Information fusion in human-robot collaboration using neural network representation
Information fusion in human-robot collaboration using neural network representation
复制标题
使用神经网络表示的人机协作中的信息融合
DOI：
10.1109/smc.2014.6974234
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
S. Mehta
中科院分区：
文献类型：
--
作者：
Ashwin P. Dani;M. McCourt;J. Curtis;S. Mehta
本文提出了一种软硬数据融合算法，利用自主智能体上传感器的硬数据和人类观测的软数据对运动目标进行跟踪。本文确定并解决了两个主要挑战：1。如何对人类观察进行建模；如何使用软数据估计状态，并将其与自主代理（如相机传感器）上传感器的状态估计融合。提出了一种利用人工神经网络（ANN）将感知到的人类观察结果模拟为真实物理状态的新方法。采用粒子滤波（PF）基于人类观察者的距离和方位观测数据估计运动目标的状态，采用EKF算法利用车载相机传感器估计运动目标的状态。距离测量用Kumaraswamy的双界分布表示。基于人工神经网络学习到的人类观察模型计算的状态估计使用快速协方差交叉（CI）算法与车载传感器的状态估计融合。CI算法在使用人类测量和机器人传感器测量获得的状态估计之间没有未知相关性的情况下产生一致的融合估计。在目标跟踪仿真平台上对算法的性能进行了验证。
In this paper, an algorithm for hard and soft data fusion is developed for tracking moving objects using hard data from sensors on autonomous agents and soft data from human observations. Two main challenges are identified and addressed in this paper: 1. how to model the human observation, 2. how to estimate state using soft data and fuse it with the state estimates from the sensors on autonomous agents (e.g., a camera sensor). A novel approach is developed to model perceived human observations to the real physical states using artificial neural networks (ANN). A particle filter (PF) is used to estimate a moving target's state based on range and bearing observation data from a human observer and an EKF is used to estimate the target state using on-board camera sensor. The range measurement is represented using Kumaraswamy's double bounded distribution. The state estimates computed based on a model of human observation learned by an ANN are fused with the state estimates from the on-board sensors using a fast covariance intersection (CI) algorithm. The CI algorithm yields consistent fused estimates in the absence of unknown correlations between state estimates obtained using human measurements and robot sensor measurements. The performance of the developed algorithms is validated on a target tracking simulation platform.