ASAP: Autonomous Search and Assistance Protocol

Revolutionizing Disaster Response through Intelligent Multi-Agent Systems

The Challenge

In disaster scenarios, every second counts, yet traditional search and rescue operations face critical limitations:

• Limited Situational Awareness: First responders often operate with incomplete information
• Resource Coordination: Difficulty in optimally deploying rescue teams and equipment
• Environmental Hazards: Dangerous conditions that put rescue teams at risk
• Time Sensitivity: Critical delays in identifying and reaching survivors
• Information Overload: Complex data streams that are difficult to process in real-time

Our Solution

ASAP revolutionizes disaster response through an intelligent multi-agent system that combines autonomous drones, ground robots, and advanced data processing to create a comprehensive disaster response platform.

Key Features

1. Multi-Agent Coordination

• Autonomous Drones: Aerial surveillance and thermal imaging
• Ground Robots: Direct assistance and hazard detection
• Dynamic Task Allocation: Intelligent distribution of resources based on priorities
• Fault Tolerance: Automatic redistribution of tasks if an agent fails

2. Real-Time Data Aggregation

• Multiple Data Sources:
  • Thermal imaging for heat signature detection
  • Gas sensor monitoring for hazardous conditions
  • Visual recognition for survivor detection
  • Weather data integration
  • Human reports processing
• Standardized Format: All data normalized for consistent processing
• Continuous Monitoring: Regular updates every 1-5 seconds from critical sensors

3. Intelligent Decision Making

• Central Command System: Processes aggregated data and makes informed decisions
• Priority-Based Task Allocation: Tasks assigned based on urgency and resource availability
• Automated Risk Assessment: Continuous evaluation of environmental hazards
• LLM Integration: Natural language processing for human-readable updates

4. Human-in-the-Loop Interface

• Operator Dashboard: Real-time visualization of all operations
• Manual Override: Ability for human operators to take control when needed
• Status Logs: Live feed of sensor inputs and task statuses
• Interactive Controls: Direct manipulation of system parameters

Impact

Immediate Benefits

1. Faster Response Times: Autonomous agents can begin search operations immediately
2. Enhanced Safety: Reduced risk to human responders in dangerous areas
3. Better Coverage: Multiple agents can search different areas simultaneously
4. Improved Accuracy: Multi-sensor data fusion for better decision making
5. Resource Optimization: Intelligent allocation of available resources

Long-term Value

1. Scalable Architecture: Easy to add new agents and sensors
2. Data-Driven Improvements: System learns from each deployment
3. Cost Reduction: More efficient use of resources
4. Lives Saved: Faster response times lead to better survival rates

Technical Architecture

Core Components

1. Data Aggregation Module

   • Collects and normalizes data from all sources
   • Supports both push and pull-based data collection
   • Handles multiple data formats and frequencies

2. Central Command System

   • Decision-making hub
   • Task generation and prioritization
   • Resource allocation

3. Agent Task Allocation

   • Dynamic task assignment
   • Health monitoring
   • Failure recovery

4. Data Fusion & Feedback

   • Real-time data integration
   • Continuous system optimization
   • Performance monitoring

Future Roadmap

Short-term Goals

• Integration with physical drone systems
• Enhanced machine learning models for better decision making
• Expanded sensor support
• Improved visualization tools

Long-term Vision

• Alerting Organizations: Alerting Hospitals, Rescue teams, First responders and other disaster management organizations in the event of disasters.
• Advanced AI Integration: Deep learning for better situation assessment
• Predictive Analytics: Anticipating disaster scenarios
• Extended Automation: Reduced need for human intervention
• Global Deployment: Standardized system for worldwide use

Getting Started

Please find instructions in the Setup Guide.

Contact

• Lakshmanan Meiyappan | GitHub
• Padmini Udayakumar | GitHub
• Sharat Naik | GitHub
• Priyanka Bhangale | GitHub
• Ashwin Srivatsa | GitHub

Python Env Setup Instructions

1. Create a Python virtual environment:

python3.11 -m venv venv

2. Activate the virtual environment:

source venv/bin/activate  # On Unix/macOS
# or
.\venv\Scripts\activate  # On Windows

3. Install dependencies:

pip install -r requirements.txt