  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.
