Seeking a better view: Guiding cameras for optimal imaging via reinforcement learning

在生物科学中，图像捕获通常用于显微镜、实验室和野外环境，作为获得可靠的植物科学测量的第一步。良好的图像捕获是许多此类实验的基础，通过测量植物，您可以确定哪些更健康，更健壮，或生产更多的食物。然而，你无法测量你看不见的东西，而捕捉到更好的图像对于测量表明作物产量更高或抗虫害的细微差异至关重要。该项目将探索强化和机器学习方法，以训练机器人系统在没有人类交互的情况下捕获更好的图像。它将评估各种奖励系统来训练人工智能，根据目前所看到的情况，人工智能可以为哪种观点可能是最好的提供指导。跨学科合作，博士将培训各种植物主题的自动图像捕获系统，其大小和形状不等，旨在提高包括3D重建，特征检测和计数在内的一系列任务的性能。机器人将包括线性一维和二维驱动器，以及一个六自由度机器人机械手。

In Biosciences, image capture is often used in microscopy, lab and field environments as the first step in obtaining reliable scientific measurements of plants. Good image capture is fundamental to many of these experiments, by measuring plants you can determine which are healthier, more robust, or producing more food. However, you can't measure what you can't see, and, capturing a better image could be crucial in measuring the subtle differences which indicate a higher yielding crop, or a resistance to pests.This project will explore reinforcement and machine learning approaches to train robotic systems to capture better images with no human interaction. It will evaluate varied reward systems to train AI that can provide guidance on which view is likely to be best given what has been seen so far. Working across disciplines, the PhD will training automated image capture systems on a variety of plant subjects ranging in size and shape, with a view to improving the performance on a range of tasks including 3D reconstruction, feature detection and counting. The robotics will include linear 1D and 2D actuators, as well as a 6-dof robotic manipulator.