Adapting the Exploration-Exploitation Balance in Heterogeneous Swarms: Tracking Evasive Targets

TL;DR

This paper investigates how heterogeneity in robot swarms affects the exploration-exploitation balance for tracking evasive targets, demonstrating that faster agents and strategic connectivity adjustments enhance overall performance.

Contribution

It introduces a decentralized strategy to adapt exploration and exploitation in heterogeneous swarms, showing performance gains through agent speed and connectivity modifications.

Findings

Faster agents improve tracking performance without strategy changes.

Reducing agent connectivity enhances exploration in heterogeneous swarms.

Adding faster agents compensates for fewer total agents while maintaining performance.

Abstract

There has been growing interest in the use of multi-robot systems in various tasks and scenarios. The main attractiveness of such systems is their flexibility, robustness, and scalability. An often overlooked yet promising feature is system modularity, which offers the possibility to harness agent specialization, while also enabling system-level upgrades. However, altering the agents' capacities can change the exploration-exploitation balance required to maximize the system's performance. Here, we study the effect of a swarm's heterogeneity on its exploration-exploitation balance while tracking multiple fast-moving evasive targets under the Cooperative Multi-Robot Observation of Multiple Moving Targets framework. To this end, we use a decentralized search and tracking strategy with adjustable levels of exploration and exploitation. By indirectly tuning the balance, we first confirm the presence of an optimal balance between these two key competing actions. Next, by substituting slower moving agents with faster ones, we show that the system exhibits a performance improvement without any modifications to the original strategy. In addition, owing to the additional amount of exploitation carried out by the faster agents, we demonstrate that a heterogeneous system's performance can be further improved by reducing an agent's level of connectivity, to favor the conduct of exploratory actions. Furthermore, in studying the influence of the density of swarming agents, we show that the addition of faster agents can counterbalance a reduction in the overall number of agents while maintaining the level of tracking performance. Finally, we explore the challenges of using differentiated strategies to take advantage of the heterogeneous nature of the swarm.