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Veuillez utiliser cette adresse pour citer ce document : http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/13356

Titre: Indoor obstacle avoidance system design And evaluation using deep learning and SLAM based approaches
Auteur(s): Benbekhma, Abdelwadoud
Taibi, Houssam Eddine
Benzaoui, Messaouda((supervisor)
Mots-clés: SLAM : Simultaneous localization and mapping
RRT : Rapidly exploring random trees
Date de publication: 2024
Editeur: Université M'hamed Bougara Boumerdès: Institue de génie electronic et electric
Résumé: This project aims to contribute to the vibrant fiel do fobstacl edetectio nan dsaf eau-tonomous navigation by designing a robust and cost-efficien tsyste mfo rindoo rmobile robot obstacle avoidance. The system combines 2D LiDAR-based SLAM with the state-of-the-art RRT algorithm for effective path planning. In addition, a pioneering deep learning approach addresses challenges in SLAM-RRT-based obstacle avoidance, including un-certain sensor measurements, complex environments, generalization, planning efficiency, and non-geometric information. The deep learning model is trained using data from a simulated environment with a 2D LiDAR sensor, serving both SLAM and data acquisition purposes. Comparative analysis between odometry-based and SLAM-based pose compu-tation methods provides insights into successful deep learning-based obstacle avoidance. Implemented within ROS2, this project represents a significan tstrid ei nexplorin gcutting-edge techniques for robust and cost-efficien tindoo rmobil erobo tobstacl eavoidance
Description: 75 p.
URI/URL: http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/13356
Collection(s) :Contrôle

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