CERNTAURO: A Modular Architecture for Robotic Inspection and Telemanipulation in Harsh and Semi-Structured Environments
Author(s) -
Mario Di Castro,
Manuel Ferre,
Alessandro Masi
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2849572
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Intelligent robotic systems are becoming essential for industries, nuclear plants, and for harsh environments in general, such as the European Organization for Nuclear Research (CERN) particles accelerator complex and experiments. In order to increase safety and machine availability, robots can perform repetitive, unplanned, and dangerous tasks, which humans either prefer to avoid or are unable to carry out due to hazards, size constraints, or the extreme environments in which they take place. A novel robotic framework for autonomous inspections and supervised teleoperations in harsh environments is presented. The proposed framework covers all aspects of a robotic intervention, from the specification and operator training, the choice of the robot and its material in accordance with possible radiological contamination risks, to the realization of the intervention, including procedures and recovery scenarios. The robotic solution proposed in this paper is able to navigate autonomously, inspecting unknown environments in a safe way. A new real-time control system was implemented in order to guarantee a fast response to environmental changes and adaptation to different type of scenarios the robot may find in a semi-structured and hazardous environment. Components of the presented framework are: a novel bilateral master-slave control, a new robotic platform named CERNbot, and an advanced user-friendly multimodal human-robot interface, also used for the operators' offline training, allowing technicians not expert in robot operation to perform inspection/maintenance tasks. The proposed system has been tested and validated with real robotic interventions in the CERN hazardous particle accelerator complex.
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