多机器人定位中基于熵的分布式观测量选择方法

本文提出了多机器人定位中基于熵的分布式观测量选择新方法.在多机器人基于相对观测量的合作定位中,当队列中某个机器人在某时刻获得多个相对观测量时,我们可以融合所有这些观测来更新整个队列的位置及协方差矩阵.但随着机器人个数及观测量的增加,定位计算量将迅速增长,影响了定位的实时性和有效性.为了减轻计算负担、保持定位的实时性,首先对这些观测量进行选择,找出那些具有大的信息量的观测,利用这些观测量来更有效的更新整个队列的位置及协方差矩阵.在保证一定定位精度的前提下,减少了整个队列定位的计算量,提高了定位的实时性和可靠性.我们研究比较了在选择不同数量的观测量的条件下,定位精度和定位时间的变化.仿真实验结果表明,基于熵的分布式观测量选择方法可有效地提高定位的效率,尤其是在机器人个数比较多的情况下,更能显示它的优势.

Distributed Entropy Based Relative Observation Selection for Multi-Robot Localization

We propose a novel distributed entropy-based measurement selection method for multi-robot localization.In multi-robot cooperative localization based on relative measurements, all the measurements obtained by a robot at one moment are fused to update pose estimation and covariance matrix. As the number of robots and measurements increase, the computationa cost increase fast, then influencing the real-time and efficiency of localization.In order to reduce the computational burden and keep real-time localization,those measurements,which yield the most information gain in estimating robots location,are selected from all the measurements obtained by the robot group to update the whole group pose estimation and the covariance matrix.It ensures the nocessary localization accuracy and rneantime reduces the computational burden, so as to improve the reliability and real-time of localization. We compare the localization accuracy and the computation time by using different number of measurerments. Simulation results show that the proposed method can effectively improve the efficiency in dealing with multi-robot localization,especially when the group is large.