RI: Small: Network Formation for Multi-Robot Systems

在搜索和救援、环境监测等应用中，许多多机器人任务都需要机器人之间进行通信。 与当前大多数工作相比，机器人部署在受控环境中并保证连接，当多机器人系统部署在现实世界的应用中时，它们通常会由于导航错误，通信故障或计划移动到通信被拒绝的区域而失去连接。当这种情况发生时，机器人需要一种系统而有效的方式来重新连接到网络中。该项目研究的理论问题是，机器人应该如何移动，以便他们可以快速形成一个网络。网络的形成概括了一些基本问题（例如，会合搜索，冻结标签，最小化运动），这些问题已经在相对简单的设置中研究过。这项工作试图引入一个统一的处理和解决与机器人应用相关的方面，例如障碍物和传感限制的存在。拟议的工作将研究网络形成的三个变种的一些实际相关的环境模型。前两个变体基于机器人可用的信息（例如，初始位置是否已知）。 第三种变体将引入新的网络形成问题，其中一组具有不同运动能力的异构车辆（例如，地面机器人和飞行器）试图形成一个网络。这些变体被制定为新的优化问题，其解决方案将提供有效的网络形成策略，可证明的性能保证。将与地对地和地对空车辆小组一起在模拟和实地应用中对结果进行评价。

Many multi-robot tasks, which arise in search-and-rescue, environmental monitoring and similar applications, require the robots to communicate with one another. In contrast to most current work, where the robots are deployed in controlled settings and are guaranteed to be connected, as multi-robot systems get deployed in real-world applications, they will often lose connectivity due to navigation errors, communication failures, or planned movements into communications-denied areas. When this happens, the robots need a systematic and efficient way to reconnect into a network. This project studies the theoretical question of how should the robots move so that they can quickly form a network.Network formation generalizes a number of fundamental problems (e.g. rendezvous search, freeze tag, minimizing movement) that have been studied in relatively simple settings by disjointed groups of communities. This work attempts to introduce a unified treatment and address aspects relevant to robotics applications, such as the presence of obstacles and sensing limitations. The proposed work will study three variants of network formation for a number of practically relevant environment models. The first two variants are based on the information available to the robots (e.g., whether the initial locations are known). The third variant will introduce novel network formation problems in which a team of heterogeneous vehicles with different motion capabilities (e.g. ground robots on a terrain and aerial vehicles flying over them) try to form a network. These variants are formulated as novel-optimization problems whose solutions will provide efficient network-formation strategies with provable performance guarantees. Results will be evaluated in simulation and field applications with ground-ground and ground-air vehicle teams.