Cilyx, a developer of custom production equipment for the pharmaceutical industry, needed to automate the depalletization of syringe boxes stored in overlapping crate configurations. The boxes, designed with vertical sides and outward-facing top flaps, frequently interlock within the crate, making it nearly impossible for a robotic arm to remove them in a fixed sequence without risking damage to the contents.
To solve this, Cilyx partnered with Sagacify to develop an AI solution capable of determining a safe extraction sequence based on real-time visual input. The teams co-developed a model tailored to Cilyx’s specific packaging format and deployed a custom labeling environment to support high-precision training data.
Sagacify built an AI model that analyzes 2D images captured by a camera mounted on the robotic arm. The system identifies all visible boxes in the crate, detects which ones have all four corners free of overlap, scores them based on pickability, and transmits precise (x, y, z) coordinates to guide extraction.
The process is executed in sequential steps: initial detection of areas of interest using reduced-resolution images, high-resolution corner detection, and scoring of free boxes. Once a removable box is identified, the robotic arm is instructed to proceed. The operation repeats until the crate is empty. The model runs entirely on the edge, embedded in a compute unit on the robotic arm.
Our first project with Sagacify was a very positive experience! We worked with an experienced team that was able to analyze and understand the case we wanted to solve with AI. Sagacify quickly built a first proof of concept and provided relevant and tangible results which allowed us to move forward intelligently in the development of advanced robotized solutions. We had the opportunity to work with smart and friendly people who were always open to share their knowledge
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