A Comprehensive Review on Autonomous Navigation

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2025-03-31 DOI:10.1145/3727642

Saeid Nahavandi, Roohallah Alizadehsani, Darius Nahavandi, Shady Mohamed, Navid Mohajer, Mohammad Rokonuzzaman, Ibrahim Hossain

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引用次数: 0

Abstract

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.

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自主导航技术综述

在过去的几十年里，自主移动机器人领域取得了巨大的进步。尽管取得了重要的里程碑，但仍有一些挑战有待解决。将机器人社区的成就汇总为调查文件对于跟踪当前的最新技术和未来必须解决的挑战至关重要。本文试图对自主移动机器人进行全面的综述，涵盖传感器类型、移动机器人平台、仿真工具、路径规划和跟踪、传感器融合方法、避障和SLAM等主题。提交调查报告的冲动是双重的。首先，自主导航领域发展迅速，因此定期撰写调查论文对于保持研究界对该领域现状的充分了解至关重要。其次，深度学习方法彻底改变了包括自主导航在内的许多领域。因此，有必要对本文所涉及的深度学习在自主导航中的作用进行适当的处理。未来的工作和研究差距也将讨论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.