Autonomous Surveillance Drone System for Property Monitoring: Technical Specification and Implementation

Executive Summary

This report outlines the technical feasibility and implementation strategy for an autonomous drone surveillance system using the Holybro QAV250 platform, ArduPilot firmware, and a Python/TypeScript server stack. The system integrates Bluetooth-enabled trail cameras with PIR sensors to trigger drone investigations across a 4-acre wooded property. Analysis confirms the viability of this architecture, with minor modifications required for Bluetooth beacon range optimization and fail-safe mission planning.

System Architecture

1. Hardware Components

1.1 Core Drone Platform (Holybro QAV250)

The QAV250's carbon fiber frame (250mm wheelbase) and Pixhawk 6C Mini flight controller¹² provide:

Autonomy Capabilities: Native support for ArduPilot's mission planner with waypoint navigation¹³
Sensor Integration: M10 GPS (3D fix within 2m accuracy) and telemetry radios (915MHz) for server communication¹⁴
Payload Capacity: 500g maximum payload (sufficient for additional compute modules)¹

1.2 Trail Camera Network

Existing cameras require Bluetooth Low Energy (BLE) beacons with:

Trigger Mechanism: PIR sensors activate BLE advertisements (UUID: FF00/FF01 per reverse-engineered protocol⁵)
Network Bridging: ESP32 modules convert BLE signals to WiFi/HTTP POST requests to the central server⁵⁶

1.3 Ground Control Station (GCS)

Raspberry Pi 4 running Python/DroneKit for MAVLink communication⁷⁸
Next.js frontend for mission monitoring and manual override⁶

2. Software Stack

2.1 ArduPilot Configuration

# Mission parameters for wooded area navigation (ardupilot.param)
WP_RADIUS = 3.0  # Reduced waypoint acceptance radius
MIS_OPTIONS = 1   # Continue mission after signal loss
FS_GCS_ENABLE = 0 # Disable GCS failsafe to prevent auto-RTL

Key firmware modifications:

Terrain Following: Use TERRAIN_FOLLOW mode with 10m altitude hold⁹
Obstacle Avoidance: Lidar Lite v3 via I2C for tree canopy detection¹⁰⁴

2.2 Python Control Server

from dronekit import connect, VehicleMode
import requests

def trigger_mission(camera_id):
    vehicle = connect('udpin:0.0.0.0:14550', wait_ready=True)
    mission = generate_path(camera_id) 
    vehicle.commands.clear()
    vehicle.commands.add(mission)
    vehicle.commands.upload()
    vehicle.mode = VehicleMode("AUTO")
    
def generate_path(camera_id):
    # Fetch camera GPS coordinates from database
    cam_loc = db.execute(f"SELECT lat,lon FROM cameras WHERE id={camera_id}")
    return [
        Command(0,0,0, mavutil.mavlink.MAV_FRAME_GLOBAL_RELATIVE_ALT,
                mavutil.mavlink.MAV_CMD_NAV_WAYPOINT, 0,0, 0,0,0,0, *cam_loc,10),
        Command(0,0,0, mavutil.mavlink.MAV_FRAME_GLOBAL_RELATIVE_ALT,
                mavutil.mavlink.MAV_CMD_NAV_RETURN_TO_LAUNCH,0,0,0,0,0,0,0,0,0)
    ]

2.3 Next.js Integration Layer

// pages/api/trigger.ts
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
  const { cameraId } = req.body;
  const response = await fetch('http://python-server:5000/launch', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ camera_id: cameraId })
  });
  res.status(200).json({ status: await response.text() });
}

Implementation Phases

Phase 1: Drone Baseline Configuration

Assemble QAV250 with AM32 ESCs for silent operation¹⁰⁴
Flash ArduPilot Copter 4.3+ with custom parameters³
Validate waypoint navigation using simulated PIR triggers¹¹

Phase 2: Camera Network Integration

Modify trail cameras to broadcast BLE BT_KEY_ON on PIR activation⁵
Deploy ESP32 bridges converting BLE → WiFi (TTGO T-Beam modules)
Implement HTTPS endpoint for mission requests:

POST /trigger 
{ "camera_id": 5, "coordinates": [47.123,-122.123] }

Phase 3: Server Infrastructure

Configure DroneKit-Python with MAVProxy middleware⁷¹²
Develop mission scheduler with collision-aware path planning:

\text{PathCost} = \sum_{i=1}^n \left( \frac{1}{\text{TreeDensity}_i} \times \text{Distance}_i \right)

Build Next.js dashboard showing real-time drone position and camera status⁸⁶

Technical Challenges and Mitigations

1. Bluetooth Range Limitations

Issue: BLE's 100m range insufficient for 4-acre coverage⁵
Solution: Mesh network using ESP32 repeaters (3 nodes minimum)

2. GPS Denied Navigation

Risk: Tree canopy obstructs M10 GPS signals¹
Mitigation: Complementary filter fusing optical flow (PX4FLOW) and barometer data⁹

3. Weatherproofing

Modification: 3D-printed conformal coating for ESC/RPi components⁴

4. Battery Life

Optimization: 4S 3000mAh LiPo with dynamic power management:

t_{\text{flight}} = \frac{3000\text{mAh} \times 0.8}{1950\text{kV} \times 4 \times 0.2\text{A}} \approx 12.3 \text{ minutes}

Conclusion

This architecture provides a robust solution for autonomous property surveillance, leveraging the QAV250's proven ArduPilot integration¹¹³ and modern web frameworks for system control. With an estimated development timeline of 6-8 weeks and component costs under $1,500, the system meets all functional requirements while allowing future expansion (e.g., AI-based intruder detection via OpenCV). Field testing should prioritize fail-over scenarios where multiple cameras trigger simultaneous drone deployments.

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Autonomous Surveillance Drone System for Property Monitoring