Learning Optimal Topology for Ad-hoc Robot Networks

TL;DR

This paper presents a data-driven, ensemble learning approach to predict optimal topologies in ad-hoc robot networks, achieving over 80% accuracy on a 10-robot network, improving efficiency in network configuration.

Contribution

It introduces a novel ensemble learning method that efficiently predicts optimal robot network topologies based on complex criteria, advancing network design automation.

Findings

Achieves over 80% accuracy in topology prediction for 10-robot networks.

Develops an algorithm to generate ground-truth optimal topologies.

Demonstrates the effectiveness of a stacked ensemble model in this task.

Abstract

In this paper, we synthesize a data-driven method to predict the optimal topology of an ad-hoc robot network. This problem is technically a multi-task classification problem. However, we divide it into a class of multi-class classification problems that can be more efficiently solved. For this purpose, we first compose an algorithm to create ground-truth optimal topologies associated with various configurations of a robot network. This algorithm incorporates a complex collection of optimality criteria that our learning model successfully manages to learn. This model is an stacked ensemble whose output is the topology prediction for a particular robot. Each stacked ensemble instance constitutes three low-level estimators whose outputs will be aggregated by a high-level boosting blender. Applying our model to a network of 10 robots displays over 80% accuracy in the prediction of optimal topologies corresponding to various configurations of the cited network.