移动机器人定位:当前挑战与未来展望

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Inam Ullah , Deepak Adhikari , Habib Khan , M. Shahid Anwar , Shabir Ahmad , Xiaoshan Bai
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引用次数: 0

摘要

移动机器人(MR)及其应用正在经历大规模发展,需要多种自主或自导机器人来实现众多目标和责任。将移动机器人(MR)与智能物联网(IIoT)相结合,不仅能使机器人具有创新性、可追踪性和强大的功能,还能在多种应用中产生众多威胁和挑战。IIoT 将人工智能和机器学习等智能技术与物联网(IoT)相结合。磁共振的位置信息(定位)引发了无数领域。为了充分发挥定位的潜力,移动机器人定位(MRL)算法需要与磁共振分类、室内定位绘图解决方案、三维定位等互补技术相结合。因此,本文致力于全面回顾 MRL 的不同方法和技术,强调智能架构、室内和室外方法、概念以及安全相关问题。此外,我们还强调了具有定位信息挑战的各种 MRL 应用,并介绍了各种计算平台。最后,我们重点讨论了导航路径规划、定位、避障、安全、定位问题类别等方面的若干挑战,以及 MRL 技术和应用的潜在未来前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mobile robot localization: Current challenges and future prospective

Mobile Robots (MRs) and their applications are undergoing massive development, requiring a diversity of autonomous or self-directed robots to fulfill numerous objectives and responsibilities. Integrating MRs with the Intelligent Internet of Things (IIoT) not only makes robots innovative, trackable, and powerful but also generates numerous threats and challenges in multiple applications. The IIoT combines intelligent techniques, including artificial intelligence and machine learning, with the Internet of Things (IoT). The location information (localization) of the MRs triggers innumerable domains. To fully accomplish the potential of localization, Mobile Robot Localization (MRL) algorithms need to be integrated with complementary technologies, such as MR classification, indoor localization mapping solutions, three-dimensional localization, etc. Thus, this paper endeavors to comprehensively review different methodologies and technologies for MRL, emphasizing intelligent architecture, indoor and outdoor methodologies, concepts, and security-related issues. Additionally, we highlight the diverse MRL applications where information about localization is challenging and present the various computing platforms. Finally, discussions on several challenges regarding navigation path planning, localization, obstacle avoidance, security, localization problem categories, etc., and potential future perspectives on MRL techniques and applications are highlighted.

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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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