Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/39922
Title: Towards a Semi-Autonomous Robot Platform for the Characterisation of Radiological Environments
Authors: De Schepper, David
Dekker, Ivo
SIMONS, Mattias 
BRABANTS, Lowie 
SCHROEYERS, Wouter 
Demeester, Eric
Issue Date: 2023
Publisher: IEEE
Source: 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), IEEE, p. 230 -237
Abstract: During the last decades, the (partial) automation of tasks during the dismantling and decommissioning of potentially nuclear contaminated environments has become of emerging interest for e.g. homeland security, disaster response, continuous maintenance, and dismantling and decomissioning activities. Nowadays, the nuclear scene is mostly dominated by manual labour. Radiation protection officers have the task of characterising an environment, which is often unknown a priori, before any dismantling and decomissioning activity can take place. Besides the potential involved health risks, going from radiation disease to an increase in the risk of cancer, this important preliminary task is very time-consuming and prone to errors concerning the taken measurements, storage and post-processing of the recorded data. To minimise the disadvantages, this paper presents the design and development of a proof-of-concept semi-autonomous, ground-based mobile manipulator robot ARCHER (Autonomous Robot platform for CHaractERization) suited for radiological monitoring purposes. Besides the mechanical and electrical overview of the design of the mobile manipulator, this paper describes the software tools used to build and deploy the robot. In addition, this paper describes the results of several in-situ laboratory experiments where the mobile manipulator platform is asked to perform a radiological scanning task on a wall.
Keywords: Index Terms-Robotics for nuclear power plants;Mobile manipulator;Radiological mapping
Document URI: http://hdl.handle.net/1942/39922
ISBN: 9781665456807
DOI: 10.1109/SSRR56537.2022.10018668
ISI #: 000964462200034
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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