### Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning

@inproceedings{brown_optimal_2020,
title = {Optimal {Sequential} {Task} {Assignment} and {Path} {Finding} for {Multi}-{Agent} {Robotic} {Assembly} {Planning}},
isbn = {978-1-72817-395-5},
url = {https://ieeexplore.ieee.org/document/9197527/},
abstract = {We study the problem of sequential task assignment and collision-free routing for large teams of robots in applications with inter-task precedence constraints (e.g., task A and task B must both be completed before task C may begin). Such problems commonly occur in assembly planning for robotic manufacturing applications, in which sub-assemblies must be completed before they can be combined to form the ﬁnal product. We propose a hierarchical algorithm for computing makespan-optimal solutions to the problem. The algorithm is evaluated on a set of randomly generated problem instances where robots must transport objects between stations in a “factory” grid world environment. In addition, we demonstrate in high-ﬁdelity simulation that the output of our algorithm can be used to generate collision-free trajectories for non-holonomic differential-drive robots.},
language = {en},
urldate = {2021-04-09},
booktitle = {2020 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
publisher = {IEEE},
author = {Brown, Kyle and Peltzer, Oriana and Sehr, Martin A. and Schwager, Mac and Kochenderfer, Mykel J.},
month = may,
year = {2020},
pages = {441--447},
month_numeric = {5}
}