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There has been growing interest in the use of multi-robot systems in varioustasks and scenarios. The main attractiveness of such systems is theirflexibility, robustness, and scalability. An often overlooked yet promisingfeature is system modularity, which offers the possibility to harness agentspecialization, while also enabling system-level upgrades. However, alteringthe agents' capacities can change the exploration-exploitation balance requiredto maximize the system's performance. Here, we study the effect of a swarm'sheterogeneity on its exploration-exploitation balance while tracking multiplefast-moving evasive targets under the Cooperative Multi-Robot Observation ofMultiple Moving Targets framework. To this end, we use a decentralized searchand tracking strategy with adjustable levels of exploration and exploitation.By indirectly tuning the balance, we first confirm the presence of an optimalbalance between these two key competing actions. Next, by substituting slowermoving agents with faster ones, we show that the system exhibits a performanceimprovement without any modifications to the original strategy. In addition,owing to the additional amount of exploitation carried out by the fasteragents, we demonstrate that a heterogeneous system's performance can be furtherimproved by reducing an agent's level of connectivity, to favor the conduct ofexploratory actions. Furthermore, in studying the influence of the density ofswarming agents, we show that the addition of faster agents can counterbalancea reduction in the overall number of agents while maintaining the level oftracking performance. Finally, we explore the challenges of usingdifferentiated strategies to take advantage of the heterogeneous nature of theswarm.

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