Hildmann, Hanno

Karvonen, Hannu

Lind, Timo

Saffre, Fabrice

English

Machine-based situational awareness is a key element to conscious and intelligent interaction with the complex world we live in, be it for the individual unit, a complex dynamical system, or even complex systems. To create this awareness, the frequent gathering of accurate and real-time intelligence data is required to ensure timely, accurate, and actionable information. Unmanned Aerial Vehicles (UAVs) and other semi-autonomous cyber-physical systems are increasing among the mechanisms and systems employed to assess the state of the world around us and collect intelligence through surveillance and reconnaissance missions. The current state of the art for humanitarian and military operations is still relying on human-controlled flight/asset operations, but with increasingly autonomous systems comes an opportunity to offload this to the devices themselves. In this chapter, we present a principled and expandable methodology for evaluating the relative performance of a collective of autonomous devices in various scenarios. The proposed approach, which is illustrated with drone swarms as an example use case, is expected to develop into a generic tool to inform the deployment of such collectives. It is expected to provide the means to infer key parameter values from problem specifications, known constraints, and objective functions

VTT Research System

Self-Swarming for Multi-Robot Systems Deployed for Situational Awareness

https://cris.vtt.fi/files/52732184/2021_FinDrones_Self_swarming_for_multi_robot_systems.pdf

Self-Swarming for Multi-Robot Systems Deployed for Situational Awareness

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