cs.ROOct 5, 2026

A Taxonomy on Collective Awareness

Authors: Guillermo GP-Lenza, Miguel Fernandez-Cortizas, Martín Molina, Ricardo Sanz, Pascual Campoy

Organizations: Computer Vision and Aerial Robotics Group, Department of Artificial Intelligence, Universidad Polit´ecnica de Madrid, C. de los Ciruelos, Boadilla del Monte, 28660, Madrid, Spain. · Computer Vision and Aerial Robotics Group, Centre for Automation and Robotics (CAR), Universidad Polit´ecnica de Madrid (UPM-CSIC), Calle Jos´e Guti´rrez Abascal 2, Madrid, 28006, Madrid, Spain. · Automation and Robotics Research Group (ARG), Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, L-1359, Luxembourg, Luxembourg. · Autonomous Systems Laboratory, ASLAB, Universidad Polit´ecnica de Madrid (UPM-CSIC), Calle Jos´e Guti´rrez Abascal 2, Madrid, 28006, Madrid, Spain.

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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.

Figures & tables

Mutual Awareness
SourceDefinition
Macmillan et al. (2004)The extent to which team members are informed of other team members’ behaviors
Schuster and Jentsch (2011)The level of understanding participants have of each other’s actions and contexts, which improves efficiency and performance in a simulator.
Rognin and Blanquart (2001)Awareness about each other’s activities and about each other’s awareness.
Schmidt (1998)The perception and understanding of member A on the activity, including intention, status, possible results and influence, of member B.
Villa et al. (2008)If member A could observe member B’s activities while member B could not observe A, then A has a watching awareness. If member A knows member B is observing him/her, but A himself/herself could not watch B, then A has a watched awareness.
Table 1: Definitions of Mutual Awareness, Shared Situational Awareness, and Team Situational Awareness in collective contexts.
Figure 1: UGV A plans a trajectory in its local occupancy grid and sends the cell coordinates to its CA module, which transforms them into coordinates in a common reference frame. The CA module transmits this shared representation to UGV B, whose CA module transforms the Cartesian coordinates into cells in B’s local cost map and updates them accordingly. This coordinate transformation chain enables mutual awareness of planned paths despite heterogeneous internal representations.
Figure 2: A UAV detects a survivor in an image and obtains bounding box coordinates in the image frame. The UAV’s CA module transforms these coordinates into real coordinates in a common reference frame and transmits them to a UGV. The UGV’s CA module transforms the received coordinates into a cell in its local occupancy grid, enabling the UGV to integrate the survivor location into its spatial representation. This transformation chain allows heterogeneous agents to share situational awareness despite different sensing modalities and internal representations.
Figure 3: UAV C detects a safe landing area in an image captured by its camera and obtains bounding box coordinates in the image frame. C’s collective awareness module transforms these coordinates into real Cartesian coordinates in a common reference frame and transmits them to another UAV. The receiving UAV’s CA module transforms the shared coordinates into voxels in its local voxel map, enabling it to integrate the landing zone location into its spatial representation. This shared understanding of a critical environmental feature supports coordinated team operations and mission planning.
Type of AwarenessDistributionObject of Awareness
Mutual AwarenessDistributedTeam member’s intentions, beliefs, goals, and plans.
Shared Situational AwarenessSharedEssential elements for coordinated task execution. The intersection of each individual agent’s awareness requirements.
Team Situational AwarenessMixedIndividualized awareness each agent must maintain to effectively perform their own functions. Overlaps with agents performing the same functions.
Table 2: Categorization of each term related to awareness in multi-agent systems according to the distribution and object of the awareness.
Figure 4: Venn diagram of the objects of mutual awareness, shared situational awareness, and role-specific elements. All of them combined are objects of team situational awareness.
Figure 5: Venn diagram of the objects of each kind of awareness for a system made up of ground and aerial robots operating in a search and rescue scenario. Specific objects of awareness for each role, combined with mutual awareness and shared situational awareness objects, form the whole set of objects of team situational awareness.

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