The nuclear industry has some of the most extreme environments in the world, with radiation levels and extremely harsh conditions restraining human access to many facilities. One method for enabling minimal human exposure to hazards under these conditions is through the use of gloveboxes that are sealed volumes with controlled access for performing handling. While gloveboxes allow operators to perform complex handling tasks, they put operators at considerable risk from breaking the confinement and, historically, serious examples including punctured gloves leading to lifetime doses have occurred. To date, robotic systems have had relatively little impact on the industry, even though it is clear that they offer major opportunities for improving productivity and significantly reducing risks to human health. This work presents the challenges of robotic and AI solutions for nuclear gloveboxes, and introduces a step forward for bringing cutting-edge technology to gloveboxes. The problem statement and challenges are highlighted and then an integrated demonstrator is proposed for robotic handling in nuclear gloveboxes for nuclear material handling. The proposed approach spans from tele-manipulation to shared autonomy, computer vision solutions for robotic manipulation to machine learning solutions for condition monitoring.

In robotic teleoperation, the knowledge of the state of the remote environment in real time is paramount. Advances in the development of highly accurate 3D cameras able to provide high-quality point clouds appear to be a feasible solution for generating live, up-to-date virtual environments. Unfortunately, the exceptional accuracy and high density of these data represent a burden for communications requiring a large bandwidth affecting setups where the local and remote systems are particularly geographically distant. This paper presents a multiple level-of-detail (LoD) compression strategy for 3D data based on tree-like codification structures capable of compressing a single data frame at multiple resolutions using dynamically configured parameters. The level of compression (resolution) of objects is prioritised based on: (i) placement on the scene; and (ii) the type of object. For the former, classical point cloud fitting and segmentation techniques are implemented; for the latter, user-defined prioritisation is considered. The results obtained are compared using a single LoD (whole-scene) compression technique previously proposed by the authors. Results showed a considerable improvement to the transmitted data size and updated frame rate while maintaining low distortion after decompression.

This paper presents a technical overview of AutoInspect, a ROS-based software system for robust and extensible mission-level autonomy. Over the past three years we have deployed AutoInspect on multiple platforms in a variety of environments, from forests to fusion reactors, for durations ranging from hours to weeks. The AutoInspect system combines robust mapping and localisation with graph-based autonomous navigation, mission execution, and scheduling, into a complete autonomous inspection and monitoring system. Code for the graph-based autonomy component will be made available at ori.ox.ac.uk/projects/autoinspect. In this article we describe in detail our most commonly-used configuration of AutoInspect: a Boston Dynamics Spot fitted with a custom compute and sensing payload, called Frontier. To highlight the flexibility of the AutoInspect system, we also describe its deployment with different hardware and software configurations. We evaluate AutoInspect’s performance in two long-term deployments on Spot at the Culham Centre for Fusion Energy in Oxfordshire, UK. The first deployment took place at a robotics test facility, spanning 49 days, including 14 uninterrupted days of autonomous operation. The second deployment covered 35 days in the torus hall of the Joint European Torus (JET) fusion reactor, and achieved 15 days of uninterrupted operation. This was the first ever deployment of a fully autonomous mobile robot in a fusion facility.

Maintenance and inspection systems for future fusion power plants (e.g., STEP and DEMO) are expected to require the integration of hundreds of systems from multiple suppliers, with lifetime expectancies of several decades, where requirements evolve over time and obsolescence management is required. There are significant challenges associated with the integration, deployment, and maintenance of very large-scale robotic systems incorporating devices from multiple suppliers, where each may utilise bespoke, non-standardised control systems and interfaces. Additionally, the unstructured, experimental, or unknown operational conditions frequently result in new or changing system requirements, meaning extension and adaptation are necessary. Whilst existing control frameworks (e.g., ROS, OPC-UA) allow for the robust integration of complex robotic systems, they are not compatible with highly efficient maintenance and extension in the face of changing requirements and obsolescence issues over decades-long periods. We present the CorteX software framework as well as results showing its effectiveness in addressing the above issues, whilst being demonstrated through hardware that is representative of real-world fusion applications.