Abstract
Many robotic tasks require compliant motions, but planning such motions poses special challenges not present in collision-free motion planning. One challenge is how to achieve exactness, that is, how to make sure that a planned path is exactly compliant to a desired contact state, especially when the configuration manifold of such a contact state is hard to describe analytically due to high geometrical complexity and/or high dimensionality. The authors tackle the problem with a hybrid approach of direct exploitation of contact constraints and randomized planning. They particularly focus on planning motion that maintains certain contact state or contact formation (CF), called a CF-compliant motion, because a general compliant motion is a sequence of such CF-compliant motions with respect to different CFs. This paper describes a randomized planner for planning CF-compliant motion between two arbitrary polyhedral solids, extending the probabilistic roadmap paradigm for planning collision-free motion to the space of contact configurations. Key to this approach is a novel sampling strategy to generate random CF-compliant configurations. The authors also present and discuss examples of sampling and planning results.
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