Meet AUTOASSESS
Open Calls Winners
Open Call #1
AUTOASSESS first Open Call invited Startups and SMEs to present their innovative technology solutions to the use-case challenges identified by the AUTOASSESS technical team together with the use-case owners. Five proposals were selected to join AUTOASSESS 6-month programme.
Click on the projects below to learn more about these innovative solutions and the teams behind them.
Call #1 Winners
HYPER-FI
Challenge being tackled:
Reliable high-bandwidth communication for off-board
control of drones
Solution Summary:
Autonomous drone communication system ensuring 1 Gbps bandwidth and <33 ms latency in GNSS-denied metallic environments for reliable offboard control. Designed for seamless integration with navigation systems and robust operation in high-interference conditions by using 802.11be WiFi solution.
Challenge proposer:
Solution Provider:
AutoPilot NDT
Challenge being tackled:
Human Operator interface for FA-IUAS
Solution Summary:
Safe and fast UAS inspections with an autonomy stack and intuitive UI, reducing training time, boosting inspection quality, and cutting costs. Built on ROS 2 and open protocols, the solution is modular, adaptable, and ready for broader industrial use.
Challenge proposer:
Solution Provider:
SPECTRUM
Challenge being tackled:
Stereo event-camera on miniature drone
Solution Summary:
A compact aerial platform equipped with stereo even-based vision and onboard real-time processing, capable of perceiving and avoiding obstacles to autonomously navigate complex, high-risk maritime environments, enabling rapid and remote inspection.
Challenge proposer:
Solution Provider:
SENTISYNC
Challenge being tackled:
Synchronization of multi-modal sensor data
Solution Summary:
Precise temporal (hardware) synchronization of multi-modal sensor data. The SentiBoard aligns diverse data streams to a common timebase in real time, enabling accurate sensor fusion and enhanced situational awareness, crucial for AUTOASSESS vessel inspections.
Challenge proposer:
Solution Provider:
MAGFLY
Challenge being tackled:
Crack detection with multi-element ultrasound transducer
Solution Summary:
Safe and fast UAS inspections with an autonomy stack and intuitive UI, reducing training time, boosting inspection quality, and cutting costs. Built on ROS 2 and open protocols, the solution is modular, adaptable, and ready for broader industrial use.
Challenge proposer:
Solution Provider:
Open Call #2
The second Open Call for Tech Innovations invited startups and SMEs to submit solutions tailored to maritime challenges identified by the project team. Out of the applicants, nine winning projects were selected to join an 11-month support programme featuring expert mentorship, access to testing infrastructure, and up to €150k in grant funding.
Click on the projects below to learn more about these innovative solutions and the teams behind them.
Call #2 Winners
NAUTIS
Challenge being tackled:
Vessel Structural Condition Analysis and Hotspot Identification via Digital Representation and Modelling for Robotic Inspection
Solution Summary:
NAUTIS creates a Digital Twin by combining archival vessel data with historical thickness measurements. Its Explainable AI engine applies digitized IACS rules to pinpoint structural hotspots, converting them into 3D waypoints for targeted, class-approved robotic inspections in GNSS-denied environments.
Challenge proposer:
Solution Provider:
A.I.S.A
Challenge being tackled:
Combined Qualitative and Quantitative Assessment of Structural Integrity Using Imaging and Measurements
Solution Summary:
The A.I.S.A. project addresses the gap between raw robotic data collection and actionable engineering intelligence by developing a software platform that acts as an intelligent “brain”. It combines qualitative (visual) and quantitative (UTM) datasets through a Spatio-Temporal Data Fusion Engine, while filtering the AI findings through a deterministic Expert System based on maritime rules, like CSR. By adding a 5-year historical data dimension, it predicts degradation rates to automate Class-ready condition evaluation reports and repair specifications.
Challenge proposer:
Solution Provider:
XR-INSPECT
Challenge being tackled:
Remote Inspection Support and Augmented Collaboration between Onboard and Shore-Based Experts
Solution Summary:
XR-INSPECT is an Agile “Ship-to-Shore” co-creation framework that deploys a hardware-agnostic Augmented Reality (AR) interface. It empowers onboard crew members to safely execute complex, targeted inspections under the real-time, spatial guidance of remote senior surveyors. By creating a persistent digital Extended Reality (XR) layer that connects robotic platforms with human operators, the system fosters a continuous, data-driven decision-making loop that transitions maritime maintenance into a proactive ecosystem while reducing human exposure to hazardous environments.
Challenge proposer:
Solution Provider:
ENGINE-GUARD
Challenge being tackled:
Autonomous Patrolling Concepts and Early Event Detection Frameworks for Engine Rooms
Solution Summary:
ENGINE-GUARD resolves the diagnostic limitations in maritime safety by mobilizing an autonomous rail-guided robotic platform. Equipped with a stereo event-camera that provides immunity to external motion blur and a specialized multi-sensory diagnostic payload (thermal, acoustic, gas), the platform executes autonomous patrols. The data is processed through an Adaptable Probabilistic Framework, effectively digitizing veteran engineering intuition to deliver high-confidence predictive maintenance alerts and Event-Triggered Rerouting commands, reducing human exposure to engine room hazards.
Challenge proposer:
Solution Provider:
SOAR-NDT
Challenge being tackled:
SOT-aided NDT Thickness Tool for a Fully-Actuated Aerial Robot
Solution Summary:
SOAR-NDT is an autonomous aerial system that brings human-grade touch to non-destructive testing in places people can’t safely go. A soft optical tactile sensor, mounted on a fully-actuated drone alongside a pulsed eddy current probe, lets the robot feel its way onto a surface, hold stable contact under real-world disturbances, and take accurate thickness readings. By combining deep-learning-based contact perception with energy-efficient force control, SOAR-NDT turns aerial inspection from a remote visual check into a genuine hands-on measurement, cutting inspection time, cost, and risk for industries that depend on structural integrity.
Challenge proposer:
Solution Provider:
PRISMA
Challenge being tackled:
Open Challenge
Solution Summary:
PRISMA adds a predictive intelligence layer to the AUTOASSESS autonomous inspection system. Autonomous drones fitted with NDT sensors scan the vast, GNSS-denied interiors of ballast tanks and cargo holds, but without prior knowledge of where structural degradation is most likely they must scan indiscriminately and exhaust scarce battery endurance on sound surfaces. PRISMA introduces Targeted Autonomous Navigation: a predictive Digital Twin driven by a Heterogeneous Graph Transformer trained on 10 to 15 years of vessel-specific Ultrasonic Thickness Measurements, visual survey logs and operational profiles. From this history it generates dynamic 3D Risk Heatmaps that ingest into the AUTOASSESS Digital Twin data environment (Cognite Data Fusion) and direct UAS mission planners to the statistically highest-risk zones before launch. By telling the robot where to look, PRISMA is the enabling component that makes it possible to compress a traditional 15-day dry-dock inspection to as little as 3 days.
Challenge proposer:
Solution Provider:
HELM
Challenge being tackled:
Graphical User Interface for UAS
Solution Summary:
HELM will develop a framework for executing and monitoring AUTOASSESS UAS vessel-inspection missions during the live operational phase. It will connect AUTOASSESS cloud infrastructure, UAS platforms, and human operators through two main components: a Mission Execution Engine that interprets mission plans, translates them into platform commands, manages mission state, telemetry, fail-safes, logs, outputs, and variable connectivity; and a Graphical User Interface that provides live monitoring, situational awareness, alerts, mission data, and human-in-the-loop controls such as start, pause, resume, abort, return-home, and task repetition. Co-designed with AUTOASSESS partners and operators, HELM will define component interfaces and data exchanges, support mission execution throughout its lifecycle, and be validated in operational pilot conditions. The project aims to improve coordination, consistency, safety, traceability, and operator oversight in complex UAS vessel inspections.
Challenge proposer:
Solution Provider:
DI-MAPS
Challenge being tackled:
Open Challenge
Solution Summary:
DI-MAPS is a PC application that automates the conversion of 2D vessel drawings into machine-readable structural priors for autonomous drone (UAS) inspection of confined maritime spaces such as ballast water tanks. The solution is built as a four-stage pipeline: an Ingestion Layer that uses libraries to strip non-structural elements from vessel drawings and normalize geometry into a unified ship-reference coordinate system in SI meters; an Orthographic Fusion Engine that reconstructs a 3D volumetric model from 2D plan and section/profile projections using a space-carving algorithm, marking voxels as occupied only where structural lines align across corresponding views; a Semantic Annotation Layer that applies a YOLO model trained on engineering symbols to detect points of interest, calculate their 3D center-points, and assign them as spatial anchors to counter drone sensor drift; and an Export Layer that serializes the resulting model into standardized, drone-ready format. The output is a reviewed structural prior that supports exploration, expected-structure comparison, semantic anchoring, and relocalization for downstream UAS navigation and mapping systems.
Challenge proposer:
Solution Provider:
APEX-VISION
Challenge being tackled:
Open Challenge
Solution Summary:
APEX-VISION introduces a hardware-agnostic, edge-based perception engine for real-time, defect-aware robotic inspection. The system integrates Intelligent Frame Prioritization (IFP) and an Onboard Defect Classification Engine (ODCE) into a compact payload. This module processes visual data directly onboard, identifying and classifying corrosion types in real time, while filtering out low-quality or irrelevant frames. By generating immediate feedback signals (e.g., MAVLink-compatible), the system enables adaptive inspection behavior, guiding robotic platforms toward areas of higher structural relevance and significantly reducing data redundancy and post-processing efforts.
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