A Survey of Distributed Optimization Methods for Multi-Robot Systems

@article{halsted_survey_2021,
  title = {A {Survey} of {Distributed} {Optimization} {Methods} for {Multi}-{Robot} {Systems}},
  url = {http://arxiv.org/abs/2103.12840},
  abstract = {Distributed optimization consists of multiple computation nodes working together to minimize a common objective function through local computation iterations and network-constrained communication steps. In the context of robotics, distributed optimization algorithms can enable multi-robot systems to accomplish tasks in the absence of centralized coordination. We present a general framework for applying distributed optimization as a module in a robotics pipeline. We survey several classes of distributed optimization algorithms and assess their practical suitability for multi-robot applications. We further compare the performance of different classes of algorithms in simulations for three prototypical multi-robot problem scenarios. The Consensus Alternating Direction Method of Multipliers (C-ADMM) emerges as a particularly attractive and versatile distributed optimization method for multi-robot systems.},
  urldate = {2021-03-29},
  journal = {arXiv:2103.12840 [cs]},
  author = {Halsted, Trevor and Shorinwa, Ola and Yu, Javier and Schwager, Mac},
  month = mar,
  year = {2021},
  note = {Under Review},
  keywords = {Computer Science - Robotics},
  month_numeric = {3}
}