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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 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.
@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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