Autonomous Robots for Scientific Monitoring of Marine Environments

是什么阻止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，今天的环境数据收集在该领域的空间和时间上的限制，因为它是（a）完全由人类科学家，因此是耗时的，稀疏的，昂贵的，有限的规模，或（B）它是由静态传感器稀疏地覆盖感兴趣的体积，并产生大量的数据，只有一小部分是重要的科学家。其次，也是最关键的一点，机器人对专家科学家真正需要和重视的数据类型一无所知。机器人不知道下一步哪里值得导航，也不知道哪里值得注意。该项目所涉及的研究活动和支持这些活动的设备以综合的方式解决了这两个问题。 由人类科学家提供信息的自主机器人探索在这两个迫切需要之间提供了一个富有成效的中间地带，并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，我们将展示自主飞行器和自主船之间的高效协作机器人探索，配备水质传感器和采样器，以及相机（多光谱，RGB和热），以及测深和测绘传感器。部署将发生在具有挑战性的海洋环境中的安大略湖，推动了迄今为止在系统和算法开发方面的机器人文献中所展示的内容。 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，以解决具有挑战性的自主采样，不确定性下的规划以及户外自然环境中的人机交互问题。它将使传感，感知和自主机器人导航领域。

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.