Autonomous Strike UAVs for Counterterrorism Missions: Challenges and Preliminary Solutions
This addresses the need for cost-effective and risk-reducing tools in modern warfare, but it is incremental as it builds on existing technologies like ledger and machine learning without introducing a new paradigm.
The research tackled the problem of implementing autonomous UAVs for strike missions in counterterrorism by analyzing challenges and proposing preliminary solutions, including deriving analytical expressions for mission success probability and describing a machine learning model for training.
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.