Color Recognition for Rubik's Cube Robot
This addresses color recognition for Rubik's Cube robots, but it is incremental as it builds on existing methods with specific adaptations.
The paper tackled color recognition for a Rubik's Cube robot by proposing offline and online methods, finding that online methods like dynamic weight label propagation and weak label hierarchic propagation were effective, while the offline SB-ELM method was ineffective due to color drifting in continuous change circumstances.
In this paper, we proposed three methods to solve color recognition of Rubik's cube, which includes one offline method and two online methods. Scatter balance \& extreme learning machine (SB-ELM), a offline method, is proposed to illustrate the efficiency of training based method. We also point out the conception of color drifting which indicates offline methods are always ineffectiveness and can not work well in continuous change circumstance. By contrast, dynamic weight label propagation is proposed for labeling blocks color by known center blocks color of Rubik's cube. Furthermore, weak label hierarchic propagation, another online method, is also proposed for unknown all color information but only utilizes weak label of center block in color recognition. We finally design a Rubik's cube robot and construct a dataset to illustrate the efficiency and effectiveness of our online methods and to indicate the ineffectiveness of offline method by color drifting in our dataset.