Using graph based approaches save trajectories for manipulators can be planned fast. It is favorable to use techniques that allow to plan at least some motions from the beginning of the graph construction process, and that can be improved incrementally. We introduce an approach that fulfills the above requirements using random configurations for graph construction (unless specific tasks are given) in configuration space. Graph nodes serve as subgoals and graph edges as collision free sub-trajectories. We show the high performance of this approach with respect to preprocessing and trajectory generation time, as well as planning success in a realistic simulation of a real world manipulator task.
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