Multipurpose Adaptive Mars Rover: A Solar-Powered, IoT-Enabled Autonomous Exploration System with Robotic Arm and Real-Time Environmental Monitoring

Abstract

It explains how traditional manual or rule-based methods are insufficient for processing the huge volume of global news, so the system uses machine learning (TF-IDF features and classifiers like Naive Bayes, SVM, and Logistic Regression) to automatically categorize news into topics such as politics, sports, and technology. It then extends this with a trend analysis module that tracks how news categories change over time using moving averages, rate-of-change metrics, and burst detection techniques. The system is built as an end-to-end pipeline, including data ingestion from APIs, preprocessing of raw text, feature extraction, classification, and visualization through a real-time dashboard. It is evaluated on standard datasets (AG News and BBC News), where the SVM model performs best with about 94% accuracy. The key contribution is integrating both news classification and temporal trend detection into one system, rather than treating them separately. The study also highlights practical applications like media monitoring, event detection, and real-time news analytics, along with the system’s efficiency and suitability for real-world deployment.

Introduction

The text describes the design, development, and testing of a low-cost Mars rover prototype built for academic research to simulate extraterrestrial exploration systems similar to NASA rovers like Curiosity and Perseverance.

The rover is constructed using commercially available components, including an ESP32 microcontroller, tank-track steel chassis, sensors (temperature, gas), ESP32-CAM, and a 3-DOF robotic arm, enabling it to navigate rough terrain, collect environmental data, stream video, and perform basic object manipulation. It is controlled remotely through a web-based user interface with real-time telemetry and camera feedback.

The system is organized into three layers: mechanical structure, sensing and processing, and communication/control. The software uses FreeRTOS-based firmware, Wi-Fi communication, and a browser-based dashboard for interaction.

Experiments show that the rover can handle different terrains (including slopes up to ~22°), provides reliable sensor readings, supports real-time control with low latency (~18 ms), and streams video at usable frame rates. Key challenges like power management, interference, and streaming delays were resolved through hardware improvements and software optimization.

Conclusion

This paper has presented the design, implementation, and evaluation of the Multipurpose Adaptive Mars Rover — a cost-effective, IoT-enabled robotic platform integrating mechanical manipulation, environmental sensing, live video streaming, and real-time wireless control within a compact solar-rechargeable package. The system successfully demonstrated stable terrain navigation, accurate multi-parameter environmental monitoring, reliable low-latency Wi-Fi telemetry, and functional 3-DOF arm operation. The modular hardware architecture and open-source firmware provide a readily extensible foundation for future enhancements. The project contributes a validated, reproducible hardware–software blueprint for academic rover research, enabling educational institutions to engage meaningfully in planetary robotics experimentation without the prohibitive cost of commercial research platforms.

References

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Copyright

Copyright © 2026 Aman Sheikh, Dhiraj Shinde, Suyash Shelar, Prof. Sarika Patil. 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.