A Survey of Distributed Optimization Methods for Multi-Robot Systems
arXiv: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.
submitted to IEEE T-RO
References in corpus (17)
- On the Linear Convergence of the ADMM in Decentralized Consensus Optimization
- Multi-Agent Distributed Optimization via Inexact Consensus ADMM
- A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
- Optimal algorithms for smooth and strongly convex distributed optimization in networks
- Online Distributed Optimization on Dynamic Networks
- Decentralized Quasi-Newton Methods
- DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers
- ExtraPush for convex smooth decentralized optimization over directed networks
- Asynchronous and Parallel Distributed Pose Graph Optimization
- A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization
- Zeroth Order Nonconvex Multi-Agent Optimization over Networks
- A Distributed Asynchronous Method of Multipliers for Constrained Nonconvex Optimization
- Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions
- A Framework for Parallel and Distributed Training of Neural Networks
- A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking
- Distributed Certifiably Correct Pose-Graph Optimization
- Optimization over time-varying directed graphs with row and column-stochastic matrices