Abstract
Abstract As Unmanned Aircraft Systems (UAS) are becoming ubiquitous, more and more use cases will be relying on multi-UAS systems for mission-oriented applications (e.g., surveillance, reconnaissance, and package delivery). The multi-UAS formation has been shown to play a critical role in the ability of the system to conserve energy and reduce travel time thus, greatly impacting mission success. The need to identify, recognize and create such formations is critical for effective control of multi-UAS platforms, especially in challenging dynamic environmental conditions. In this paper, we propose and describe a framework for off-line identification and categorization of various types of multi-UAS formations based on the relative position of UAS in a two-dimensional space using machine learning techniques. The formation algorithm is trained with simulation trace data of different formations so that it is capable of accurately recognizing one that is materialized in the world. This information is proven crucial to enable formation-based adaptation of multi-UAS in highly dynamic environment thus, contributing to the resilience of the system. A prototype implementation and simulation are currently in development which will illustrate the capabilities of our approach.
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