IEEE Trans Haptics. 2023 Apr-Jun;16(2):118-133. doi: 10.1109/TOH.2023.3253856. Epub 2023 Jun 20.
Shared control, which permits a human operator and an autonomous controller to share the control of a telerobotic system, can reduce the operator's workload and/or improve performances during the execution of tasks. Due to the great benefits of combining the human intelligence with the higher power/precision abilities of robots, the shared control architecture occupies a wide spectrum among telerobotic systems. Although various shared control strategies have been proposed, a systematic overview to tease out the relation among different strategies is still absent. This survey, therefore, aims to provide a big picture for existing shared control strategies. To achieve this, we propose a categorization method and classify the shared control strategies into 3 categories: Semi-Autonomous control (SAC), State-Guidance Shared Control (SGSC), and State-Fusion Shared Control (SFSC), according to the different sharing ways between human operators and autonomous controllers. The typical scenarios in using each category are listed and the advantages/disadvantages and open issues of each category are discussed. Then, based on the overview of the existing strategies, new trends in shared control strategies, including the "autonomy from learning" and the "autonomy-levels adaptation," are summarized and discussed.
共享控制允许人类操作员和自主控制器共享遥操作机器人系统的控制,从而可以减少操作员的工作量和/或在执行任务时提高性能。由于将人类智能与机器人更高的功率/精度能力相结合具有巨大的优势,因此共享控制架构在遥操作机器人系统中占据了广泛的范围。尽管已经提出了各种共享控制策略,但仍然缺乏梳理不同策略之间关系的系统概述。因此,本调查旨在为现有的共享控制策略提供一个整体概述。为了实现这一目标,我们提出了一种分类方法,根据人类操作员和自主控制器之间的不同共享方式,将共享控制策略分为 3 类:半自主控制(SAC)、状态指导共享控制(SGSC)和状态融合共享控制(SFSC)。列出了每种类型的典型应用场景,并讨论了每种类型的优缺点和开放性问题。然后,基于对现有策略的概述,总结和讨论了共享控制策略的新趋势,包括“从学习中获得的自主性”和“自主性级别自适应”。