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

  address = {Paris, France},
  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}