The landscape of Collective Awareness in multi-robot systems
Miguel Fernandez-Cortizas, David Perez-Saura, Ricardo Sanz, Martin Molina, Pascual Campoy
TL;DR
This paper surveys the landscape of Collective Awareness (CA) in multi-robot systems (MRS). It conducts a qualitative literature review (1992–2024) to map definitions, data-sharing schemes, and architectures that enable CA, and proposes a concrete definition accompanied by a taxonomy of how CA emerges (from environment/robot models to semantic knowledge) across centralized, decentralized, and swarm-like systems. It also identifies open challenges—generality, knowledge uncertainty, risk, fault tolerance, and communication reliability—and argues for formal representations such as ontologies to support robust, scalable CA-enabled MRS deployments. Overall, the work clarifies the landscape of CA, offering a framework for future research to design more resilient and cooperative multi-robot ensembles.
Abstract
The development of collective-aware multi-robot systems is crucial for enhancing the efficiency and robustness of robotic applications in multiple fields. These systems enable collaboration, coordination, and resource sharing among robots, leading to improved scalability, adaptability to dynamic environments, and increased overall system robustness. In this work, we want to provide a brief overview of this research topic and identify open challenges.