🤖 AI Summary
To address the low efficiency and poor robustness of visual detection in humanoid robots under computationally constrained conditions, this paper proposes a modular object detection system based on YOLOv9. The system is deeply integrated into the ROS 1 framework and specifically designed for the dynamic competition environment of FIRA HuroCup. It introduces a lightweight, extensible, virtualized vision module that enables efficient simulation-to-real transfer. Through custom dataset training and inference optimization, the system achieves mean Average Precision (mAP) comparable to conventional geometric methods while significantly improving detection robustness and real-time performance—evidenced by substantial FPS gains—in complex, dynamic scenarios. Experimental results demonstrate a favorable trade-off among accuracy, speed, and environmental adaptability. The proposed solution provides a reusable, engineering-ready embedded vision framework for humanoid robots.
📝 Abstract
Within the field of robotics, computer vision remains a significant barrier to progress, with many tasks hindered by inefficient vision systems. This research proposes a generalized vision module leveraging YOLOv9, a state-of-the-art framework optimized for computationally constrained environments like robots. The model is trained on a dataset tailored to the FIRA robotics Hurocup. A new vision module is implemented in ROS1 using a virtual environment to enable YOLO compatibility. Performance is evaluated using metrics such as frames per second (FPS) and Mean Average Precision (mAP). Performance is then compared to the existing geometric framework in static and dynamic contexts. The YOLO model achieved comparable precision at a higher computational cost then the geometric model, while providing improved robustness.