Energy: Nuclear Disaster Cleanup
1. Problem
1a. Statement
The Fukushima Daiichi nuclear disaster left 880 tons of melted fuel debris across three damaged reactors, with cleanup costs exceeding $157 billion and a 30-40 year decommissioning timeline. Human workers cannot enter the highly contaminated reactor buildings, making autonomous robotics essential for debris identification and removal. No computer vision systems existed that could reliably identify objects within nuclear wreckage, a critical capability for robots to distinguish fuel debris from structural materials.
1b. Client Profile
1c. Motivation
2. Analysis
2a. Requirements
The computer vision system needed to process multiple sensor inputs including 3D point clouds, camera feeds, and LIDAR data to identify and classify objects within nuclear debris fields. Using 3D scans of reference objects, the algorithm classified discovered items such as fuel debris, structural materials, pipes, valves, and reactor equipment, outputting object classifications, bounding boxes, and spatial coordinates for integration with the motion planning system. The system required real-time processing capability to support autonomous navigation in unmapped environments, maintaining high accuracy despite challenging conditions including low visibility from dust and particulates, sensor degradation from radiation exposure, inconsistent lighting conditions, and debris fields with no prior mapping data available. Integration with ROS was essential for seamless handoff to the motion planning team handling robot traversal and manipulation tasks.
The computer vision system needed to process multiple sensor inputs including 3D point clouds, camera feeds, and LIDAR data to identify and classify objects within nuclear debris fields. Using 3D scans of reference objects, the algorithm classified discovered items such as fuel debris, structural materials, pipes, valves, and reactor equipment, outputting object classifications, bounding boxes, and spatial coordinates for integration with the motion planning system. The system required real-time processing capability to support autonomous navigation in unmapped environments, maintaining high accuracy despite challenging conditions including low visibility from dust and particulates, sensor degradation from radiation exposure, inconsistent lighting conditions, and debris fields with no prior mapping data available. Integration with ROS was essential for seamless handoff to the motion planning team handling robot traversal and manipulation tasks.
2b. Constraints
3. Solution
3a. Architecture
3b. Implementation
4. Result
4a. DUBEScore™
4b. Outcomes
4c. Learnings
Reference object scanning quality determined matching accuracy. Varied lighting and occlusion improved results 12%.
Multi-sensor fusion required precise temporal alignment. Early synchronization infrastructure prevented rework.
Early integration with motion planning was essential. Weekly handoff tests caught coordinate system mismatches.
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