The role of machine learning in robotics

IF 1.9 4区 计算机科学 Q3 ENGINEERING, INDUSTRIAL Industrial Robot-The International Journal of Robotics Research and Application Pub Date : 2022-12-08 DOI:10.1108/ir-11-2022-0279
R. Bogue
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引用次数: 1

Abstract

Purpose This paper aims to illustrate the growing role of machine learning techniques in robotics. Design/methodology/approach Following an introduction which includes a brief historical perspective, this paper provides a short introduction to machine learning techniques. It then provides examples of robotic machine learning applications in agriculture, waste management, warehouse automation and exoskeletons. This is followed by a short consideration of applications in future generations of self-driving vehicles. Finally, brief conclusions are drawn. Findings Machine learning is a branch of artificial intelligence and the topic of extensive academic study. Recent years have seen machine learning techniques being applied successfully to a diversity of robotic systems, most of which involve machine vision. They have imparted these with a range of unique or greatly improved operational capabilities, allowing them to satisfy all manner of new applications. Originality/value This provides a detailed insight into how machine learning is being applied to robotics.
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机器学习在机器人中的作用
本文旨在说明机器学习技术在机器人技术中日益重要的作用。设计/方法论/方法在介绍包括简要的历史观点之后,本文提供了对机器学习技术的简短介绍。然后,它提供了机器人机器学习在农业、废物管理、仓库自动化和外骨骼方面的应用实例。接下来是对未来几代自动驾驶汽车应用的简短考虑。最后,得出了简要的结论。机器学习是人工智能的一个分支,也是广泛学术研究的主题。近年来,机器学习技术被成功地应用于各种机器人系统,其中大多数涉及机器视觉。他们赋予了这些具有一系列独特或大大改进的操作能力,使它们能够满足各种新应用。原创性/价值这为机器学习如何应用于机器人提供了详细的见解。
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来源期刊
CiteScore
4.50
自引率
16.70%
发文量
86
审稿时长
5.7 months
期刊介绍: Industrial Robot publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of robotic technology, and reflecting the most interesting and strategically important research and development activities from around the world. The journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations. Industrial Robot''s coverage includes, but is not restricted to: Automatic assembly Flexible manufacturing Programming optimisation Simulation and offline programming Service robots Autonomous robots Swarm intelligence Humanoid robots Prosthetics and exoskeletons Machine intelligence Military robots Underwater and aerial robots Cooperative robots Flexible grippers and tactile sensing Robot vision Teleoperation Mobile robots Search and rescue robots Robot welding Collision avoidance Robotic machining Surgical robots Call for Papers 2020 AI for Autonomous Unmanned Systems Agricultural Robot Brain-Computer Interfaces for Human-Robot Interaction Cooperative Robots Robots for Environmental Monitoring Rehabilitation Robots Wearable Robotics/Exoskeletons.
期刊最新文献
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