Caking Icing Robot

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.

Click here to view the full website for this project

Why Cake Icing? 

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. 

What improvements would we make if we had additional time? 

With additional time, the system could be made more robust and accurate. The transform from the camera pixels to robot coordinates requires manually measuring the distance from the robot base link to the camera every time it is placed, a process that results int he need for tuning and offsets. A more robust transform could mitigate this. A mounting system that places the camera in the exact same spot each every time would result in more consistency. Another approach could involve AR tags located directly on the cakes.

For the design trajectories, the code could be made more modular, such as using static_transform.py rather than coding the transform in shape_detector.py and utilizing functions for each set of waypoints.

The trajectories could be improved to create cleaner designs. Currently, the velocity of the end effector changes, accelerating and decelerating point-to-point. This could be improved by adjusting the maximum velocity. Alternatively, a different approach to computing trajectories could be used, allowing the way-points to be incorporated into one trajectory instead of several.

At the end of each trajectory, the robot arm blocks the camera, making it impossible to detect subsequent shapes on the table. This could be resolved by changing the placement of the camera, or ensuring the arm is out of view at the end of each design trajectory.

For the version of the project shown on demo day. knowing the height of each cake and the distance relative to the base link, the height of the end effector could be hard-coded. However, cakes come in many shapes, sizes, and heights. For a more versatile implementation, depth sensing was explored. A topic was created to report the distance of the cake from the camera, establishing proof of concept. With more time, this topic could be implemented to apply icing to cakes of different heights.

While there is room for improvement, the design effectively senses the cakes and deposits icing, resulting in various patterns.