Optimizing Search and Rescue UAV Connectivity in Challenging Terrain through Multi Q-Learning

TL;DR

This paper presents a multi Q-learning based approach for UAVs to optimize navigation and maintain reliable cellular connectivity in challenging terrains during search and rescue missions, improving autonomy and communication reliability.

Contribution

It introduces a novel multi Q-learning framework with specialized agents for path planning and connectivity maintenance in SAR UAV operations, a first in this context.

Findings

Successful navigation in complex terrains with obstacles

Effective maintenance of cellular connectivity across frequency bands

Enhanced UAV autonomy and communication reliability

Abstract

Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.