ICACIA:使用本体论和深度学习模型的国防工业 COBOT 智能情境感知框架"[《机器人与自主系统》第 157 卷,2022 年 11 月,104234] 的更正

IF 4.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Robotics and Autonomous Systems Pub Date : 2024-06-04 DOI:10.1016/j.robot.2024.104726
Arodh Lal Karn , Sudhakar Sengan , Ketan Kotecha , Irina V Pustokhina , Denis A Pustokhin , V Subramaniyaswamy , Dharam Buddhi
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Corrigendum to “ICACIA: An Intelligent Context-Aware framework for COBOT in defense industry using ontological and deep learning models” [Robotics and Autonomous Systems Volume 157, November 2022, 104234]
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来源期刊
Robotics and Autonomous Systems
Robotics and Autonomous Systems 工程技术-机器人学
CiteScore
9.00
自引率
7.00%
发文量
164
审稿时长
4.5 months
期刊介绍: Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.
期刊最新文献
Editorial Board A sensorless approach for cable failure detection and identification in cable-driven parallel robots Learning latent causal factors from the intricate sensor feedback of contact-rich robotic assembly tasks GPS-free autonomous navigation in cluttered tree rows with deep semantic segmentation Robust trajectory tracking for omnidirectional robots by means of anti-peaking linear active disturbance rejection
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