RI: Small: Network Formation for Multi-Robot Systems

在搜救、环境监测和类似的应用中，许多多机器人任务都需要机器人之间进行通信。与大多数当前工作不同的是，机器人部署在受控环境中并确保连接，随着多机器人系统部署在现实世界的应用中，它们经常会由于导航错误、通信故障或计划进入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和有效的方式重新连接到网络中。这个项目研究了机器人应该如何移动以使它们能够快速形成网络的理论问题。网络形成概括了一些基本问题(例如，集合搜索、冻结标签、最小化移动)，这些问题已经在相对简单的环境中被分散的社区组研究过。这项工作试图介绍一种统一的治疗方法，并解决与机器人应用相关的方面，如障碍物的存在和感知限制。拟议的工作将为一些实际相关的环境模型研究网络形成的三种变体。前两个变型基于机器人可用的信息(例如，初始位置是否已知)。第三种变型将引入新的网络编队问题，其中具有不同运动能力的一组不同的车辆(例如，地面上的地面机器人和飞越它们的飞行器)试图形成网络。这些变体被描述为新颖的优化问题，其解决方案将提供具有可证明的性能保证的高效网络形成策略。结果将在地面-地面和地面-空中车辆小组的模拟和现场应用中进行评估。

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.