Mobile ad-hoc communication in machine swarms for relative positioning based on GNSS-raw data exchange
Mobile ad-hoc communication in machine swarms for relative positioning based on GNSS-raw data exchange
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基于 GNSS 原始数据交换的相对定位机器群中的移动自组织通信
DOI：
10.1109/plans.2012.6236983
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
P. Hecker
中科院分区：
文献类型：
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作者：
J. Schattenberg;T. Lang;S. Batzdorfer;M. Becker;U. Bestmann;P. Hecker
随着移动工作机器的自动化程度的提高，以及越来越多地使用一台以上的机器来完成协同任务，需要有关机器之间以及机器及其附件之间相对位置的信息。当在编队中的分布式机器上执行任务时，这一点尤其必要和重要。应用的例子是农业企业的并行收获过程或在雪灾或城市地震后的幸存者合作搜索。此外，在GNSS接收机部分故障或接收不良的情况下，确保相对位置信息非常重要，例如避免机器之间的碰撞。解决这个问题的方法是将全球导航卫星系统（GNSS）的测量与惯性测量单元（IMU）的测量相结合。进一步的改进或稳定可以通过基于视觉的系统来完成，比如使用光流方法进行运动估计的2D或3d相机系统。另一种确定群体几何形状的改进方法是所谓的群体定位方法，本文将对此进行描述。该方法基于使用移动自组织网络在群中的每个参与者之间交换测量的GNSS原始数据，即距离测量。此外，GNSS原始测量和惯性测量使用多个滤波器耦合，以检测退化的GNSS测量并将其排除在进一步的数据处理之外。移动自组织网络的挑战在于时变网络结构和小的可用传输速率，以及对快速数据交换的高要求。由于需要非常灵活地响应拓扑变化、补偿单个网络参与者的损失以及自发地集成更多参与者，因此不允许使用固定的协调器。因此，不同的路由算法必须组合和开发，以确保在各种场景下的信息交换。
The increasing automation of mobile working machines and the progressive use of more than one machine up to machine swarms for cooperative tasks demands information about the relative position between the machines as well as between the machines and their attachments. This is especially necessary and important when carrying out tasks on distributed machines in a formation. Examples for application are parallel harvesting process in agricultural business or the cooperative search for survivors after a snow slide or e.g. an earthquake in urban scenarios. Moreover, it is very important to ensure relative position information in case of partial failure or a poor reception of the GNSS receiver, for example to avoid the collision between the machines. Options to handle this problem are the coupling of Global Navigation Satellite System (GNSS) measurements with measurements of an Inertial Measurement Unit (IMU). A further improvement or stabilization can be done by vision based systems, like 2D- or 3D-camera system using methods of optical flow for motion estimation. Another possibility for improvement for determining the swarm geometry, which will be described in this paper, is the so called swarm positioning method. This method is based on the exchange of the measured GNSS raw data, i.e. range measurements, between each participant in the swarm using a mobile ad-hoc network. Additionally, GNSS raw measurements and inertial measurements are coupled using multiple filters in order to detect degraded GNSS measurements and exclude these from further data processing. The challenge of the mobile ad-hoc network is the time variant network structure and the small available transmission rate in combination with a high demand for quick data exchange. The requirement to respond very flexibly to changes in topology and to compensate the loss of individual network participants as well as to spontaneously integrate further participants forbids the use of a fixed coordinator. Therefore different routing algorithms have to be combined and developed to ensure an information exchange in various scenarios.