A Survey of Distributed Optimization Methods for Multi-Robot Systems
This is a survey paper, so it is incremental, summarizing existing methods for researchers and practitioners in multi-robot systems.
The paper surveys distributed optimization methods for multi-robot systems, presenting a framework and comparing algorithms in simulations, with C-ADMM identified as a versatile and attractive method.
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.