Socially Aware Path Planning for a Flying Robot in Close Proximity of Humans
Socially Aware Path Planning for a Flying Robot in Close Proximity of Humans
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DOI：
10.1145/3341570
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发表时间：
2019-09
影响因子：
2.3
通讯作者：
Hyung-Jin Yoon;Christopher Widdowson;Thiago Marinho;R. Wang;N. Hovakimyan
中科院分区：
文献类型：
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作者：
Hyung-Jin Yoon;Christopher Widdowson;Thiago Marinho;R. Wang;N. Hovakimyan
在这篇文章中，我们提出了一个初步的运动规划框架的网络物理系统组成的人与飞行机器人在附近。飞行机器人的运动规划考虑了人的安全感知。我们的目标是根据测试数据确定人类安全感知的参数模型。我们使用虚拟现实作为安全测试环境，收集测试对象在其附近体验飞行机器人时反映在皮肤电反应（GSR）上的安全感知数据。GSR信号既包含与机器人交互驱动的有意义的信息，也包含未知因素的干扰。为了解决这个问题，我们使用两个参数模型来近似GSR数据：(1)机器人位置和速度的函数和(2)随机分布。直觉上，我们需要在给定数据的情况下选择更可能的模型。当GSR在统计上与飞行机器人无关时，应选择随机分布而不是机器人位置和速度的函数。我们在隐马尔可夫模型（HMM）估计的框架下实现了直观的思想。因此，与高斯噪声模型相比，提出的基于hmm的模型提高了似然性，高斯噪声模型不区分由于未知因素导致的相关和不相关样本。我们还提出了一种数值最优路径规划方法，该方法考虑了安全感知模型，同时保证了与障碍物的空间分离，尽管时间离散化。使用所提出的模型生成的最优路径导致与人类的合理安全距离。相反，在不考虑未知因素的情况下，采用高斯噪声假设的标准回归模型生成的轨迹具有不理想的形状。
In this article, we present a preliminary motion planning framework for a cyber-physical system consisting of a human and a flying robot in vicinity. The motion planning of the flying robot takes into account the human’s safety perception. We aim to determine a parametric model for the human’s safety perception based on test data. We use virtual reality as a safe testing environment to collect safety perception data reflected on galvanic skin response (GSR) from the test subjects experiencing a flying robot in their vicinity. The GSR signal contains both meaningful information driven by the interaction with the robot and also disturbances from unknown factors. To address the issue, we use two parametric models to approximate the GSR data: (1) a function of the robot’s position and velocity and (2) a random distribution. Intuitively, we need to choose the more likely model given the data. When GSR is statistically independent of the flying robot, then the random distribution should be selected instead of the function of the robot’s position and velocity. We implement the intuitive idea under the framework of hidden Markov model (HMM) estimation. As a result, the proposed HMM-based model improves the likelihood compared to the Gaussian noise model, which does not make a distinction between relevant and irrelevant samples due to unknown factors. We also present a numerical optimal path planning method that considers the safety perception model while ensuring spatial separation from the obstacle despite the time discretization. Optimal paths generated using the proposed model result in a reasonably safe distance from the human. In contrast, the trajectories generated by the standard regression model with the Gaussian noise assumption, without consideration of unknown factors, have undesirable shapes.