Humanoid Robot With Vision Recognition Control System
This work addresses the problem of improving humanoid robot control and perception for robotics applications, but it appears incremental as it applies an existing ellipse detection method to a new robotic context.
The paper tackled controlling humanoid robots by enabling them to execute complex actions based on motion primitives and enhancing their understanding of the external world through vision recognition, specifically by detecting ellipses in real-world images using the Randomized Hough Transform with Result Clustering, achieving detection through preprocessing steps like noise reduction and edge detection.
This paper presents a solution to controlling humanoid robotic systems. The robot can be programmed to execute certain complex actions based on basic motion primitives. The humanoid robot is programmed using a PC. The software running on the PC can obtain at any given moment information about the state of the robot, or it can program the robot to execute a different action, providing the possibility of implementing a complex behavior. We want to provide the robotic system the ability to understand more on the external real world. In this paper we describe a method for detecting ellipses in real world images using the Randomized Hough Transform with Result Clustering. Real world images are preprocessed, noise reduction, greyscale transform, edge detection and finaly binarization in order to be processed by the actual ellipse detector. After all the ellipses are detected a post processing phase clusters the results.