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Asynchronous Task Plan Refinement for Multi-Robot Task and Motion Planning.
Yoonchang
Sung, Rahul Shome, and Peter Stone.
In IEEE International Conference
on Robotics and Automation (ICRA), March 2024.
Video
presentation
This paper explores general multi-robot task and motion planning, where multiplerobots in close proximity manipulate objects while satisfying constraints and agiven goal. In particular, we formulate the plan refinement problem—which, givena task plan, finds valid assignments of variables corresponding to solutiontrajectories—as a hybrid constraint satisfaction problem. The proposed algorithmfollows several design principles that yield the following features: (1)efficient solution finding due to sequential heuristics and implicit time androadmap representations, and (2) maximized feasible solution space obtained byintroducing minimally necessary coordination-induced constraints and not relyingon prevalent simplifications that exist in the literature. The evaluation resultsdemonstrate the planning efficiency of the proposed algorithm, outperforming thesynchronous approach in terms of makespan.
@InProceedings{yoonchang_sung_ICRA2024,
author = {Yoonchang Sung and Rahul Shome and Peter Stone},
title = {Asynchronous Task Plan Refinement for Multi-Robot Task and Motion Planning},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {2024},
month = {March},
location = {Yokohama, Japan},
abstract = {This paper explores general multi-robot task and motion planning, where multiple
robots in close proximity manipulate objects while satisfying constraints and a
given goal. In particular, we formulate the plan refinement problem—which, given
a task plan, finds valid assignments of variables corresponding to solution
trajectories—as a hybrid constraint satisfaction problem. The proposed algorithm
follows several design principles that yield the following features: (1)
efficient solution finding due to sequential heuristics and implicit time and
roadmap representations, and (2) maximized feasible solution space obtained by
introducing minimally necessary coordination-induced constraints and not relying
on prevalent simplifications that exist in the literature. The evaluation results
demonstrate the planning efficiency of the proposed algorithm, outperforming the
synchronous approach in terms of makespan.
},
wwwnote={<a href="https://youtu.be/t87o215cU3A?si=U3yhwu4HESupd89v">Video presentation</a>},
}
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