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Probabilistic roadmaps (PRMs) are a popular representation used by many current path planners. Construction of a PRM requires the ability to generate a set of random samples from the robot's configuration space, and much recent research has concentrated on new methods to do this. In this paper, we present a sampling scheme that is based on the manipulability measure associated with a robot arm. Intuitively, manipulability characterizes the arm's freedom of motion for a given configuration.doi:10.1109/robot.2002.1014855 dblp:conf/icra/LevenH02 fatcat:3hre3bfivjgn3jcv4ua2duiwsu