Problem
Research questions and friction points this paper is trying to address.
Collective Awareness
Multi-Robot Systems
Situational Awareness
Coordination
Taxonomy
A Taxonomy on Collective Awareness
🤖 AI Summary
This study addresses the conceptual ambiguity surrounding collective awareness and the ill-defined coordination requirements in multi-robot systems by proposing a two-dimensional classification framework based on "object" and "distribution" axes. Through the construction of an inclusiveness hierarchy model, this work systematically delineates the hierarchical relationships among mutual, shared, and team situational awareness, explicitly clarifying the cognitive distribution spectrum from individualized to homogenized states while rectifying prevalent terminological confusions in existing literature. The validity of the proposed framework is empirically verified through case analyses of search-and-rescue scenarios involving heterogeneous aerial-ground robot teams. Ultimately, this research establishes a robust conceptual foundation for the design, specification, and evaluation of collective awareness in multi-robot systems.
📝 Abstract
As robotic teams tackle increasingly complex tasks in dynamic and unstructured environments, effective coordination requires agents to maintain accurate, aligned representations of their environment, teammates, and mission state. We argue that Mutual Awareness, Shared Situational Awareness, and Team Situational Awareness --- three concepts widely invoked in the multi-robot systems literature --- are not interchangeable: they form a containment hierarchy in which each type subsumes the previous in scope, and span a distribution spectrum from fully individualized understanding (MA) to fully uniform understanding (SSA), with TSA occupying a mixed position. We establish this through a two-dimensional taxonomy organized along the object of awareness and awareness distribution axes. Treating these terms as synonyms obscures the precise coordination requirements each imposes on a robotic system. A Search and Rescue case study with heterogeneous aerial and ground robots grounds each taxonomy position in concrete coordination requirements. These findings provide a conceptual foundation for principled specification, design, and comparison of collective awareness in multi-robot systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Collective Awareness
Multi-Robot Systems
Taxonomy
Situational Awareness
Coordination
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