Boustrophedon cell decomposition
Encyclopedia
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
.
A strength of the boustrophedon cell decomposition is that it allows for more diverse, non-polygonal obstacles within a configuration space. 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. 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.
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...
and robotics
Robotics
Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots...
for configuration space
Configuration space
- Configuration space in physics :In classical mechanics, the configuration space is the space of possible positions that a physical system may attain, possibly subject to external constraints...
solutions. Like other cellular decomposition methods, this method transforms the configuration space into cell regions that can be used for path planning
Motion planning
Motion planning is a term used in robotics for the process of detailing a task into discrete motions....
.
A strength of the boustrophedon cell decomposition is that it allows for more diverse, non-polygonal obstacles within a configuration space. 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. 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.