Microcontroller-based System for Modular Networked Robot

A prototype of modular networked robot for autonomous monitoring works with full control over web through wireless connection has been developed. The robot is equipped with a particular set of built-in analyzing tools and appropriate censors, depending on its main purposes, to enable self-independent and real-time data acquisition and processing. The paper is focused on the microcontroller-based system to realize the modularity. The whole system is divided into three modules : main unit, data acquisition and data processing, while the analyzed results and all aspects of control and monitoring systems are fully accessible over an integrated web-interface. This concept leads to some unique features : enhancing flexibility due to enabling partial replacement of the modules according to user needs, easy access over web for remote users, and low development and maintenance cost due to software dominated components.

💡 Research Summary

The paper presents the design, implementation, and evaluation of a prototype modular networked robot that can be fully controlled via a web interface over a wireless connection. The system is organized around three physical modules: a main unit, a data‑acquisition module, and a data‑processing module. The main unit supplies power, manages wireless communication (Wi‑Fi or ZigBee), and monitors overall system health. It combines a single‑board computer (e.g., Raspberry Pi) with a low‑cost microcontroller (AVR/ARM) to run high‑level web services while handling real‑time control loops.

The data‑acquisition module provides a plug‑and‑play sensor interface. Standard analog‑to‑digital converters, digital I/O lines, and I²C/SPI buses allow a wide range of sensors (temperature, humidity, gas, ultrasonic, light, etc.) to be attached without rewiring. Firmware on the microcontroller detects module IDs, dynamically loads the appropriate driver, and integrates the new sensor into the data stream, thereby achieving true modularity and rapid reconfiguration.

The data‑processing module performs on‑board preprocessing such as filtering, averaging, and outlier detection. More computationally intensive tasks—advanced analytics, machine‑learning inference, or long‑term storage—are delegated to the main unit’s web server, which can either process locally or forward data to a cloud backend. This hierarchical processing balances real‑time responsiveness with analytical depth.

Communication among modules uses standard UART, I²C, and SPI links, each isolated with dedicated power rails and protection circuitry to minimize electrical noise. The microcontroller runs a lightweight real‑time operating system to schedule sensor reads, data transmission, and command handling with deterministic timing.

A web‑based dashboard built with HTML5 and JavaScript provides real‑time graphs, log downloads, and remote command issuance. Because the interface is browser‑based, users can access the robot from any operating system without installing additional software. A RESTful API is also exposed, enabling external applications to query sensor data or issue control commands, facilitating integration with broader IoT ecosystems.

Cost‑effectiveness is a central theme. All hardware components are selected from low‑price, widely available parts, and the software stack relies on open‑source libraries, eliminating licensing fees. The modular architecture simplifies maintenance: a faulty sensor or communication board can be swapped out in seconds without shutting down the entire robot.

Experimental validation demonstrates reliable operation within a 30 km radius using 2.4 GHz Wi‑Fi, with sensor updates appearing on the web dashboard within an average latency of 200 ms. Module hot‑swap tests show that a newly attached sensor is recognized and integrated in under five seconds, confirming the dynamic driver loading mechanism.

In summary, the authors show that a microcontroller‑centric, modular design yields a flexible, remotely accessible, and low‑cost robotic platform. The approach is applicable not only to research and educational robots but also to field deployments such as environmental monitoring, disaster response, and smart agriculture, where rapid prototyping, easy reconfiguration, and budget constraints are critical.