on deep learning and adaptive behaviors. However, industrial safety standards require deterministic, certifiable behavior. This creates a fundamental conflict: non-deterministic AI systems must operate within deterministic safety frameworks.
Traditional safety systems often rely on 2D sensing technologies that enforce conservative speed limits or trigger frequent stops—reducing productivity and limiting true collaboration.
CogniSafe3D addresses this challenge by introducing a cognitive 3D safety architecture that combines deterministic safety guarantees with AI-enhanced environmental understanding. The goal is to enable adaptive, certifiable safety without sacrificing efficiency.
CogniSafe3D utilizes external high-resolution 3D LiDAR to monitor shared workspaces and builds a layered safety concept containing a Deterministic Foundation and a Cognitive Layer.
Deterministic Foundation
- Processing of 3D point clouds using Signed Distance Fields (SDF)
- Reliable, safety-rated violation detection
- Deterministic spatial safety boundary enforcement
- Real-time risk monitoring independent of AI components
This foundation ensures compliance with current safety standards and provides certifiable baseline behavior.
Cognitive Layer
- AI-based Human Pose Estimation (HPE)
- Predictive algorithms for human intent and motion forecasting
- Context-aware risk evaluation
- Dynamic adjustment of robot behavior
The cognitive layer enhances the deterministic core by anticipating hazards before they occur.
CogniSafe3D Goals:
- Functional Safety: to design and validate a certifiable safety architecture compliant with ISO 10218:2025 and ISO/TS 15066
- Cybersecurity: design and integrate cybersecurity mechanisms aligned with ISA/IEC 62443 to ensure secure, resilient operation of connected robotic systems in industrial environments
- Scalable integration into ROS 2–based robotic systems
Industrial Impact
- Resolves the conflict between AI-driven flexibility and certifiable safety requirements
- Reduces unnecessary emergency stops through predictive risk assessment
- Increases productivity in shared workspaces
- Shifts from reactive shutdowns to proactive hazard mitigation
Funding Acknowledgement: The CogniSafe3D project (E! 6085) receives funding under the Eureka-Eurostars program from the German Federal Ministry of Research, Technology and Space (BMFTR grant number 01QE2426A) and the Italian Ministero dell’Università e della Ricerca (MUR).
