Rescue Robot Requirement

Updated 4 Oct 2026

Critical Mission Tasks for Vision System

PRIMARY OBJECTIVES (Must Have)

Victim Detection & Classification
- Light injury victim (>50% visible)
- Medium injury victim (<50% visible)
- Critical victim (<10% visible - needs immediate help)
Coordinate Reporting
- Report exact victim locations back to HQ
- Map coordinates within the warehouse layout
- Real-time position tracking
Environmental Navigation
- Navigate in dark/dimly lit areas
- Detect obstacles and rough terrain
- Identify danger zones (gas leaks, unstable areas)

SECONDARY OBJECTIVES (Nice to Have)

Assistance Capabilities
- Guide victims to safety if mobile
- Clear small obstacles
- Provide emergency communication

Technical Requirements from Mission

Physical Constraints

Robot size: <40cm W x 40cm H entrance
Remote operation (no direct line of sight)
Wireless camera communication required
Must work in debris-filled environment

Environmental Challenges

Dark zones - requires low-light vision
Rubble terrain - camera must handle vibration/movement
Gas danger zones - time-limited operation
Narrow passages - wide-angle vision needed

Performance Requirements

Real-time victim detection and reporting
Coordinate mapping and transmission
Reliable operation in challenging conditions

Next Development Steps (Prioritized)

PHASE 2A: Enhanced Detection (Week 3-4)

Step 1: Victim Classification Algorithm

Replace simple color detection with victim visibility assessment:

def classify_victim_urgency(cv_image, detected_regions):
    """
    Classify victims by visibility percentage
    - >50% visible = Light injury (green priority)
    - <50% visible = Medium injury (yellow priority) 
    - <10% visible = Critical (red priority - immediate help needed)
    """
    for region in detected_regions:
        visibility_percent = calculate_visibility(region)
        
        if visibility_percent > 50:
            return "LIGHT_INJURY", "GREEN"
        elif visibility_percent > 10:
            return "MEDIUM_INJURY", "YELLOW"  
        else:
            return "CRITICAL", "RED"  # Immediate help needed!

Step 2: Coordinate Mapping System

Add precise location reporting:

def report_victim_coordinates(detection_result, robot_position):
    """
    Convert pixel coordinates to real-world warehouse coordinates
    Report back to HQ for rescue planning
    """
    warehouse_coords = pixel_to_warehouse_coords(detection_result.position)
    
    victim_report = {
        "victim_id": generate_victim_id(),
        "coordinates": warehouse_coords,
        "urgency": detection_result.urgency_level,
        "visibility": detection_result.visibility_percent,
        "timestamp": current_time(),
        "robot_position": robot_position
    }
    
    publish_to_hq(victim_report)

Step 3: Low-Light Enhancement

Improve vision for dark zones:

def enhance_low_light_detection(cv_image):
    """
    Enhance images for dark warehouse conditions
    """
    # Histogram equalization for better contrast
    enhanced = cv2.equalizeHist(cv_image)
    
    # Gamma correction for brightness
    gamma_corrected = adjust_gamma(enhanced, gamma=1.5)
    
    # Noise reduction for better detection
    denoised = cv2.bilateralFilter(gamma_corrected, 9, 75, 75)
    
    return denoised

PHASE 2B: Mission Simulation (Week 4)

Enhanced Camera Simulator

Update your simulator to match mission scenarios:

def create_mission_scenarios():
    """
    Simulate the actual warehouse rescue scenarios
    """
    scenarios = [
        "empty_warehouse_section",
        "light_injury_victim_visible",      # >50% visible
        "medium_injury_victim_partial",     # <50% visible  
        "critical_victim_buried",           # <10% visible
        "dark_zone_navigation",
        "gas_danger_zone",
        "rubble_obstacle_course"
    ]
    return scenarios

PHASE 3: Hardware Integration (Week 5-6)

Pi Camera Setup for Real Conditions

Configure for low-light performance
Add LED lighting for dark zones
Implement wireless streaming to base station

Connect vision system to robot movement
Implement coordinate-based navigation
Add obstacle avoidance

Your Specific Next Actions

This Week (Week 3):

Modify Your Vision Node - Add victim classification:

# Update your vision_node.py with victim urgency detection
# Test with enhanced camera simulator

Add Coordinate System - Implement warehouse mapping:

# Create coordinate conversion functions
# Add HQ reporting capabilities

Test Low-Light Detection - Handle dark zones:

# Add image enhancement algorithms
# Test with simulated dark scenarios

Week 4 Goals:

Complete mission-specific detection algorithms
Test all victim classification scenarios
Validate coordinate reporting accuracy
Prepare for hardware transition

Mission Success Criteria

Minimum Viable Product (Competition Ready):

✅ Detect and classify 3 victim types ✅ Report coordinates accurately ✅ Operate in low-light conditions ✅ Real-time communication with HQ

Stretch Goals:

🎯 Obstacle detection and avoidance 🎯 Autonomous navigation to victims 🎯 Emergency assistance capabilities 🎯 Multi-sensor fusion (camera + LiDAR)

Linked from

ROS2