论文标题
有效水下人体机器人相互作用的潜水员注意估计框架
A Diver Attention Estimation Framework for Effective Underwater Human-Robot Interaction
论文作者
论文摘要
许多水下任务，例如电缆和折磨检查和搜索搜索，可以从强大的人类机器人互动（HRI）功能中受益。随着基于视觉的水下HRI方法的最新进展，自动水下车辆（AUV）有能力与他们的人类伴侣互动而无需上层操作员的帮助。但是，在这些方法中，AUV假设潜水员已经准备好进行互动，而实际上，潜水员可能会分心。在本文中，我们试图通过提出一个潜水员注意估计框架来解决此问题，以使AUV自主确定潜水员的注意力，并开发机器人控制器，以允许AUV在启动交互之前对潜水员进行导航和重新定位。该框架的核心要素是一个名为datt-net的深卷积神经网络。它基于金字塔结构，可以利用潜水员的10个面部关键点之间的几何关系来估计其头部方向，我们将其用作专心的指标。我们在闭水机器人试验和开放式机器人试验期间进行的基础实验评估和现实世界实验证实了该框架的功效。
Many underwater tasks, such as cable-and-wreckage inspection and search-and-rescue, can benefit from robust Human-Robot Interaction (HRI) capabilities. With the recent advancements in vision-based underwater HRI methods, Autonomous Underwater Vehicles (AUVs) have the capability to interact with their human partners without requiring assistance from a topside operator. However, in these methods, the AUV assumes that the diver is ready for interaction, while in reality, the diver may be distracted. In this paper, we attempt to address this problem by presenting a diver attention estimation framework for AUVs to autonomously determine the attentiveness of a diver, and developing a robot controller to allow the AUV to navigate and reorient itself with respect to the diver before initiating interaction. The core element of the framework is a deep convolutional neural network called DATT-Net. It is based on a pyramid structure that can exploit the geometric relations among 10 facial keypoints of a diver to estimate their head orientation, which we use as an indicator of attentiveness. Our on-the-bench experimental evaluations and real-world experiments during both closed- and open-water robot trials confirm the efficacy of the proposed framework.
