Synthetic Environment for Navigation, Simulation and Operations for Robotics
Sensorama Lab is a Ukrainian deep-tech SME with over seven years of experience delivering enterprise XR training solutions. Our simulators and immersive training products are already deployed across oil and gas, manufacturing, healthcare, logistics, food production and energy, improving safety, procedural compliance and workforce productivity. Building on this simulator and XR training expertise, we are extending from human operator training to physical-AI robotics with SENSOR - the Synthetic Environment for Navigation, Simulation and Operations for Robotics. Currently at TRL 4, SENSOR is a civil, vendor-agnostic platform that combines a digital-twin simulator, virtual training and certification, and fleet management for unmanned aerial systems. It enables repeatable, evidence-grade inspections in complex, and even GPS-denied environments by uniting photorealistic simulation, perception-guided obstacle avoidance, compliance logging and mission analytics. The technology stack integrates SITL/HITL validation on a Gazebo/Unreal backbone with synthetic data generation to train and evaluate perception and sense-and-avoid models before field deployment. Target applications include inspection of critical infrastructure, factories and indoor warehouses, and construction sites, with extensions toward industrial logistics corridors. The project’s objective is to bring SENSOR to TRL 6, demonstrating measurable gains in mission repeatability, safety and data quality, while achieving market readiness through pilots with early adopters and establishing a clear pathway to investment.
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Imported from OpenAIRE Ukraine | Grant 101312937.
Open sourceOverview
Grant agreement 101312937 | SENSOR
Open sourceSensorama Lab is a Ukrainian deep-tech SME with over seven years of experience delivering enterprise XR training solutions. Our simulators and immersive training products are already deployed across oil and gas, manufacturing, healthcare, logistics, food production and energy, improving safety, procedural compliance and workforce productivity. Building on this simulator and XR training expertise, we are extending from human operator training to physical-AI robotics with SENSOR - the Synthetic Environment for Navigation, Simulation and Operations for Robotics. Currently at TRL 4, SENSOR is a civil, vendor-agnostic platform that combines a digital-twin simulator, virtual training and certification, and fleet management for unmanned aerial systems. It enables repeatable, evidence-grade inspections in complex, and even GPS-denied environments by uniting photorealistic simulation, perception-guided obstacle avoidance, compliance logging and mission analytics. The technology stack integrates SITL/HITL validation on a Gazebo/Unreal backbone with synthetic data generation to train and evaluate perception and sense-and-avoid models before field deployment. Target applications include inspection of critical infrastructure, factories and indoor warehouses, and construction sites, with extensions toward industrial logistics corridors. The project’s objective is to bring SENSOR to TRL 6, demonstrating measurable gains in mission repeatability, safety and data quality, while achieving market readiness through pilots with early adopters and establishing a clear pathway to investment.