Miro's Blog

Reading Instructions from QR Codes and Executing Them Using an Android Application Introduction Recently, I had the opportunity to build a drone prototype controlled by an Android device. The requirements were clear: the drone needed to be small in size and come with an SDK that supported video streaming. After some research, I decided that the Bebop 2 from Parrot was the best choice. Parrot is one of the few companies that offers an open SDK for developers, and they recently released the third version of their SDK. To get started, I worked with the Android application example provided by Parrot. This example covers almost every basic feature: connecting to the drone, moving it around, capturing high-quality photos, and accessing the drone’s media. One of the tasks for this prototype was to enable autonomous landing on a pattern. After researching existing solutions, I found a paper that explained the theory behind autonomous landing. This inspired me to create an Android applicatio

Robotic Arm with Computer Vision - Picking Up the Object Idea The main idea behind this project was to create an environment where a robotic arm can execute various commands based on image analysis of a scene. In this article, I will describe each part of the project in detail. For the first task, I focused on detecting and moving a single object. Environment The setup consists of several components assembled together. I used an old table as the base, repainting it white to provide better contrast with the objects. The robotic arm, which I purchased from eBay, is mounted on the middle of the longer side of the table. The arm has six servo motors, including a rotating base and claws at the other end. The parts are made of aluminum and are quite sturdy. Next, I cut and mounted perforated metal ledges to the corners of the table, securing everything together. I then attached an RGB LED strip to the bottom side of the top part of the structure. Finally, I placed a USB ca

Computer vision exercises with preprocessing Before starting my next project, I decided to work on some computer vision exercises. Each example is based on straightforward image preprocessing techniques. No complex data structures or machine learning are involved. Dice Detection I got this idea while browsing the net and became curious about how challenging it would be to write such a script. Here’s a step-by-step breakdown of the algorithm: Movement Detection : By comparing several frames with thresholds, we can determine if there is any movement in the frame. Adding a small time buffer after the movement stops gives us more accurate information. Removing the Background : Thresholding the grayscale frame helps to remove the background, leaving us with a binary image that highlights the objects. Cropping the Objects : Using contours, we can detect and isolate the objects by cropping them. Detecting Dots : By inverting the image, the dots on the dice become more distinguish