Autonomous Robots for Scientific Monitoring of Marine Environments

是什么阻碍了加拿大水体的机器人环境监测完全自动化？答案是双重的。首先，今天的野外环境数据收集在空间和时间上都受到限制，因为它要么完全由人类科学家执行，因此耗时、稀疏、昂贵和规模有限，要么由静态传感器完成，它稀疏地覆盖感兴趣的体积并产生大量数据，其中只有一小部分对科学家来说是重要的。其次，也是最关键的一点是，机器人忽视了专家科学家真正需要和重视的数据类型。机器人不知道下一步哪里值得导航，也不知道应该注意哪里。该项目所涉及的研究活动以及支持这些活动的设备从整体上解决了这两个问题。 由人类科学家提供信息的自主机器人探索在这两个愿望之间提供了一个富有成效的中间地带，允许在更广泛的领域进行更快的探索，同时关注具有高价值的科学相关数据。使用该设备，我们将演示自主飞行器和自主船之间高效的协作机器人探索，该船配备了水质传感器和采样器，以及相机(多光谱、RGB和热像仪)以及水深和测绘传感器。部署将在具有挑战性的安大略湖海洋环境中进行，突破了迄今为止机器人文献在系统和算法开发方面所展示的极限。 更具体地说，该设备将支持计算机视觉、机器学习和控制算法的开发和验证，以解决室外自然环境中具有挑战性的自主采样、不确定性规划和人-机器人交互问题。它将实现现场的传感、感知和自主机器人导航。

What is preventing robotic environmental monitoring of Canadian water bodies from being fully automated? The answer is twofold. First, today's environmental data collection in the field is spatially and temporally constrained because it is (a) either performed entirely by human scientists and is therefore time-consuming, sparse, expensive, and of limited scale, or (b) it is done by static sensors which sparsely cover the volume of interest and yield a lot of data, only small portions of which is important to the scientist. Second, and most crucial, robots are oblivious to the type of data that expert scientists really need and value. Robots do not know where is worth navigating next or where to pay attention to. The research activities involved in this project and the equipment that will support them address these two questions in a holistic way. Autonomous robot exploration informed by the human scientist provides a fruitful middle ground between these two desiderata, and allows faster exploration, in broader areas, while at the same time being attentive to scientifically relevant data of high value. Using this equipment we will demonstrate efficient collaborative robot exploration between an autonomous aerial vehicle and an autonomous boat, equipped with water quality sensors and samplers, as well as cameras (multi-spectral, RGB, and thermal), and bathymetry and mapping sensors. Deployments will happen in challenging marine environments in Lake Ontario, pushing the envelope of what has been demonstrated in the robotics literature thus far in terms of systems and algorithms development. More specifically, this equipment will support the development and validation of computer vision, machine learning, and control algorithms to address challenging autonomous sampling, planning under uncertainty, and human-robot interaction problems in outdoor natural environments. It will enable sensing, perception, and autonomous robot navigation in the field.