Quantum sensing is a new R&D line at TR2, currently in the study and feasibility phase, aimed at exploring how quantum-enabled measurement can unlock inspection capabilities beyond conventional vision and spectroscopy. Our research activities focus on high-impact domains such as electronics (ultra-sensitive detection of micro-defects and process variations in advanced assemblies and materials), photonics (precision characterization of optical components, coatings and alignment stability) and space-grade qualification (robust, traceable inspection concepts designed for stringent reliability requirements and challenging, low-signal conditions). By investigating quantum-enhanced detection principles and hybrid architectures that can complement RGB, SWIR and 3D sensing, we are evaluating performance gains, calibration stability and integration pathways needed to translate scientific potential into industrially deployable inspection modules.
TR2 explores quantum-enabled sensing and advanced photonics to push inspection and measurement beyond classical limits. Our work focuses on ultra-low-signal detection, high-stability optical architectures and robust calibration concepts for demanding environments. We investigate hybrid approaches that combine photonic components with data-driven inference to improve sensitivity, repeatability and traceability, targeting applications where conventional vision or spectroscopy struggles (low contrast, complex materials, extreme reliability requirements).
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Generative AI is a strategic R&D line at TR2, focused on building reliable, controllable and industrial-grade generative models that enhance inspection, automation and decision-making across complex production environments. Our research targets high-impact use cases such as synthetic data generation for rare-defect scenarios, self-supervised learning to reduce labeling costs, multimodal foundation models that combine images, spectra and process metadata, and retrieval-augmented assistants that transform technical documentation into actionable operational knowledge. We investigate methods for traceability, safety and evaluation, including constraint-based generation, uncertainty estimation and measurable quality metrics. By integrating generative models with existing RGB, SWIR and 3D pipelines, we aim to accelerate deployment, improve robustness under domain shift and enable scalable configuration of inspection systems with minimal engineering overhead.
TR2 develops Generative AI for industry with an emphasis on controllability, verification and measurable performance. We combine synthetic data, multimodal learning and RAG pipelines to improve defect coverage, reduce time-to-deploy and support operators with trustworthy AI that works in real conditions.
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Cognitive Robotics at TR2 focuses on robots that can perceive, reason and act reliably in dynamic industrial settings. Our research addresses perception-driven manipulation, adaptive motion planning, and closed-loop control informed by vision, 3D sensing and force feedback. We develop architectures where inspection and action are tightly coupled: robots can detect anomalies, localize them in 3D, choose the best intervention and verify outcomes through re-inspection. Key topics include skill learning from demonstrations, task generalization across product variants, real-time safety constraints and human-robot collaboration for assisted operations. The goal is to transform robotic cells from rigid automation into flexible systems that handle variability, reduce setup time and maintain consistent quality across shifts, batches and changing line conditions.
TR2 builds cognitive robotic systems that link sensing to action. We research robust perception, adaptive planning and verification-by-inspection so robots can operate safely, handle variability and deliver consistent outcomes in real production environments.
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TR2’s Blue Economy research focuses on sensing and AI technologies that support maritime operations, coastal infrastructure and marine environmental monitoring. We investigate robust perception in challenging conditions such as low visibility, glare, spray, motion and low-signal scenarios. Our work includes multimodal sensing (optical, SWIR, 3D and domain-specific sensors), anomaly detection for assets and structures, and data-driven models for condition assessment and operational awareness. We explore inspection and monitoring concepts for ports, offshore installations, coastal protection systems and marine equipment, with attention to traceability, reliability and long-term deployment constraints. By developing resilient sensing pipelines and scalable analytics, we aim to enable smarter maintenance, safer operations and better environmental compliance across blue-industry ecosystems.
TR2 develops AI and sensing for maritime and coastal domains, designed for harsh, low-visibility conditions and long-term operation. We focus on multimodal monitoring, anomaly detection and reliable analytics for assets, infrastructure and environmental observables.
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TR2’s research in Livestock and Agrifarming develops sensing, AI and decision-support tools for resilient, data-driven agriculture. We focus on monitoring animals, pastures and farm operations using multimodal approaches that can work in connectivity-limited environments. Key directions include animal tracking and behavior analysis, pasture condition estimation, environmental and welfare indicators, and low-power edge pipelines for continuous data collection. We investigate robust algorithms that handle noise, missing data and seasonal variability, and we design integration pathways with farm workflows and existing hardware (GNSS, IoT gateways, local buffering and store-and-forward architectures). The objective is to deliver actionable intelligence that improves productivity and animal welfare while supporting compliance, traceability and sustainable land management.
TR2 builds agrifarming and livestock sensing systems that work in real field conditions, including low connectivity. We combine edge AI, multimodal data and practical integration to monitor welfare, pasture status and operations with reliable, actionable outputs.
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TR2’s Environment and Communities research targets technologies that help measure, understand and improve environmental quality and societal resilience. We develop sensing and analytics pipelines for monitoring air, water and land indicators, identifying anomalies and supporting evidence-based decisions for public bodies, local stakeholders and infrastructure operators. Our work emphasizes deployability: low-maintenance sensing nodes, robust calibration, data quality assurance and transparent reporting. We study methods for fusing heterogeneous data sources (remote sensing, in situ sensors and contextual datasets) and for producing interpretable outputs that can be used in planning, risk assessment and community engagement. By translating advanced sensing into accessible monitoring concepts, we aim to support sustainability goals, climate adaptation strategies and improved environmental governance.
TR2 develops environmental monitoring and decision-support systems that prioritize data quality, transparency and field deployability. We focus on robust sensing, multimodal fusion and interpretable analytics that communities and institutions can use with confidence.
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