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Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

机译:异构多机器人系统的分布式领导辅助定位方法

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This paper presents a distributed leader-assistive localization approach for a heterogeneous multirobotic system (MRS). The localization algorithm is formulated to estimate the position and orientation (pose) of a group of robots in a given reference coordinate frame (or global coordinate frame). It is assumed that the heterogeneous-MRS has one or a group of robots (which we refer as leader robots) with higher sensor payload, higher processing power, and larger memory capacity, enabling accurate self-localization capabilities. Robots with limited resources (which we refer as child robots) rely on leader robots, and inter-robot observations between leaders and themselves for localization. Finite-range sensing is a key challenge for such leader-assistive localization. This study presents a sensor sharing technique which virtually enhances the sensing range of leader robots. In the proposed method, each robot locally runs a cubature Kalman filter to estimate its own pose and hosts a low cost, lightweight, and low-power sensory system to periodically measure relative pose of neighbors. Each robot transmits these relative pose measurements to leader robots. Leader robots then combine available relative observations in order to synthesise global pose measurements and associated noise covariances for child robots. Child robots are acknowledged by the leader robots with the synthesized global pose measurements and fuse these measurements with their local belief in order to improve their localization. Theoretical developments are presented to virtually enhance the leader robots’ sensing range. The performance of the proposed distributed leader-assistive localization algorithm is evaluated on a multirobot simulation test-bed and on a publicly available multirobot localization and mapping data-set. The results illustrate that the proposed algorithm is capable of establishing accurate and consistent localization for the child robots even when they operate beyond the se- sing range of the leader robots.
机译:本文提出了一种异构的多机器人系统(MRS)的分布式领导者辅助定位方法。制定了定位算法,以估算给定参考坐标系(或全局坐标系)中一组机器人的位置和方向(姿势)。假定异构MRS具有一个或一组具有更高传感器有效负载,更高处理能力和更大存储容量的机器人(我们称为领导者机器人),从而实现了准确的自定位功能。资源有限的机器人(我们称为子级机器人)依赖于领导者机器人,以及领导者与自身之间的机器人间观察以进行本地化。有限距离感测是这种领导者辅助定位的关键挑战。这项研究提出了一种传感器共享技术,该技术实际上可以增强领导者机器人的感应范围。在提出的方法中,每个机器人都在本地运行一个库尔曼卡尔曼滤波器来估计自己的姿势,并托管一个低成本,轻巧和低功耗的传感系统来定期测量邻居的相对姿势。每个机器人将这些相对姿态测量值发送给领导者机器人。领导者机器人然后结合可用的相对观测值,以便为儿童机器人合成全局姿势测量值和相关的噪声协方差。领导机器人确认儿童机器人具有综合的全局姿势测量值,并将这些测量值与它们的本地信念融合在一起,以改善其定位。提出了理论上的发展以虚拟地扩展领导者机器人的感应范围。在多机器人仿真测试台上以及在公开可用的多机器人本地化和制图数据集上评估了所提出的分布式领导者辅助定位算法的性能。结果表明,即使子机器人超出领导者机器人的选择范围,所提出的算法也能够为子机器人建立准确一致的定位。

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