论文标题
通过图表学习，用于异质多机器人传感器覆盖的团队分配
Team Assignment for Heterogeneous Multi-Robot Sensor Coverage through Graph Representation Learning
论文作者
论文摘要
传感器覆盖范围是通过部署多个机器人在环境中最大化事件检测的关键多机器人问题。大型多机器人系统通常由通常不配备完整传感器的简单机器人组成，因此需要具有全面感应能力的团队才能正确覆盖该区域。机器人还表现出多种形式的关系（例如，通信连接或空间分布），在分配机器人团队以进行传感器覆盖范围时需要考虑。为了解决这个问题，在本文中，我们介绍了一种新型的传感器覆盖范围，这些传感器覆盖范围具有异构关系作为图表的学习问题。我们根据定期优化的数学框架提出了一种原则性方法，以从描述异质关系的图表中学习多机器人系统的统一表示，并确定学到的表示表示的基础结构，以便将机器人分配给团队。为了评估所提出的方法，我们对模拟的多机器人系统和物理多机器人系统作为案例研究进行了广泛的实验，这表明我们的方法能够有效地为异构的多机器人传感器覆盖范围分配团队。
Sensor coverage is the critical multi-robot problem of maximizing the detection of events in an environment through the deployment of multiple robots. Large multi-robot systems are often composed of simple robots that are typically not equipped with a complete set of sensors, so teams with comprehensive sensing abilities are required to properly cover an area. Robots also exhibit multiple forms of relationships (e.g., communication connections or spatial distribution) that need to be considered when assigning robot teams for sensor coverage. To address this problem, in this paper we introduce a novel formulation of sensor coverage by multi-robot systems with heterogeneous relationships as a graph representation learning problem. We propose a principled approach based on the mathematical framework of regularized optimization to learn a unified representation of the multi-robot system from the graphs describing the heterogeneous relationships and to identify the learned representation's underlying structure in order to assign the robots to teams. To evaluate the proposed approach, we conduct extensive experiments on simulated multi-robot systems and a physical multi-robot system as a case study, demonstrating that our approach is able to effectively assign teams for heterogeneous multi-robot sensor coverage.
