PatrolCar Pro - ESP32 First-Year Project Stack

This workspace has been upgraded from a basic remote car demo to a full patrol robotics platform.

What is new

• Modern ESP32 WebServer dashboard with live telemetry
• Patrol mode with obstacle-aware behavior
• API-based control endpoints for remote integration
• Dedicated Sensors folder with calibration and filtering assets
• Raspberry Pi bridge for MQTT monitoring and logging

Workspace structure

Images/
	Remote_Car.jpeg
RemoteCar/
	RemoteCar.ino
Sensors/
	README.md
	calibration_profiles.json
	analysis/
		sensor_fusion.py
RaspberryPi/
	README.md
	requirements.txt
	patrol_bridge.py
	systemd/
		patrol-bridge.service
README.md

ESP32 features in this version

• Soft AP mode network for direct control
• Browser dashboard at root endpoint
• HTTP API:
  • /api/drive?dir=forward|backward|left|right|stop
  • /api/speed?value=80..255
  • /api/patrol?enable=0|1
  • /api/headlight?enable=0|1
  • /api/status
• Telemetry fields:
  • motion
  • speed
  • distance_cm
  • battery_pct
  • rssi_dbm
  • patrol
  • headlights
  • uptime_s

Hardware assumptions

• ESP32 dev board
• L298N motor driver
• DC gear motors (left and right)
• HC-SR04 ultrasonic sensor
• Battery voltage divider to ADC pin
• Optional headlight LED

Build and run

1. Open RemoteCar/RemoteCar.ino in Arduino IDE.
2. Select ESP32 board and serial port.
3. Install ESP32 board package and upload firmware.
4. Connect phone/laptop to Wi-Fi SSID PatrolCarLab.
5. Open browser to http://192.168.4.1.

Raspberry Pi monitoring

1. Enter RaspberryPi.

2. Install dependencies:

   pip install -r requirements.txt

3. Start telemetry bridge:

   python3 patrol_bridge.py --host 192.168.4.1 --mqtt-host localhost

4. Subscribe to MQTT topics for analytics and dashboards.

Some Points Related to Projects

First

• Manual web control

• Basic motor movement

• Single sensor readouts

• Telemetry APIs and filtered measurements

• Event logs and bridge process on Raspberry Pi

• Basic safety responses on obstacle detection

• Sensor fusion with IMU and encoder data

• Camera-assisted patrol decisions

• Mission planning and autonomous route graph

• Research-grade evaluation metrics in report

Notes

• Tune pin mapping in RemoteCar/RemoteCar.ino for your hardware.
• Calibrate battery and ultrasonic profiles in Sensors/calibration_profiles.json.