Year: 2024
Type: preprint
Abstract: This paper provides a comprehensive review of autonomous navigation systems and collision avoidance strategies for mobile robots. It classifies and evaluates various algorithms and methodologies, focusing on sensor fusion, path planning, and decision-making technologies. The work aims to bridge the gap between theoretical advancements and practical applications by reviewing current techniques and identifying areas for future research in autonomous navigation. The study further explores the interconnection of diverse technological approaches, such as graph-based path planning, reactive obstacle avoidance, and deep learning integration for enhanced decision-making.
Source: arXiv (Cornell University)
Authors: Yoshioka, Leopoldo Rideki, Leal de Carvalho, Marcus Vinícius
Institution: Universidade de São Paulo
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G29316538
OpenAlex ID: W4403363904