# Networks of UAVs of Low-Complexity for Time-Critical Localization

arXiv: https://arxiv.org/abs/2108.13181 · HTML: https://mlsanity.com/paper/2108.13181/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2208.12328/) (2022)
- [A Cognitive Framework for Autonomous Agents: Toward Human-Inspired Design](https://mlsanity.com/paper/2601.16648/) (2026)

## Abstract (arXiv)

Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization performance (e.g., tracking the state of a malicious user). Since there is the need to preserve low UAV complexity while tackling the challenging goals of missions in effective ways, one key aspect is the UAV intelligence (UAV-I). The UAV's intelligence includes the UAV's capability to process information and to make decisions, e.g., to decide where to sense and whether to delegate some tasks to other network entities. In this paper, we provide an overview of possible solutions for the design of UAVs of low complexity, showing some of the needs of the UAVs for running efficient localization operations, performed either as a team or individually. Further, we focus on different network configurations, which possibly include assistance with edge computing. We also discuss open problems and future perspectives for these settings.