Boustrophedon cell decomposition

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The boustrophedon cell decomposition (BCD) is a method used in artificial intelligence and robotics for configuration space solutions. Like other cellular decomposition methods, this method transforms the configuration space into cell regions that can be used for path planning.

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. More specifically, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. Colloquially, the term "artificial intelligence" is used to describe machines that mimic "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".

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In classical mechanics, the parameters that define the configuration of a system are called generalized coordinates, and the vector space defined by these coordinates is called the configuration space of the physical system. It is often the case that these parameters satisfy mathematical constraints, such that the set of actual configurations of the system is a manifold in the space of generalized coordinates. This manifold is called the configuration manifold of the system.

A strength of the boustrophedon cell decomposition is that it allows for more diverse, non-polygonal obstacles within a configuration space. [1] The representation still depicts polygonal obstacles, but the representations are complex enough that they are very effective when describing things like rounded surfaces, jagged edges, etc.

It is a goal of the method to optimize a path that can be chosen by an intelligent system. [2] While a BCD can represent the existence of objects in a physical space, it does very little to nothing in terms of recognizing the objects. This would be done using another method, one which most likely requires additional sensory data in order to be used.

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