On the Performance of Multi-robot Target Tracking
On the Performance of Multi-robot Target Tracking
复制标题
多机器人目标跟踪性能研究
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
S. Roumeliotis
中科院分区：
文献类型：
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作者：
Faraz M. Mirzaei;Anastasios I. Mourikis;S. Roumeliotis
本文研究了移动的机器人协作定位与目标跟踪（CLATT）的精度问题，推导了位置不确定性的解析上界。所获得的界限提供了一个描述的渐近定位性能的机器人和目标的传感器特性和相对位置测量的图形的结构的函数。通过采用扩展卡尔曼滤波（EKF）制定的数据融合，两个关键的渐近结果。第一个提供了保证的最坏情况下的定位精度，而第二个确定的估计的预期协方差的上限。我们研究了联合估计目标和机器人的位置的影响，并证明了它会导致更好的精度为机器人的位置估计。理论结果在仿真和实验中得到了证实。
In this paper, we study the accuracy of cooperative localization and target tracking (CLATT) in a team of mobile robots, and derive analytical upper bounds for the position uncertainty. The obtained bounds provide a description of the asymptotic positioning performance of the robots and the targets as a function of the sensor characteristics and the structure of the graph of relative position measurements. By employing an extended Kalman filter (EKF) formulation for data fusion, two key asymptotic results are derived. The first provides the guaranteed worst-case positioning accuracy, whereas the second determines an upper bound on the expected covariance of the estimates. We investigate the effects of jointly estimating the targets' and the robots' position, and demonstrate that it results in better accuracy for the robots' position estimates. The theoretical results are confirmed both in simulation and experimentally.