论文标题
移动机器人技术中的人类机器人组合的层次变量自治混合启动框架
A Hierarchical Variable Autonomy Mixed-Initiative Framework for Human-Robot Teaming in Mobile Robotics
论文作者
论文摘要
本文提出了一个混合定位（MI）框架，用于解决远程人类操作员与AI代理之间控制机器人的控制权转移的问题。我们的分层专家指导的混合启动控制切换器（等级）利用了有关人类运营商的状态和意图的信息。控制切换策略基于关键性层次结构。在移动机器人导航的背景下，在高保真模拟的灾难响应和远程检查方案中进行了实验评估，并将等级与最先进的专家指导的混合定位控制转换器（EMICS）进行了比较。结果表明，等级减少了人与AI代理之间控制的冲突，这在MI控制范式以及相关的共享控制范式中都是基本的挑战。此外，我们还提供了统计学上的显着证据，证明了改善，导航安全性（即更少的碰撞），LOA转换效率以及减少控制的冲突。
This paper presents a Mixed-Initiative (MI) framework for addressing the problem of control authority transfer between a remote human operator and an AI agent when cooperatively controlling a mobile robot. Our Hierarchical Expert-guided Mixed-Initiative Control Switcher (HierEMICS) leverages information on the human operator's state and intent. The control switching policies are based on a criticality hierarchy. An experimental evaluation was conducted in a high-fidelity simulated disaster response and remote inspection scenario, comparing HierEMICS with a state-of-the-art Expert-guided Mixed-Initiative Control Switcher (EMICS) in the context of mobile robot navigation. Results suggest that HierEMICS reduces conflicts for control between the human and the AI agent, which is a fundamental challenge in both the MI control paradigm and also in the related shared control paradigm. Additionally, we provide statistically significant evidence of improved, navigational safety (i.e., fewer collisions), LOA switching efficiency, and conflict for control reduction.
