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: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=482711
• 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.

你怎么能走到一个想要的位置，伸手抓住一个物体，如此优雅和轻松，而我们的机器人却如此笨重，缓慢和容易出错？这是一个与工程师有关的问题，他们探索自然世界是如何运作的，以便使我们创造的人工制品更好（称为生物灵感或仿生学）。我特别关心的是让机器人探索太空和行星，这些对人类来说太遥远了。为了做到这一点，我们需要让我们的机器人更有能力，更聪明。毫无疑问，如果我们能让机器人变得更聪明，我们就能让它们做更多的事情，从而让它们成为更有效的代理人。我们可以在更大的背景下更快、更便宜、更有效地了解我们的环境。特别是，天体生物学科学对我们的机器人探险家提出了更高的要求，要求他们在火星上寻找生命。我的方法是研究生物有机体学会了做什么以及如何做。一个重要的教训是，在机器人和其他应用中普遍采用的传统反馈控制本身不足以赋予鲁棒性和适应性。生物进化已经进化出额外的策略来增强有机体的性能，以补偿其神经“湿件”中信息传输的缓慢。这些策略基于前馈预测，要么是遗传的（比如新生马驹出生后立即行走），要么是通过经验学习的（比如人类婴儿需要一年或更长时间才能学会走路）。通过将这种前馈方法与机器人中的传统反馈方法相结合，我希望实现一种更自然的行为，这种行为是快速的，并且可以适应可变的环境。这将使我们能够创造更有效的机器人探险家到其他星球。但这只是第一步-前馈控制只是智能控制器的一个方面，但它是一个重要的方面。