R-LGP: A Reachability-guided Logic-geometric Programming Framework for Optimal Task and Motion Planning on Mobile Manipulators
arXiv (Cornell University)(2023)
摘要
This paper presents an optimization-based solution to task and motion
planning (TAMP) on mobile manipulators. Logic-geometric programming (LGP) has
shown promising capabilities for optimally dealing with hybrid TAMP problems
that involve abstract and geometric constraints. However, LGP does not scale
well to high-dimensional systems (e.g. mobile manipulators) and can suffer from
obstacle avoidance issues due to local minima. In this work, we extend LGP with
a sampling-based reachability graph to enable solving optimal TAMP on high-DoF
mobile manipulators. The proposed reachability graph can incorporate
environmental information (obstacles) to provide the planner with sufficient
geometric constraints. This reachability-aware heuristic efficiently prunes
infeasible sequences of actions in the continuous domain, hence, it reduces
replanning by securing feasibility at the final full path trajectory
optimization. Our framework proves to be time-efficient in computing optimal
and collision-free solutions, while outperforming the current state of the art
on metrics of success rate, planning time, path length and number of steps. We
validate our framework on the physical Toyota HSR robot and report comparisons
on a series of mobile manipulation tasks of increasing difficulty. Videos of
the experiments are available at https://youtu.be/NEVVHEhQnOQ.
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关键词
motion planning,r-lgp,reachability-guided,logic-geometric
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