Distributed Optimization Methods for Multi-Robot Systems: Part II -- A Survey

TL;DR

This survey reviews fully-distributed optimization algorithms applicable to multi-robot systems, highlighting their structures, variations, practical implications, and open challenges in robotics applications.

Contribution

It provides a comprehensive overview of three main classes of distributed optimization algorithms tailored for multi-robot problems, emphasizing their structures and practical considerations.

Findings

Analyzes three classes of distributed optimization algorithms for multi-robot systems.

Highlights variations and practical implications of these algorithms.

Identifies open research challenges in applying distributed optimization to robotics.

Abstract

Although the field of distributed optimization is well-developed, relevant literature focused on the application of distributed optimization to multi-robot problems is limited. This survey constitutes the second part of a two-part series on distributed optimization applied to multi-robot problems. In this paper, we survey three main classes of distributed optimization algorithms -- distributed first-order methods, distributed sequential convex programming methods, and alternating direction method of multipliers (ADMM) methods -- focusing on fully-distributed methods that do not require coordination or computation by a central computer. We describe the fundamental structure of each category and note important variations around this structure, designed to address its associated drawbacks. Further, we provide practical implications of noteworthy assumptions made by distributed optimization algorithms, noting the classes of robotics problems suitable for these algorithms. Moreover, we identify important open research challenges in distributed optimization, specifically for robotics problems.