RBTx 2024 AI Module Preparation

 

 

 

I had the incredible opportunity to be in Petrosains KLCC in a a pre-workshop focusing on developing a learning module for an AI line tracer. Hosted by a team of experts (Dr Fauzan and Aein) in robotics and AI technology, the workshop deals with the innovative process of retrofitting an existing Arduino robot with a camera module to enable advanced image processing capabilities.

The centerpiece of the workshop was the integration of a camera module, specifically the ESP Cam, into the Arduino robot. This integration was facilitated through a serial connection, utilizing a soft serial approach to convert digital pins into serial inputs. Before attaching the ESP Cam to the Arduino, the camera module underwent initial coding to capture images using the AIthinker ESP camera module. These captured images were then fed into an AI image processing platform called Edge Impulse (https://edgeimpulse.com/) , where the magic truly began.

The task at hand was to train the system to detect specific images, namely images representing wind, water, and sun. This process, known as clustering, involved training the Edge Impulse platform with the collected images. Edge Impulse, as one of the available web AI platforms, utilizes sophisticated algorithms to process and classify images. Once the images were trained, Edge Impulse generated an Arduino library with AI image classification capabilities, enabling the Arduino robot to recognize and respond to the detected images.

The integration of the AI image processing module into the Arduino robot was a meticulous process. Due to the limited number of pins on the Arduino Nano, a soft serial approach was employed to establish communication between the ESP Cam and the Arduino. This involved coding two digital pins to serve as a transmit (TX) and receive (RX) interface for the serial connection.

Using a block programming approach known as Tinkercode, the Arduino robot was programmed to follow a line track while simultaneously activating the camera to “see” images. Additionally, the gripper mechanism on the robot was coded to release or block whenever the right image was detected, adding an extra layer of functionality to the system.

The workshop – master class – brought another perspective of  robotics education, showcasing how AI image processing can be seamlessly integrated into Arduino-based systems. With the ability to detect and respond to visual stimuli, Arduino robots equipped with AI capabilities hold immense potential in various applications, from automated manufacturing to environmental monitoring. This serve as a perfect playground to nurture interest and skills in digital making skillsets =).

Nurul – May 7th, 2024

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Launching Ceremony of RBTX 2024 Challenge

The anticipation is over as the 6th year of the RBTX 2024 Challenge is officially launched, marking yet another exciting chapter in the robotics competition in Malaysia. With three distinctive tracks – Line Tracing, Sumo, and Innovation – this year’s challenge promises to be a platform for creativity, collaboration, and technological advancement.

For years, being part of the RBTX community has been nothing short of a privilege. The open concept, which allows any robot to participate, stands as a strong commitment of democratizing robotics. This inclusivity not only fosters diversity in ideas and approaches but also empowers aspiring innovators from all backgrounds to showcase their talents on a prestigious platform.

As a participant – advisor, or enthusiast, the journey through RBTX has always been enriching. Interacting with fellow advisors, science communicators, and the esteemed team at Petrosains has been an invaluable learning experience. I am truly humbled by this opportunity. Each encounter has broadened horizons, sparked creativity, and instilled a deeper appreciation for the transformative power of robotics. Through this journey, witnessing the impact of robotics education on aspiring engineers keeps me inspired in my passion for engineering education. It reinforces my belief in the importance of hands-on learning experiences and mentorship in shaping the next generation of innovators in the field.

This year, the addition of AI to the Line Tracing track adds a new dimension of challenge and opportunity. Participants are now tasked with leveraging artificial intelligence to identify objects, plot the shortest route, and accurately place objects along the path. This innovative twist not only tests technical prowess but also encourages participants to explore the boundless possibilities of AI in robotics applications.

The positive impact of the RBTX Challenge extends far beyond the competition arena, particularly within the UMP STEM Lab. By embracing innovation and fostering a culture of exploration, RBTX serves as a catalyst for growth and development. Through hands-on participation and mentorship opportunities, students at UMP are equipped with the skills, knowledge, and confidence to tackle real-world challenges in STEM fields.

As we embark on this new chapter of the RBTX Challenge, let us celebrate the spirit of innovation, collaboration, and inclusivity that defines this remarkable journey. Together, we will continue to push the boundaries of what is possible in robotics and inspire the next generation of STEM leaders.

Nurul – May 6th 2024

 

 

 

 

 

 

 

 

 

 

BTE1522 – Innovation (Python) – Week 9 – BME 280

Exploring Temperature & Humidity Sensing with Python

In Week 9 of our BTE 1522 Innovation (Python) class, we explored temperature and humidity sensing using Python programming. Let’s recap the key activities and learning outcomes from this week’s session:

Activity 5 – Temperature & Humidity Sensor

We learned about working with the I2C communication protocol, which is commonly used for connecting and communicating with external sensors.
Reading data from external sensors and interpreting the sensor data were the main coding concepts covered in this activity.

Level up Activities

In the Level Up challenge for Week 9, students were tasked with building upon their knowledge from previous labs and enhancing their Python programs to incorporate additional features and functionalities.

1. Completed Lab 4 with BME 280 Sensor

Students revisited Lab 4, which involved reading ambient temperature, pressure, and humidity using the BME 280 sensor. This sensor is commonly used for environmental sensing applications and provides accurate measurements of these parameters.

2. Modified Codes to Incorporate Enhancements

  • Displayed Sensor Readings with Units: Students modified their Python codes to display temperature, pressure, and humidity readings with appropriate units. This enhancement ensured that the data presented to users was clear, informative, and easy to interpret.
  • Captured Data Every 6 Seconds: To enhance the data acquisition process, students adjusted their programs to capture sensor data at regular intervals of 6 seconds. This modification allowed for more frequent updates of sensor readings, enabling users to track changes in environmental conditions over time.
    Implemented Red LED Blinking for Temperature Readings: As a visual indicator of sensor activity, students incorporated red LED blinking whenever temperature readings were taken. This feature provided immediate feedback to users, indicating when temperature data was being sampled by the sensor.

3. Level Up Challenge: Displayed Data on an OLED Screen

  • In the ultimate level up challenge, students were tasked with integrating an OLED (Organic Light Emitting Diode) screen into their projects to display sensor data in real-time. OLED screens offer advantages such as high contrast, wide viewing angles, and low power consumption, making them ideal for displaying text and graphics.
  • By successfully implementing this feature, students elevated the functionality of their Python programs to a new level. Displaying sensor data on an OLED screen provided a visually appealing and intuitive way for users to monitor environmental conditions and interact with the system.

The level up activities encouraged students to innovate and enhance their Python programs beyond the basic requirements. By incorporating OLED display integration, students elevated their projects to a new level of sophistication. OLED displays provide a visually appealing way to present sensor data in real-time, offering clear and concise information to users. Through this enhancement, students not only demonstrated their mastery of sensor data acquisition and interpretation but also showcased their creativity in user interface design and data visualization. Overall, the level up activities served as a platform for students to explore advanced concepts and apply innovative solutions to real-world challenges, fostering a spirit of creativity and experimentation in their Python projects.