UAV Swarm Intelligence in CEMA: Enhancing Urban Communication Line Management

In response to the increasing challenges of Cyber Electromagnetic Activities (CEMA) in urban settings, characterized by dense electromagnetic (EM) signals and rising data traffic, this research introduces an Agent-Based Model (ABM) aimed at prioritizing critical signals. The primary goal of this research is to deploy a Unmanned Aerial Vehicle ...

Obstacle avoidance for quadrotors using reinforcement learning and obstacle-airflow interactions

This research investigates and proposes a new method for obstacle detection and avoidance on quadrotors. One that does not require the addition of any sensors, but relies solely on measurements from the accelerometer and rotor controllers. The detection of obstacles is based on the principle that the airflow around a quadrotor changes when the...

Potential Field Methods for Safe Reinforcement Learning: Exploring Q-Learning and Potential Fields

A Reinforcement Learning (RL) agent learns about its environment through exploration. For most physical applications such as search and rescue UAVs, this exploration must take place with safety in mind. Unregulated exploration, especially at the beginning of a run, will lead to fatal situations such as crashes. One approach to mitigating these...