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Sampling-Based Robot Motion Planning


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constructed figure walks around obstacles, illustration

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In recent years, robots play an active role in everyday life: medical robots assist in complex surgeries; search-and-rescue robots are employed in mining accidents; and low-cost commercial robots clean houses. There is a growing need for sophisticated algorithmic tools enabling stronger capabilities for these robots. One fundamental problem that robotic researchers grapple with is motion planning—which deals with planning a collision-free path for a moving system in an environment cluttered with obstacles.13,29

To a layman, it may seem the wide use of robots in modern life implies that the motion-planning problem has already been solved. This is far from true. There is little to no autonomy in surgical robots and every owner of a house-cleaning robot has experienced the highly simplistic (and often puzzling) routes taken by the robot.


 

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