Hybrid Planning: Task-Space Control and Sampling-Based Planning

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Abstract

We propose a hybrid approach to motion planning for redundant robots, which combines a powerful control framework with a sampling-based planner. We argue that a suitably chosen task controller already manages a huge amount of trajectory planning work. However, due to its local approach to obstacle avoidance, it may get stuck in local minima. Therefore we augment it with a globally acting planner, which operates in a lower-dimensional search space, thus circumventing the curse of dimensionality afflicting modern, many-DoF robots.

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