# Autonomous Strike UAVs for Counterterrorism Missions: Challenges and Preliminary Solutions

arXiv: https://arxiv.org/abs/2403.01022 · HTML: https://mlsanity.com/paper/2403.01022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection](https://mlsanity.com/paper/1906.07064/) (2019)
- [Graph Learning and Its Advancements on Large Language Models: A Holistic Survey](https://mlsanity.com/paper/2212.08966/) (2022)

## Abstract (arXiv)

Unmanned Aircraft Vehicles (UAVs) are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and preliminary solutions for successful implementation of an autonomous UAV mission. Specifically, we identify challenges that have to be overcome and propose possible technical solutions for the challenges identified. We also derive analytical expressions for the success probability of an autonomous UAV mission, and describe a machine learning model to train the UAV.