STM32-based floor sweeping robot

Abstract

This dissertation presents the design and implementation of an intelligent cleaning robot based on the STM32F103 microcontroller, integrating autonomous navigation, environmental sensing, and IoT connectivity. The system employs dual STM32F103 controllers to generate PWM signals for motion control: one governs four TT motors with PID-based speed regulation and quadrature encoder feedback, while the other drives an SG90 servo for brush head adjustment. Obstacle avoidance is achieved through real-time distance monitoring using HC-SR04 ultrasonic sensors, ensuring collision-free navigation. A modular communication architecture is developed, utilizing ESP01S for Wi-Fi connectivity and USART interfaces to interconnect three STM32 units, enabling remote control via a custom Android app developed with AppInventor. Additional functionalities include environmental monitoring via timer based input capture for temperature/humidity sensing and an I²C-driven OLED interface for real-time parameter visualization. Experimental results validate the system’s robustness in adaptive speed control, obstacle detection accuracy (<3 cm error), and low-latency wireless communication (<150 ms). The work demonstrates a cost-effective, scalable framework for smart home devices, balancing hardware efficiency with software flexibility. Future extensions could incorporate machine learning for path optimization or multi-robot coordination.