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.

Although robotic grasp planning has been extensively studied in the literature, comparing the performance of different approaches still proves challenging due to the lack of standardization in hardware setup and benchmarking protocols. This work addresses the issue with a threefold contribution. First, it provides a standardized hardware platform and a software framework integrating a benchmarking protocol for grasp planning algorithms (GRASPA). Second, it uses such a framework to benchmark three state-of-the-art algorithms in a reproducible way. Third, it employs the framework to investigate the effect of camera pose variance in visual-based grasp planning. We show how the proposed benchmarking setup can be used to provide insight into the results, not only to compare different vision-based grasp planners but also to evaluate different parameter configurations within the same grasp planner, for instance, camera viewpoint with respect to the scene. To ease the reproducibility of our results and usability of the platform, we provide extensive information for replicating the experimental setup and installing our software in the supplementary material (available at https://doi.org/10.1109/MRA.2023.3256272 ). All the software used in this article is freely available online in the form of Docker images.

In the last few decades, several approaches have been presented to accomplish tasks with robots or autonomous systems in a glovebox; nevertheless, in nuclear facilities, risky operations are still executed by humans that guarantee a high manipulation capability and dexterity. Inside the gloveboxes, robotic devices have to operate in cluttered environments, or environments with limited space for movement; therefore, it is of significant interest to identify grasping poses that are feasible within such constrained environments. In this paper, we present and experimentally evaluate a strategy to synthesise optimal grasps considering geometric primitives for a manipulation systems in a constrained environment. The novel strategy has been experimentally evaluated in a cluttered environment (as a glovebox mock-up) with realistic objects, and the efficacy of the proposed grasping algorithm is proposed.