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Shared Control of Robot Manipulators With Obstacle Avoidance: A Deep Reinforcement Learning Approach
IEEE Control Systems ( IF 3.9 ) Pub Date : 1-11-2023 , DOI: 10.1109/mcs.2022.3216653
Matteo Rubagotti 1 , Bianca Sangiovanni 2 , Aigerim Nurbayeva 1 , Gian Paolo Incremona 3 , Antonella Ferrara 2 , Almas Shintemirov 1
Affiliation  

The word teleoperation (which, in general, means “working at a distance”) is typically used in robotics when a human operator commands a remote agent. A teleoperated robot is often employed to substitute human beings in conditions where the latter cannot operate. A possible reason is the need to be in contact with dangerous substances, and indeed, the first robot teleoperation system was designed in the 1940s for handling nuclear and chemical materials [1]. Other reasons can be the difficulty in bringing people on missions to explore deep waters or space [2], [3] as well as the need to work with very high precision (for example) during a surgery [4], [5]. In certain cases, the reference provided by the human operator is not directly passed to the robot but is instead used to generate an adaptive motion. This approach is known as semiautonomous teleoperation or shared control [6], and its aim is to reduce the workload of the human operator during the performance of a difficult task that involves controlling a robotic system.

中文翻译:


具有避障功能的机器人操纵器的共享控制:一种深度强化学习方法



当人类操作员命令远程代理时,远程操作一词(通常意味着“远距离工作”)通常在机器人技术中使用。远程操作机器人经常被用来在人类无法操作的情况下代替人类。一个可能的原因是需要接触危险物质,事实上,第一个机器人远程操作系统是在 20 世纪 40 年代设计的,用于处理核材料和化学材料 [1]。其他原因可能是让人们执行探索深水或太空的任务很困难 [2], [3] 以及在手术期间需要非常高精度的工作 [4], [5]。在某些情况下,人类操作员提供的参考不会直接传递给机器人,而是用于生成自适应运动。这种方法被称为半自主远程操作或共享控制[6],其目的是减少人类操作员在执行涉及控制机器人系统的困难任务期间的工作量。
更新日期:2024-08-26
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