A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions

TL;DR

This paper reviews distributed optimization methods applicable to cooperative robotics, discussing theoretical frameworks, algorithms, practical toolboxes, and experimental validations on robot networks.

Contribution

It introduces tailored distributed optimization frameworks for cooperative robotics, reviews existing toolboxes, and demonstrates their application through simulations and real experiments.

Findings

Distributed optimization frameworks effectively address multi-robot coordination tasks.

State-of-the-art toolboxes enable implementation of distributed algorithms on real robot networks.

Experimental results validate the practical applicability of the proposed methods.

Abstract

Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection, and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss its implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots.