An agent-based modeling for collective scene criticality assessment in multi-UV systems

In recent years, the role of unmanned vehicles (UVs) is increased in many surveillance applications; they are substituting the humans in many risky activities, especially when cooperative tasks from UV team are required. To this purpose, this paper presents an agent-based framework that models a multi-UV system for surveillance applications. The agents act as wrappers for the different types of UVs, that capture data from the scene (in the area of the UV mission) and then process them, each one according to its own skills and features. The collected and processed data are then shared from the agent team to find a common agreement on the comprehension and criticality assessment of the scenario. The agent paradigm provides a seamless framework for UV interaction, making the different methodologies and technologies, designed for the different UV types, transparent. The proposal shows the agent-based modeling for a multi-UV system, where each agent hides the facilities and features of the UV it wrapped, with the aim of deploying a homogeneous interface to facilitate the collective scenario assessment in terms of critical or alerting issues, detected in the evolving scene.