Decentralized multi-agent exploration with online-learning of Gaussian processes
Decentralized multi-agent exploration with online-learning of Gaussian processes
复制标题
通过高斯过程在线学习进行去中心化多智能体探索
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
L. Merino
中科院分区：
文献类型：
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作者：
Alberto Viseras Ruiz;T. Wiedemann;Christoph Manss;L. Magel;Joachim Müller;D. Shutin;L. Merino
勘探是搜救任务等生命安全应用中的关键问题。高斯过程构成了一个有趣的底层数据模型，它利用要探索的过程的空间相关性来减少所需的数据采样。此外，多智能体方法为探索提供了众所周知的优势。以前使用高斯过程作为底层数据模型的分散多智能体探索算法仅通过仿真进行了验证。然而，一种探索算法的实现带来了尚未解决的困难。在这项工作中，我们提出了一种探索算法，该算法处理以下挑战：(i)传输哪些信息以实现多智能体协调；（ii）如何实施轻型避碰装置；（iii）如何在没有先验信息的情况下学习数据的模型。我们用两个真实的机器人实验验证了我们的算法。首先，我们利用地面机器人探测磁场强度。其次，两架配备超声传感器的四轴飞行器探测地形轮廓。我们表明，我们的算法优于蜿蜒和随机轨迹，并且我们能够在探索的同时在线学习数据模型。
Exploration is a crucial problem in safety of life applications, such as search and rescue missions. Gaussian processes constitute an interesting underlying data model that leverages the spatial correlations of the process to be explored to reduce the required sampling of data. Furthermore, multi-agent approaches offer well known advantages for exploration. Previous decentralized multi-agent exploration algorithms that use Gaussian processes as underlying data model, have only been validated through simulations. However, the implementation of an exploration algorithm brings difficulties that were not tackle yet. In this work, we propose an exploration algorithm that deals with the following challenges: (i) which information to transmit to achieve multi-agent coordination; (ii) how to implement a light-weight collision avoidance; (iii) how to learn the data's model without prior information. We validate our algorithm with two experiments employing real robots. First, we explore the magnetic field intensity with a ground-based robot. Second, two quadcopters equipped with an ultrasound sensor explore a terrain profile. We show that our algorithm outperforms a meander and a random trajectory, as well as we are able to learn the data's model online while exploring.