Autonomous Olfactory Canine Robot

Abstract

The Autonomous Olfactory Canine Robot (AOCR) is an advanced electronic nose (e-nose) engineered for trace gas monitoring and environmental odor classification. Its primary purpose is to overcome the severe electrical noise and baseline drift inherent in Metal-Oxide Semiconductor (MOS) gas sensors by utilizing a highly isolated dual-controller architecture. An Arduino Mega 2560 safely manages 5V high-current sensor heaters and precise 16-bit analog-to-digital conversions, while an ESP32 handles 3.3V logic, WiFi synchronization, and environmental compensation. The AOCR successfully mitigates hardware interference, calculates normalized chemical \"Signatures\" from its eight-sensor array, and securely uploads a comprehensive 66-column dataset to a Google Sheets cloud database to facilitate real-time monitoring and future machine learning analysis.

Introduction

The Autonomous Olfactory Canine Robot (AOCR) is an advanced electronic nose system designed for accurate detection and classification of trace gases in the environment. Its main challenge is capturing weak gas signals while avoiding noise and interference from high-current gas sensors.

To solve this, the system uses high-precision 16-bit ADS1115 ADC modules instead of standard 10-bit microcontroller ADCs, significantly improving measurement resolution. It also implements a dual-controller architecture where an Arduino Mega handles real-time sensor control and an ESP32 manages WiFi communication, cloud uploads, and display functions. This separation prevents wireless operations from disrupting sensor accuracy. Additional hardware design choices like isolated power supplies, voltage regulation, filtering capacitors, and RC low-pass filters are used to reduce electrical noise from sensor heaters and relay switching.

The system uses an array of MQ-series gas sensors to detect different gases, along with environmental sensors for temperature, humidity, and light to help correct measurement drift. A controlled airflow system actively draws air into a sensing chamber and later purges it to ensure consistent readings between cycles.

In operation, sensors are activated in a timed sequence and stabilized before data is collected. The readings are processed into a structured dataset that includes raw values, normalized values, environmental factors, and derived features like sensor ratios and deltas. This results in a rich 66-column dataset that is uploaded to Google Sheets in real time.

Conclusion

The Autonomous Olfactory Canine Robot (AOCR) successfully demonstrates that a high-fidelity electronic nose can be built from commodity hardware by addressing the two fundamental problems of resolution and electrical noise through architectural design rather than expensive specialized components. The dual-controller architecture, independent power isolation, hardware-level RC filtering, and staggered warm-up routine collectively eliminate the noise and interference that limit single-controller MOS sensor systems. The structured 66-column Google Sheets dataset, built on normalized values, delta values, and virtual sensor ratios, provides a robust foundation for training supervised machine learning classifiers for gas identification. Future work will focus on implementing a Support Vector Machine or lightweight neural network model for real-time on-device gas classification, moving toward a fully autonomous odor identification platform suitable for environmental monitoring and safety applications.

References

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Copyright

Copyright © 2026 Rohith Natteti, Nagarjuna Reddy Golamaru. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.