It has been shown that modulating the saliency of a dense amount of information presented as icons on a map-based interface can reduce cognitive workload and improve user performance. Further, first response teams, particularly those responding to complex events, such as Chemical, Biological, Radiological, Nuclear and Explosive device incidents will incorporate robots into their future teams to assist human team members and collect additional information. The deployment of such robots will require a human team member to supervise and task the various robots associated with the team. As the complexity of an incident increases and the number of responders with different specialties increases, for example Police, Emergency Medical Services, and Hazardous Materials, it will be harder to track the robots associated with a particular team, especially by the human team member responsible for the robots. A new algorithm, the Robot Visualization Algorithm, was developed to improve the saliency of robots for which the human team operator (e.g., Emergency Medical Services) is responsible, while generally minimizing the saliency of the robots from other teams (e.g., Police and Hazardous Materials) that are not relevant to the team operator. The presented Robot Visualization Algorithm makes the other teams’ robots more salient if their activities will impact the operator’s team. The within-subjects evaluation determined that the Robot Visualization Algorithm allowed operators to have a better awareness and lower cognitive workload than a base visualization condition. A number of proposed algorithm refinements are also discussed.