Mars rover navigation system

Rover Navigation Optimization System (R-NOS) 🛰

The Rover Navigation Optimization System is a project by Tejas Gupta and Ojas Gupta developed under the guidance of Prof. Bindu Garg, HOD, CSE & CSBS, Bharati Vidyapeeth University, College of Engineering, Pune. It is a scaled-down prototype designed to explore real-time environmental monitoring and data-driven decision-making. The project focuses on collecting temperature and humidity readings to analyze climate conditions and detect anomalies that could impact autonomous navigation.

Table of Contents

Research Hypothesis

A scaled-down Rover prototype can effectively collect temperature and humidity data, demonstrating how real-time environmental monitoring can be used for autonomous navigation, climate analysis, and anomaly detection.

By analyzing the collected data, we aim to identify trends, evaluate sensor accuracy, and explore potential improvements in robotic exploration. This includes assessing response time, consistency, and anomalies caused by external factors like human interference or sudden environmental changes.

Objective

• To explore potential improvements in robotic exploration through data-driven insights.
• Collect and transmit thermal data wirelessly to a computer.
• Detect obstacles using ultrasonic sensors.
• Indicate path changes via buzzer and LED.
• Safeguard navigation based on temperature and obstacles.
• Log and analyze data on a laptop using ThingSpeak.

System Architecture & Control Loop

flowchart TD
    subgraph Onboard Sensors
        DHT22[DHT22 Temp & Humidity Sensor]
        Ultrasonic[HC-SR04 Distance Sensor]
    end

    subgraph ESP32 Controller Loop
        DHT22 --> DataRead[Data Sampling Engine]
        Ultrasonic --> DataRead
        DataRead --> ObstacleCheck{Obstacle < 15cm?}
        DataRead --> TempCheck{Temp Spike > threshold?}
    end

    subgraph Navigation & Indicators
        ObstacleCheck -- Yes --> Alert[Buzzer & Warning LED ON]
        TempCheck -- Yes --> Alert
        ObstacleCheck -- No --> Clear[Normal Trajectory]
        TempCheck -- No --> Clear
    end

    subgraph Telemetry Pipeline
        DataRead --> WiFi[ESP32 Wi-Fi Module]
        WiFi --> ThingSpeak[ThingSpeak Cloud API]
        ThingSpeak --> Dashboard[ThingSpeak Analytics Dashboard]
    end

Setup & Firmware Guide

Prerequisites

• Arduino IDE with ESP32 board support
• Required Libraries: DHT sensor library, Adafruit Unified Sensor, ThingSpeak

Flashing Firmware (code/mrnos.ino)

1. Open code/mrnos.ino in Arduino IDE.
2. Select Board: ESP32 Dev Module.
3. Update Wi-Fi SSID, Password, and ThingSpeak Write API Key in the script:

   const char* ssid = "YOUR_WIFI_SSID";
   const char* password = "YOUR_WIFI_PASSWORD";
   unsigned long myChannelNumber = 2890599;
   const char * myWriteAPIKey = "YOUR_THINGSPEAK_WRITE_KEY";

4. Connect ESP32 via USB and click Upload.

Components

Circuit

Project Snapshots

Environmental Sensor Readings from Rover Prototype

Dataset

What the Data Shows

This dataset contains 5,400 timestamped temperature and humidity readings collected over a period of 3 hours and logged every 2 seconds by the rover’s onboard DHT22 sensor. The data highlights:

• Gradual fluctuations in environmental conditions.
• Notable temperature spikes (~10°C) introduced using a lighter to test sensor response.
• Stable humidity levels with minor deviations due to air circulation or sensor drift.

Notable Findings

• Controlled Temperature Spikes: Short bursts of heat resulted in clear temperature increases (~10°C), demonstrating the sensor's ability to detect and log transient changes.
• Humidity Stability: Humidity levels remained within a narrow range, confirming minimal impact from applied temperature fluctuations.
• Gradual Environmental Variations: Small temperature and humidity shifts were observed, likely due to ambient conditions and ventilation effects.

How the Data Was Gathered

• Sensor Used: DHT22 (for temperature & humidity).
• Data Collection Frequency: Logged every few seconds.
• Controlled Testing: Heat spikes added using a lighter to simulate external interference.
• Data Transmission: Logged in real-time via wireless communication to a laptop.

Data Visualised on ThingSpeak

How to Interpret and Use the Data

• Identify Trends: Observe temperature and humidity variations over time.
• Detect Anomalies: Locate sharp temperature spikes (~10°C increases) caused by external heating.
• Compare Sensor Performance: Evaluate how quickly temperature normalizes after a spike.
• Develop Predictive Models: Train machine learning models to predict environmental changes.

Potential Applications

• Autonomous Environment Monitoring: Detecting and responding to environmental anomalies.
• Sensor Calibration & Validation: Testing DHT22 sensor accuracy under different conditions.
• Climate Simulation & Research: Indoor climate modeling & environmental trend analysis.
• Robotics & AI: Training AI for automated responses to climate fluctuations.

License

This project is open-source under the CC BY 4.0.