Thomas Piercy Guido Herrmann Angelo Cangelosi Ioannis Dimitrios Zoulias Erwin Lopez
In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failure. A system designed to generate data-based behavioural estimations using observed operator features could be used to reduce risks in industrial teleoperation. This paper describes a non-invasive …
PublishedYao Ren Robert Skilton
Piping systems that transport coolant and breeding fluid are naturally an essential part of the support system of the nuclear fusion power plants. Following a campaign of operations, the reactor is required to be shut down and maintained entirely. Pipes connected to the reactor components are to be cut, re-welded or re-joined, and inspected non-des…
PreprintAlice Cryer Alfie Sargent Fumiaki Abe Paul Dominick Baniqued Ipek Caliskanelli Hasan Kivrak Hanlin Niu Salvador Pacheco-Gutierrez Alexandros Plianos Masaki Sakamoto Tomoki Sakaue Wataru Sato Shu Shirai Yoshimasa Sugawara Harun Tugal Andika Yudha Robert Skilton
The maturation of Virtual Reality software introduces new avenues of nuclear decommissioning research. Digital Mockups are an emerging technology which provide a virtual representation of the environment, objects or processes, supporting the whole lifecycle of product development and operations. This paper provides a survey on currently available s…
PreprintG. Qin H. Wu C. Li A. Ji S. Budden
The hybrid kinematic mechanism (HKM) as a remote handling subsystem of Demonstration Fusion Power Plant (DEMO) breeding blanket (BB) is undergoing extensive theoretical analysis and feasibility verification. In this paper, the forward and inverse kinematic models of HKM are respectively developed by combining the Newtonian iterative method and the …
PublishedB. Tabia I. Zoulias G.Burroughes
The debris removal effectiveness is evaluated using visual inspection with three types of dry debris: flour, sand, and metallic swarf. These debris particles are chosen for their property mimicking contaminants found in real conditions. Brushing operational parameters such as the brush angle of attack and the brush penetration have been investig…
PurchaseD. Batty L. Manes A. West M. Patel I. Caliskanelli P. Paoletti
In the nuclear industry, the need for improved reliability in current and future technology hinders the deployment of autonomous robotic systems. The following research aims to develop a method of reliably mapping a large environment and abstracting the map into a sparse node graph to create a more efficient data form. The proposed data form all…
PurchaseD. McGarrigle F. Warmer J. Lilburne M. Torrance
This paper reports on the analysis of potential rail-based maintenance systems when implemented into a Helical Advanced Stellarator (HELIAS) 5-B device. The main purpose of such a system would be to handle and exchange the internal vessel components, namely the breeding blanket segments, which are expected to be the largest and heaviest componen…
PublishedWenxing Liu Hanlin Niu Robert Skilton Joaquin Carrasco
When transferring a Deep Reinforcement Learning (DRL) model from simulation to the real world, the performance could be unsatisfactory since the simulation cannot imitate the real world well in many circumstances. This results in a long period of fine-tuning in the real world. This paper proposes a self-supervised vision-based DRL method that al…
PurchaseB. Devlin-Hill R. Calinescu J. Cámara I. Caliskanelli
Many complex domains would benefit from the services of Large-scale, Safety-verified, Always-on (LSA) robotic systems. However, existing large-scale solutions often forego the complex reasoning required for safety verification and prescient reasoning in favour of scalability. We propose a method of partitioning the task domain to enable scalabil…
PurchaseMichal Staniaszek Lara Brudermuller Raunak Bhattacharyya Bruno Lacerda Nick Hawes
We consider planning problems where a robot must visit a large set of locations to complete a task at each one. Our focus is problems where the difficulty of each task, and thus its duration, can be predicted, but not fully known in advance. We propose a general Markov decision process (MDP) model for difficulty-aware problems, and propose varia…
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