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A robust localization system for multi-robot formations based on an extension of a Gaussian mixture probability hypothesis density filter
Autonomous Robots ( IF 3.5 ) Pub Date : 2019-06-20 , DOI: 10.1007/s10514-019-09860-5
Alicja Wasik , Pedro U. Lima , Alcherio Martinoli

This paper presents a strategy for providing reliable state estimates that allow a group of robots to realize a formation even when communication fails and the tracking data alone is insufficient for maintaining a stable formation. Furthermore, the tracking information does not provide the identity of the robot, therefore a simple fusion of tracking and communication data is not possible. We extend a Gaussian mixture probability hypothesis density filter to incorporate, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. Our method of combining communicated data, information about the formation and sensory detections is capable of maintaining the state estimates even when long-duration occlusions occur, and improves awareness of the situation when the communication is sporadic or suffers from short-term outage. The proposed method is validated using a high-fidelity simulator in scenarios with a formation of up to five robots. The results show that the proposed tracking strategy allows for sustaining formations in cluttered environments, with high measurement uncertainty and low quality communication.

中文翻译:

基于高斯混合概率假设密度滤波器的扩展的多机器人编队鲁棒定位系统

本文提出了一种提供可靠状态估计的策略,即使在通信失败并且仅跟踪数据不足以维持稳定编队的情况下,一组机器人也可以实现编队。此外,跟踪信息不能提供机器人的身份,因此不可能简单地将跟踪和通信数据融合在一起。我们扩展了一个高斯混合概率假设密度过滤器,以合并,首先,由机器人交换的绝对姿势,其次,合并所需地层的几何形状。我们将通讯数据,形成信息和感官检测信息相结合的方法,即使发生长时间的遮挡,也能够保持状态估计,并在零星通信或短期中断的情况下提高对这种情况的了解。在多达五个机器人的情况下,使用高保真模拟器对提出的方法进行了验证。结果表明,所提出的跟踪策略可以在混乱的环境中维持地层,具有较高的测量不确定性和低质量的通信。
更新日期:2019-06-20
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