Hierarchical control for space robotic applications

空间机器人应用的分层控制

• 批准号: 355488-2010
• 负责人: Ellery, Alexander
• 金额: $ 1.75万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=595675
• 关键词: Hierarchical; control; space; robotic; applications

How is it that you can walk to a desired location and reach out and grasp an object with such grace and ease yet our robots are so clunky, slow and error-prone? That is a question that concerns engineers who explore how the natural world works in order to make our created artifacts better (known as bio-inspiration or biomimetics). My particular concern is to make robots to explore space and planets which are too far away for humans to explore. In order to do this we need to make our robots more capable and smarter. There is no doubt that if we can make robots smarter we can get them to do more things and therefore make them more effective surrogates. We could learn more about our environment in its larger context faster, cheaper and more efficiently. In particular, the science of astrobiology is making ever greater demands on our robot explorers to search for life on Mars. My approach is to examine what biological organisms have learned to do and how. One important lesson is that traditional feedback control commonly adopted in robotics and other applications is by itself insufficient to impart robust and adaptable behaviour. Biological evolution has evolved additional strategies to enhance an organism's performance to compensate for the slow transmission of information in their neural "wetware". These strategies are based on feedforward prediction either inherited genetically (like a newborn foal walking immediately after being born) or learned through experience (like a human baby which takes a year or more to learn to walk). By incorporating such feedforward approaches to the traditional feedback approaches in robots, I hope to achieve a much more natural behaviour which is rapid and can adjust to a variable environment. This will enable us to create more efficient robot explorers to other planets. But this will only be the first step - feedforward control is merely one aspect of intelligent controllers but it is an important one.

您如何步行到所需的位置并以如此宽敞和轻松的方式伸出手并抓住一个物体，但是我们的机器人如此笨拙，缓慢且容易出错？这个问题涉及探索自然世界如何运作的工程师，以使我们创造的人工制作更好（称为生物试觉或仿生学）。我特别关注的是，让机器人探索太空和行星，这些空间和行星太远，无法探索人类。为此，我们需要使我们的机器人更有能力和更聪明。毫无疑问，如果我们能使机器人更聪明，我们可以让他们做更多的事情，从而使它们更有效地代理。我们可以在更大，更便宜，更有效的速度上更多地了解我们的环境。特别是，天体生物学科学对我们的机器人探险家的要求越来越大，以寻找火星上的生活。我的方法是检查哪些生物生物学学会的生物以及如何做。一个重要的教训是，机器人技术和其他应用程序中通常采用的传统反馈控制本身不足以赋予强大和适应性的行为。生物进化已经发展了其他策略，以提高生物体的性能，以弥补其神经“湿软件”中信息的缓慢传输。这些策略是基于饲养场预测的遗传性（例如出生后的新生儿小马驹行走）或通过经验来学习的（例如，人类婴儿需要一年或更长时间才能学会走路）。通过在机器人中将这种馈送方法纳入传统的反馈方法，我希望实现一种更加自然的行为，该行为快速，可以适应可变环境。这将使我们能够为其他行星创建更有效的机器人探险家。但这仅是第一步 - 前馈控制仅仅是智能控制器的一个方面，但这是一个重要的控制器。