![]() ![]() We often see videos of the latest research robot in the lab, performing fantastic feats of dexterity, navigation, or teamwork, and we are tempted to ask, “Why isn’t this used in the real world?” Well, next time you see such a video, take a look at how highly-controlled the lab environment is. This is one of the key reasons that robotics programming is so difficult. (Unless some benevolent outside force restores it.) Often, once control is lost, it can never be regained. As soon as the real world deviates from these assumptions, however, we will no longer be able to make good guesses, and control will be lost. As long as the real world behaves according to the assumptions of the model, we can make good guesses and exert control. Thus, one of the first steps in control design is to come up with an abstraction of the real world, known as a model, with which to interpret our sensor readings and make decisions. Robot control software can only guess the state of the real world based on measurements returned by its sensors. It can only attempt to change the state of the real world through the generation of control signals. The fundamental challenge of all robotics is this: It is impossible to ever know the true state of the environment. The Challenge of the Programmable Robot: Perception vs. The snippets of code shown here are just a part of the entire simulator, which relies on classes and interfaces, so in order to read the code directly, you may need some experience in Python and object oriented programming.įinally, optional topics that will help you to better follow this tutorial are knowing what a state machine is and how range sensors and encoders work.
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