Let's reduce the gap between task planning and motion planning

摘要

In the stream of research that aims to speed up practical planners, we propose an approach to task planning based on probabilistic roadmap methods (PRM). Our contribution is twofold. The first issue concerns the development of ShaPer, a task planner that is able to deal efficiently with large problems. ShaPer "captures" the structure of the task space. The second contribution involves promising results on robot task planning. This is obtained through an analysis of the task space structure that exhibits the relation between task and geometric reasoning for a given robot task. To illustrate such an approach, we solve a complex problem where motion and task planning are closely interleaved.