Mission-based online generation of probabilistic monitoring models for mobile robot navigation using Petri nets |
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| Affiliation: | 1. CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia;2. PSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia;1. Department of Neurology, University Hospital Heidelberg, Heidelberg, Germany;2. German Cancer Research Centre, Department of Radiology, Heidelberg, Germany;3. Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany;4. Institute for Diagnostic and Interventional Neuroradiology, University Hospital Würzburg, Germany;5. CCU Neurooncology, German Cancer Consortium (DKTK) & German Cancer Research Centre (DKFZ), Heidelberg, Germany;1. Cognitive Neuroscience Section, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy;2. Department of Biomedical Sciences and Advanced Therapies, University of Ferrara, Ferrara, Italy;3. University of Verona and National Institute of Neuroscience-Italy;4. Perception and Awareness (PandA) Laboratory, Department of Neurological and Movement Sciences, University of Verona, Italy;1. ERI-CES and Departamento de Análisis Económico, Facultad de Economía, Universidad de Valencia, Avenida de los Naranjos s/n, 46022 Valencia, Spain;2. Grupo Interdisciplinar de Sistemas Complejos (GISC) Departamento de Matemáticas and UC3M-BS Institute of Financial Big Data (IFiBiD), Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain;3. Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain;1. Dep. of Information Technologies and Systems, University of Castilla-La Mancha, Spain;2. Dep. of Information Systems, University of the Bío-Bío, Chile;1. Department of Social and Educational Policy, University of the Peloponnese, Greece;2. Alliance Manchester Business School, People Management and Organisations Division, The University of Manchester, United Kingdom |
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| Abstract: | This paper presents a generic hybrid monitoring approach, which allows the detection of inconsistencies in the navigation of autonomous mobile robots using online-generated models. A mission on the context of the navigation corresponds to an autonomous navigation from a start to an end mission point. The operator defines this mission by selecting a final goal point. Based on this selection the monitoring models for the current mission must be generated online. The originalities of this work are (i) the association of classic state estimation based on a particle filter with a special class of Petri net in order to deliver an estimation of the next robot state (position) as well as the environment state (graph nodes) and to use both pieces of information to distinguish between external noise influences, internal component faults and global behaviour inconsistency (ii) the integration of the geometrical and the logical environment representation into the monitor model (iii) the online generation of the corresponding monitoring model for the present mission trajectory while the system is running. The model takes simultaneously into account the uncertainty of the robot and of the environment through a unified modelling of both. To show the feasibility of the approach we apply it to an intelligent wheelchair (IWC) as a special type of autonomous mobile robot. |
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