Chinese Academy of Sciences Engineers Break New Ground in Fusion Reactor Maintenance Robotics
- MM24 News Desk
- 4 days ago
- 3 min read
Researchers at the Hefei Institutes of Physical Science of the Chinese Academy of Sciences have developed a suite of heavy-duty robotic technologies crucial for maintaining future fusion reactors. Their breakthroughs include a novel robotic joint with an ultra-high torque of 139kNm and a deep learning control system that achieves sub-0.1 mm precision, paving the way for robots to perform complex, high-risk tasks in radioactive environments.
The dream of clean, limitless energy from nuclear fusion hinges on overcoming immense engineering challenges, not least of which is how to maintain and assemble the reactor's colossal internal components in an environment too hazardous for humans.
A team from the Chinese Academy of Sciences is turning that dream into a tangible reality with a series of robotic innovations designed specifically for this daunting task. Their work, detailed in publications in IEEE/ASME Transactions on Mechatronics and Engineering Applications of Artificial Intelligence, represents a significant leap forward in heavy-duty robotics and intelligent control.
At the heart of their development is a revolutionary robotic joint designed for brute strength and pinpoint accuracy. By re-engineering the traditional planetary gearbox and removing the sun gear, the team, according to their published research, created extra space for power and control cables while maintaining a compact form.
This new three-stage transmission mechanism achieves a staggering ultra-high reduction ratio of 13,806:1. The result is a joint that can deliver a massive 139kNm of torque while maintaining a remarkably low backlash of just 4.86arcmin. This combination of Herculean strength and fine control is essential for maneuvering the massive in-vessel components of a fusion reactor.
But a powerful arm is useless without a deft touch and keen perception. To solve the critical challenge of precise assembly—such as the classic "peg-in-hole" task—in visually degraded and radioactive environments, the researchers looked to biology for inspiration.
They developed a deep reinforcement learning method that mimics human hand-eye coordination. This system intelligently fuses data from a 2D camera and a force/torque sensor, allowing the robot to "feel" its way to a successful assembly with sub-0.1 mm accuracy.
The breakthrough, reported in their paper, is that it achieves this extraordinary precision without relying on expensive and complex 3D vision systems, making it both robust and practical for real-world reactor conditions.
Environmental perception is another major hurdle. Inside a reactor, clutter and complex geometries can confuse robotic navigation systems. To address this, the team introduced TCIPS (Transformer-based model for 3D point cloud segmentation), an advanced AI model that processes 3D point cloud data.
Unlike conventional methods, TCIPS uses a Transformer-based architecture to capture long-range spatial relationships within the data, allowing it to segment scenes into basic geometric primitives like planes, spheres, and cylinders with superior boundary detection.
This enhanced understanding of the environment is crucial for a robot to navigate and manipulate objects safely and efficiently in the chaotic interior of a fusion device.
Together, these three innovations—the powerful joint, the intelligent control system, and the advanced perception model—form a cohesive technological toolkit.
They mark a critical step toward building the autonomous, heavy-duty robotic systems that will be indispensable for the future of fusion energy. By enabling robots to carry out complex, high-risk maintenance tasks, the work from the Hefei Institutes of Physical Science directly addresses one of the biggest operational barriers to making fusion power a practical reality, bringing the world one step closer to a revolutionary energy source.
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