GAIA: Ground-Aerial maps Integration for increased Autonomy outdoors

GAIA：地空地图集成以增强户外自主性

• 批准号: EP/Y003438/1
• 负责人: Riccardo Polvara
• 金额: $ 20.98万
• 依托单位: University of Lincoln
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY003438%2F1
• 关键词: GAIA; Ground; Aerial; maps; Integration

Multi-robot mapping is becoming an always more demanded application for targeting a large variety of problems, such as site inspection, human search and rescue, and orchard monitoring, due to the flexibility of having multiple robotic agents acting cooperatively for completing the same task. In this view, the robots need to communicate one with another to share information on their relative position and their surrounding. Integrating this information allows for obtaining a global overview of the environment and the progress status of the mission. One of the simpler examples of shared information is represented by the local maps built by individual robots during their operations. In the literature, multiple maps integration is usually cast as a map merging problem, in which maps are represented as occupancy grids and stitched together by looking for overlapping parts, or as a multi-robot Simultaneous Localisation and Mapping (SLAM) problem, where graphs of individual robots' pose are built and connected using graph theory. However, this is still an interesting and open research problem despite the effort put into robot localisation in the last twenty years. Differently from the single robot use case, multi-robot maps integration needs a higher level of abstraction to identify which elements are common across multiple maps so to make the integration efficient and real-time. In particular, within this project we call "Ground-Aerial maps Integration for increased Autonomy Outdoors" (GAIA), we target the scenario of having a fleet of heterogeneous robots characterised by complementary behaviours, movements and perception capabilities, making the maps integration problem even harder than homogeneous fleet (e.g., using only ground robots). To solve this problem, this project focuses on exploiting the human understanding of a scene so as to integrate multi-perspective robotic maps.In particular, we plan to use the semantic information in robotic observations to have a better understanding of the scene, to identify which entities are present in it, and to leverage such information so as to integrate multi-perspective observations into a single map representation. More specifically, within GAIA and the scope of this call, we tackle the problem of multi-robot maps integration in the agricultural domain, where robotics solutions can represent a game-changer technology. The possibility of deploying autonomous agents in the field to assist, if not replace, human workers in monitoring and harvesting tasks opens up a new revolution focused on precision agriculture and sustainability. Indeed, robots are equipped with dedicated software and hardware that can assist farmers by collecting data on rainfall, soil moisture and soil composition, so to help them make more target interventions. More specifically, the ground robot offers a closer and more detailed inspection point of view over the crops, while the UAV allows observing a larger field in a shorter time. The UAV offers active sensing capabilities to complete and update a partially complete map on demand, while improving its level of confidence (how much we trust the map) by, for example, mapping human workers or any other agriculture-related tools (e.g., tractors or trolleys) located in the fields. This updated information can be exploited by the UGV's path planner to make the ground platform's deployment more efficient, avoiding those obstructed paths.

由于多个机器人代理协作完成同一任务的灵活性，多机器人测绘正在成为针对各种问题的日益需求的应用，例如现场检查、人员搜索和救援以及果园监控。从这一观点来看，机器人需要相互通信以共享有关其相对位置和周围环境的信息。整合这些信息可以获取环境的全局概览和任务的进度状态。共享信息的一个更简单的例子是由各个机器人在操作过程中构建的本地地图来表示。在文献中，多地图集成通常被视为地图合并问题，其中地图表示为占用网格并通过寻找重叠部分缝合在一起，或者被视为多机器人同时定位和建图（SLAM）问题，其中使用图论构建和连接各个机器人的姿态图。然而，尽管过去二十年来人们在机器人定位方面付出了努力，但这仍然是一个有趣且开放的研究问题。与单个机器人用例不同，多机器人地图集成需要更高层次的抽象来识别多个地图中哪些元素是通用的，从而使集成高效且实时。特别是，在这个我们称之为“增强户外自主性的地空地图集成”（GAIA）的项目中，我们的目标是拥有一组具有互补行为、运动和感知能力的异构机器人舰队，使得地图集成问题比同质舰队（例如，仅使用地面机器人）更加困难。为了解决这个问题，该项目的重点是利用人类对场景的理解来集成多视角机器人地图。特别是，我们计划使用机器人观察中的语义信息来更好地理解场景，识别场景中存在哪些实体，并利用这些信息将多视角观察集成到单个地图表示中。更具体地说，在 GAIA 和本次电话会议的范围内，我们解决农业领域的多机器人地图集成问题，其中机器人解决方案可以代表一种改变游戏规则的技术。在现场部署自主代理来协助（如果不是取代）人类工人进行监测和收割任务的可能性开启了一场以精准农业和可持续性为重点的新革命。事实上，机器人配备了专用的软件和硬件，可以帮助农民收集降雨量、土壤湿度和土壤成分的数据，从而帮助他们进行更有针对性的干预。更具体地说，地面机器人提供了对农作物更近距离、更详细的检查视角，而无人机则可以在更短的时间内观察更大的田地。无人机提供主动传感功能，可以根据需要完成和更新部分完整的地图，同时通过绘制人类工人或位于田间的任何其他农业相关工具（例如拖拉机或手推车）等方式来提高其置信度（我们对地图的信任程度）。 UGV 的路径规划器可以利用这些更新的信息来提高地面平台的部署效率，从而避开那些受阻的路径。