Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning
参考中译:提升移动机器人技术：人工智能和机器学习的先驱应用


          

刊名：Revue d'Intelligence Artificielle
作者：Haider Sahib Nasrallah（Department of Mechanics and Control Processes, Academy of Engineering, Peoples' Friendship University of Russia (RUDN University)）
Ivan V. Stepanyan（Department of Mechanics and Control Processes, Academy of Engineering, Peoples' Friendship University of Russia (RUDN University)）
Karrar Sahib Nassrullah（Department of Mechanics and Control Processes, Academy of Engineering, Peoples' Friendship University of Russia (RUDN University)）
Neder Jair Mendez Florez（Department of Mechanics and Control Processes, Academy of Engineering, Peoples' Friendship University of Russia (RUDN University)）
Israa M. Abdalameer AL-Khafaji（ERP Systems Department, Institute of Information Technologies, MIREA-Russian Technological University 78）
Abdelrrahmane Mohamed Zidoun（Department of Mechanics and Control Processes, Academy of Engineering, Peoples' Friendship University of Russia (RUDN University)）
Ravi Sekhar（Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU)）
Pritesh Shah（Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU)）
Sushma Parihar（Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU)）
刊号：737F0004
ISSN：0992-499X
出版年：2024
年卷期：2024, vol.38, no.1
页码：351-363
总页数：13
分类号：TP3
关键词：Mobile robot；Artificial intelligence；Machine learning；Robot localization；Artificial neural networks；Convolutional neural networks
参考中译：移动机器人;人工智能;机器学习;机器人定位;人工神经网络;卷积神经网络
语种：eng
文摘：The present study delves into the utilization of subsumption architecture for the modeling of mobile robot behaviors, particularly those that respond adaptively to environmental dynamics and inaccuracies in sensor measurements. Central to this investigation is the deployment of reactive controller networks, wherein each node-representing a distinct state-is governed by sensor-triggered conditions that dictate state transitions. The methodology adopted comprises a thorough literature review, encompassing sources from IEEE Xplore, ScienceDirect, and the ACM Digital Library, which discuss the integration of subsumption architecture in the realm of mobile robot control. Through this review, the effectiveness of subsumption architecture in crafting reactive robotic behaviors is underscored. It has been established that augmented finite state machines (AFSMs), which are integral to the subsumption architecture and possess internal timing mechanisms, are pivotal in managing the temporal aspects of state transitions. Additionally, the technique of layering-merging multiple simple networks to form intricate behavior patterns-emerges as a significant finding, accentuating the architecture's capability to facilitate complex behavioral constructs. The prime contribution of this body of work lies in identifying and elucidating the strategic role of subsumption architecture in enhancing the adaptability and robustness of mobile robots. The insights gleaned from this study not only advance our understanding of robotic control systems but also hold implications for the amplification of industrial efficiency and effectiveness through the application of sophisticated AI and machine learning techniques in mobile robotics.
参考中译：本研究探讨了包含体系结构在移动机器人行为建模中的应用，特别是那些对环境动态和传感器测量误差做出适应性响应的移动机器人行为。这项调查的核心是部署无功控制器网络，其中每个节点-代表不同的状态-由传感器触发的条件控制，这些条件指示状态转换。所采用的方法包括全面的文献综述，包括来自IEEE Xplore、Science Direct和ACM数字图书馆的资料，这些资料讨论了包容体系结构在移动机器人控制领域的集成。通过这篇综述，强调了包容体系结构在制作反应性机器人行为方面的有效性。增广有限状态机(AFSM)是包含体系结构中不可或缺的一部分，具有内部的时序机制，在管理状态转移的时间方面起着关键作用。此外，分层技术--将多个简单网络合并成复杂的行为模式--成为一项重大发现，突显了该体系结构促进复杂行为构建的能力。这项工作的主要贡献在于识别和阐明包容体系结构在增强移动机器人的适应性和健壮性方面的战略作用。从这项研究中获得的见解不仅增进了我们对机器人控制系统的理解，而且对于通过将复杂的人工智能和机器学习技术应用于移动机器人来扩大工业效率和有效性具有重要意义。



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