Arduino on Everardo Shain

Industrial AGV

Mon, 05 Dec 2022 08:06:25 +0600

Overview

This was a team project in which we upgraded an existing Ackerman vehicle by turning it into an Automated Guided Vehicle. This was achieved by using ROS as framework on a NVIDIA Jetson Xavier computer and with the implementation of navigation concepts such as mapping, path planning and motion control. As a result, we managed to navigate the AGV inside a closed environment by setting a goal position and avoiding collisions.

Robofest 2022 | Robociety

Sat, 26 Mar 2022 08:06:25 +0600

Overview

This project was a Sumo robot for the international competition Robofest 2022, fulfilling the task of pushing three bottles without falling off the table, plus an unknown initial task to be coded during the competition itself. Although my main responsibility was software development, I also contributed to the robot’s design to ensure it met the competition’s weight and size requirements.

This was part of my two-year participation as a member of Robociety, a robotics student association. Through this project, I not only developed hard skills in robot design and programming but also soft skills by mentoring new members.

Tomato Harvesting Robot

Wed, 16 Feb 2022 08:06:25 +0600

Overview

Project in collaboration with Mondragon Unibertsitatea, Tecnológico de Monterrey campuses Puebla and Queretaro. My team and I worked on the End-Effector Subsystem where we designed and built a 3D-printed prototype of a robot gripper capable of collecting tomatoes using as sensors and actuators a limit switch, a potentiometer and a stepper motor. I had the specific task of writing the code of the embedded system that controlled its movement using an Arduino UNO, and we also programmed an UR10e industrial robot to follow a certain trajectory, mounted the gripper and tested the whole system which successfully completed the tasks of collecting a tomato and placing it on a container.

IoT Sensor Data Classification

Fri, 29 Oct 2021 08:06:25 +0600

Overview

This was an IoT project in which I implemented two machine learning algorithms, K-Nearest Neighbors (KNN) and Decision Tree, in Python to classify data collected from an MMA7361 accelerometer and a DHT11 temperature/humidity sensor connected to a NodeMCU ESP32 microcontroller. The sensors were programmed and tested using Arduino IDE, with data transmitted both through wired serial communication and wirelessly via MQTT using Mosquitto and OpenSSL for secure transfer. I organized the workflow on Jupyter Notebook, where I processed the collected data, labeled different scenarios (dark room, sunny room, bathroom, sensor movement types), and split it into training and testing sets. Both algorithms achieved good results, with accuracies above 97%, including perfect classification with the Decision Tree on the accelerometer dataset.