رسالة جامعية

The RHIZOME architecture : a hybrid neurobehavioral control architecture for autonomous vision-based indoor robot navigation ; L’architecture RHIZOME : une architecture de contrôle neurocomportementale hybride pour la navigation autonome indoor des robots mobiles reposant sur la perception visuelle

التفاصيل البيبلوغرافية
العنوان: The RHIZOME architecture : a hybrid neurobehavioral control architecture for autonomous vision-based indoor robot navigation ; L’architecture RHIZOME : une architecture de contrôle neurocomportementale hybride pour la navigation autonome indoor des robots mobiles reposant sur la perception visuelle
المؤلفون: Rojas Castro, Dalia Marcela
المساهمون: Laboratoire Informatique, Image et Interaction - EA 2118 (L3I), La Rochelle Université (ULR), Université de La Rochelle, Michel Ménard, Arnaud Revel
المصدر: https://theses.hal.science/tel-01753804Test ; Robotics [cs.RO]. Université de La Rochelle, 2017. English. ⟨NNT : 2017LAROS001⟩.
بيانات النشر: HAL CCSD
سنة النشر: 2017
المجموعة: HAL - Université de La Rochelle
مصطلحات موضوعية: Artificial neuronal network-based control architecture, Autonomous mobile robot indoor navigation, Visual perception, Data merging, Floor plan analysis, Pattern recognition, Hybrid behavior-based approach, Architecture de contrôle neuronale robotique, Navigation autonome indoor de robots mobiles, Perception visuelle, Fusion de données, Analyse d’un plan du bâtiment, Reconnaissance de symboles, Approche hybride comportementale, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]
الوصف: The work described in this dissertation is a contribution to the problem of autonomous indoor vision-based mobile robot navigation, which is still a vast ongoing research topic. It addresses it by trying to conciliate all differences found among the state-of-the-art control architecture paradigms and navigation strategies. Hence, the author proposes the RHIZOME architecture (Robotic Hybrid Indoor-Zone Operational ModulE) : a unique robotic control architecture capable of creating a synergy of different approaches by merging them into a neural system. The interactions of the robot with its environment and the multiple neural connections allow the whole system to adapt to navigation conditions. The RHIZOME architecture preserves all the advantages of behavior-based architectures such as rapid responses to unforeseen problems in dynamic environments while combining it with the a priori knowledge of the world used indeliberative architectures. However, this knowledge is used to only corroborate the dynamic visual perception information and embedded knowledge, instead of directly controlling the actions of the robot as most hybrid architectures do. The information is represented by a sequence of artificial navigation signs leading to the final destination that are expected to be found in the navigation path. Such sequence is provided to the robot either by means of a program command or by enabling it to extract itself the sequence from a floor plan. This latter implies the execution of a floor plan analysis process. Consequently, in order to take the right decision during navigation, the robot processes both set of information, compares them in real time and reacts accordingly. When navigation signs are not present in the navigation environment as expected, the RHIZOME architecture builds new reference places from landmark constellations, which are extracted from these places and learns them. Thus, during navigation, the robot can use this new information to achieve its final destination by overcoming unforeseen ...
نوع الوثيقة: doctoral or postdoctoral thesis
اللغة: English
العلاقة: NNT: 2017LAROS001; tel-01753804; https://theses.hal.science/tel-01753804Test; https://theses.hal.science/tel-01753804/documentTest; https://theses.hal.science/tel-01753804/file/2017RojasCastro97699.pdfTest
الإتاحة: https://theses.hal.science/tel-01753804Test
https://theses.hal.science/tel-01753804/documentTest
https://theses.hal.science/tel-01753804/file/2017RojasCastro97699.pdfTest
حقوق: info:eu-repo/semantics/OpenAccess
رقم الانضمام: edsbas.99E54EC7
قاعدة البيانات: BASE