Hierarchical control for space robotic applications

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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.