Navigation with shadow prices to optimize multi-commodity flow rates

TL;DR

This paper introduces a novel optimization method using shadow prices to strategically position communication agents in multi-agent systems, enhancing information transfer rates tailored to specific network needs.

Contribution

It presents a new convex optimization-based algorithm for optimal placement of communication agents, adaptable to various network performance objectives.

Findings

Algorithm effectively improves network communication rates.

Flexible to different system requirements.

Validated on three practical scenarios.

Abstract

We propose a method for providing communication network infrastructure in autonomous multi-agent teams. In particular, we consider a set of communication agents that are placed alongside regular agents from the system in order to improve the rate of information transfer between the latter. In order to find the optimal positions to place such agents, we define a flexible performance function that adapts to network requirements for different systems. We provide an algorithm based on shadow prices of a related convex optimization problem in order to drive the configuration of the complete system towards a local maximum. We apply our method to three different performance functions associated with three practical scenarios in which we show both the performance of the algorithm and the flexibility it allows for optimizing different network requirements.