DREUS: Disaster Response Enhanced UAV Swarms

Project Overview

DREUS is an advanced disaster response framework that leverages deep reinforcement learning (DRL) to enable autonomous UAV swarms for post-disaster surveillance and survivor assistance. The system integrates facial recognition, emotion detection, and federated learning for collaborative swarm intelligence, ensuring efficient navigation and coordination in dynamic and unpredictable disaster environments.

Overview of the DREUS system architecture showing UAV swarm coordination and data flow.

Core Features

Surveillance & Assistance

• Coordinated UAV swarm surveys
• Advanced survivor identification
• Priority-based assistance allocation
• Real-time environment mapping

Swarm Intelligence

• Shared swarm database
• Redundancy prevention
• Centralized data management
• Collaborative decision-making

Visualization of swarm coordination and path planning strategies.

Technical Implementation

Deep Learning Components

• YOLOv8 for object detection
• Facial recognition systems
• Emotion detection algorithms
• Federated learning integration

Performance Metrics

Our system demonstrates impressive capabilities:

• High accuracy in survivor detection
• Efficient path planning
• Real-time decision making
• Privacy-preserving data sharing

Average detection rates across different model configurations.

Inference Analysis

The system's inference capabilities show:

• Fast processing times
• High confidence scores
• Reliable object tracking
• Efficient resource utilization

Analysis of inference times across different models and scenarios.

Model Performance

Our extensive testing revealed:

• Consistent confidence scores
• Reliable detection rates
• Optimal resource usage
• Scalable performance

Confidence score distribution across different detection scenarios.

Future Developments

Planned improvements include:

• Enhanced swarm coordination algorithms
• Advanced privacy preservation techniques
• Improved resource optimization
• Extended sensor integration
• Real-world deployment testing

Project Repository

You can find the full project on GitHub.