The goal of this project is to design and implement an autonomous cake-decorating robotic system capable of applying icing patterns accurately and consistently without human intervention. The system uses a 6-DOF UR7e robotic arm equipped with a custom syringe-based icing end effector, along with an overhead vision system to detect cake geometry and generate corresponding 2D decoration trajectories. By combining perception, planning, and precise actuation, the robot is able to adapt its motion to different cake sizes and shapes while maintaining smooth and controlled icing flow.

Creating a system that senses and decorates different types of cake requires combining multiple subsystems into a single pipeline, and that complexity is what makes this such an interesting problem to solve. To achieve end-to-end functionality, we needed to solve problems in computer vision (detecting cake boundaries and extracting clean outlines), trajectory planning (generating smooth, continuous 2D paths that respect robot kinematics), and robot control (coordinating arm motion with consistent icing extrusion). Additionally, the system must handle real-world imperfections such as camera noise, alignment errors, and variations in icing behavior, making robustness and calibration important components of the design.

The techniques developed in this project extend beyond cake decoration and can be applied to various real-world robotic tasks involving precise surface interaction. Similar perception-to-motion pipelines can be used in food automation, robotic painting or printing, additive manufacturing, surgical robotics, and automated dispensing systems. More broadly, this project demonstrates how vision-guided robotic arms can adapt motion plans to unknown geometries, a capability that is essential for flexible manufacturing and service robotics.