Distributed Multi-Robot Cooperation for Information Gathering Under Communication Constraints
Distributed Multi-Robot Cooperation for Information Gathering Under Communication Constraints
复制标题
通信约束下分布式多机器人协作信息采集
DOI：
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发表时间：
2018
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;Zhe Xu;L. Merino
最近的许多工作提出了受益于多机器人合作的信息收集算法。然而，大多数算法要么采用状态和动作空间的离散化，这使得它们对于具有复杂动力学的机器人系统来说在计算上变得困难；或者无法处理机器人间的限制，例如沟通限制。本文提出了一种解决上述两个问题的多机器人信息收集方法。为此，我们提出了一种算法，以创新的方式结合高斯过程（GP）来建模感兴趣的物理过程，RRT规划器在连续域中规划路径，以及分布式决策算法来实现多机器人合作。具体来说，我们通过定义信息论效用函数和路径聚类方法，采用最大和算法进行分布式多机器人协作。此功能可最大限度地收集信息，但受到机器人间通信的限制。我们在模拟和现场实验中验证了所提出的方法，其中三个四轴飞行器探索模拟风场。结果证明了该方法的有效性。
Many recent works have proposed algorithms for information gathering that benefit from multi-robot cooperation. However, most algorithms either employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics; or cannot deal with inter-robot restrictions like e.g. communication constraints. This paper presents an approach for multi-robot information gathering that tackles the two aforementioned issues. To this end we propose an algorithm that combines in an innovative manner Gaussian processes (GPs) to model the physical process of interest, RRT planners to plan paths in a continuous domain, and a distributed decision-making algorithm to achieve multi-robot cooperation. Specifically, we employ the Max-sum algorithm for distributed multi-robot cooperation by defining an information-theoretic utility function together with a path clustering approach. This function maximizes information gathering, subject to inter-robot communication constraints. We validate the proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness of the approach.