2025-03-01 –, Dela Costa Bldg (NGF)
Discover how Python can transform basic electronic components into intelligent robotics systems. This talk demonstrates practical approaches to hardware control using Python, covering everything from GPIO manipulation to complex robotics control systems. Through live demonstrations, we'll explore Python libraries for hardware integration, sensor processing, and motor control, with real-world examples from educational robotics projects. Perfect for developers interested in physical computing and robotics education.
The session will cover three main areas:
Python Hardware Control Architecture
Direct GPIO manipulation techniques
Implementation of common protocols (I2C, SPI, UART)
Real-time sensor data processing
Integration with microcontroller platforms
Robotics Control Systems
Motor and servo control optimization
Sensor fusion implementation
Real-time feedback systems
Custom protocol development
Live Demonstrations
Real-time hardware control
Sensor data acquisition
Interactive robot movement
Error handling and safety systems
Attendees will learn practical techniques for implementing Python in robotics projects, understanding hardware integration, and developing control systems for educational and professional applications.
Notes (for organizers):
Technical requirements:
Power outlets for demonstrations
Table space for hardware setup
HDMI connection for presentation
Strong Wi-Fi connection
Will bring own hardware for demonstrations including:
Raspberry Pi-based robot
Various sensors and motors
Control interfaces
backup hardware set
Intermediate
Category:Hardware/IoT
Workshop Duration:3 hours
Specializing in Python programming and robotics systems, Florante is currently pursuing graduate studies in Computer Engineering with a focus on Data Science at PUP. He develops Python-based solutions for autonomous systems using Arduino and Raspberry Pi and teaches advanced Python programming at PUP Biñan, emphasizing scientific computing and robotics applications.
His research includes machine learning-enabled detection for hybrid AGV-drone systems and YOLOv5 algorithms for autonomous vehicles. As Governor of the MRSP NCR Chapter, he advances mechatronics and robotics education while creating Python frameworks that bridge hardware and software in intelligent automated systems