To meet its objectives, a robot would plan a simple sequence of actions. For instance, to revise the order of a stack of blocks, it would assess the blocks' locations, then draw up a list of things to do, such as lifting the first block, putting it down beside the stack, lifting the second block, and so on. It could take as much time as it needed to make its initial visual assessment of the situation and formulate a plan. Coping with somebody walking into the room and pushing the stack over half way through the sequence was not part of the model. But animals and people get things done despite messy, unruly environments over which they exert far from complete control.
Soccer is all about reaching goals amid a continually changing situation, and doing it in real time.
In robosoccer, likewise, robots must continually take into account their environment, including their own location and orientation on the field, the location of the ball, and the positions of teammates and opposing players. They must be able to react quickly, but within an overall game plan. Hands-on learning The richness of the robosoccer challenge has attracted a growing number of researchers and, significantly, students as well. Raffaello D'Andrea is an assistant professor of mechanical engineering at Cornell University in upstate New York and a robosoccer enthusiast. There are two reasons why I've done this project.