To handle more complex Lego structures with irregular shapes or varying brick sizes within the same prototype, the proposed system can be extended in several ways:
Adaptive End-of-Arm Tool (EOAT): Develop an EOAT with adjustable components that can accommodate different shapes and sizes of Lego bricks. This adaptive tool can be equipped with sensors to detect the dimensions and orientation of each brick, allowing for precise manipulation.
Advanced Computer Vision: Implement advanced computer vision algorithms to recognize and classify different Lego brick types and configurations. This can enable the robot to adapt its manipulation strategy based on the specific structure it is working with.
Machine Learning for Structure Recognition: Train machine learning models to analyze and understand the structure of complex Lego prototypes. By feeding the system with a diverse dataset of Lego configurations, the robot can learn to handle various irregular shapes and sizes effectively.
Hierarchical Planning: Implement a hierarchical planning system that breaks down the assembly process into smaller, manageable subtasks. This approach can help the robot navigate through complex structures by focusing on one section at a time.
By incorporating these enhancements, the robotic Lego prototyping system can efficiently handle the assembly and disassembly of intricate Lego structures with irregular shapes and varying brick sizes.