Implementação de robô móvel para rastreio de pessoa com visão computacional

Abstract

The development of an autonomous robot capable of tracking people integrates knowledge areas such as computer vision, control systems, and communication protocols. In this con- text, this work aims to develop and implement a mobile robot that performs human tracking in unstructured environments. The robot architecture is based on a high-level processing unit, implemented on a Raspberry Pi, which executes the computer vision algorithm and the control logic; the low-level processing unit, based on the ESP32, interfaces with the power system and controls motor actuation through an H-bridge; finally, the system actuators consist of two direct current motors driven in a differential and independent manner. The computer vision algorithm, developed in Python, processes the image stream from acquisi- tion to the extraction of the required information. Upon acquisition, the images are converted into numerical matrices using the OpenCV and NumPy libraries to feed the YOLO11 (Ultra- lytics) neural network. This network is responsible for detecting people and extracting the parameters of the image bounding box, using the horizontal center coordinate and the height of the bounding box to perform, respectively, orientation correction, keeping the robot aligned with the target, and distance control, stabilizing the robot within a resting range. These values are normalized and undergo stochastic filtering using the Kalman filter, which estimates the variables and reduces detection noise. The estimated variables are then provided to the finite state machine, where the robot’s operational states are defined. The velocities are defined and transmitted via the MQTT communication protocol over a local network; this information coordinates the differential traction system controlled by an H-bridge and direct current motors, applying hysteresis, deadband, and intermittent actuation techniques to mitigate static friction and chattering. The experimental results demonstrate the robot’s ability to maintain target tracking with stable behavior, operating within the required time to follow a person walking through the environment. This result highlights the feasibility of integrating computer vision techniques with embedded control in autonomous mobile robotic systems.