Few-Shot Reasoning Based Safe Reinforcement Learning Framework for Autonomous Robot Navigation

IF 1.3 Q4 AUTOMATION & CONTROL SYSTEMS

International Journal of Automation and Control Pub Date : 1900-01-01 DOI:10.1504/ijaac.2024.10055043

Mingli Lu, Siwen Chen, Benlian Xu, Xu Zhou, Weiqiang Wang, Jun Yu Li, Yuejiang Gu

引用次数: 0

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基于少镜头推理的自主机器人导航安全强化学习框架

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

International Journal of Automation and Control AUTOMATION & CONTROL SYSTEMS-

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41.70%

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期刊介绍： IJAAC addresses the evolution and realisation of the theory, algorithms, techniques, schemes and tools for any kind of automation and control platforms including macro, micro and nano scale machineries and systems, with emphasis on implications that state-of-the-art technology choices have on both the feasibility and practicability of the intended applications. This perspective acknowledges the complexity of the automation, instrumentation and process control methods and delineates itself as an interface between the theory and practice existing in parallel over diverse spheres. Topics covered include: -Control theory and practice- Identification and modelling- Mechatronics- Application of soft computing- Real-time issues- Distributed control and remote monitoring- System integration- Fault detection and isolation (FDI)- Virtual instrumentation and control- Fieldbus technology and interfaces- Agriculture, environment, health applications- Industry, military, space applications