Graph Neural Network-Based Real-Time 3D Tracking for Micro-Agent Control

Micro-surgical robotic systems are gaining prominence in minimally invasive surgery within the medical field. However, accurately tracking the position of the moving agents at the micro-scale remains a significant challenge, particularly for multi-agent systems operating in cluttered and unknown environments. Traditional image analysis methods can falter when confronted with issues such as mutual and obstacle occlusion, especially in dynamic and unstructured scenarios. In order to address this issue, this study introduces a graph-based multi-agent 3D tracking algorithm for a micro-agent control system. This algorithm integrates image information with the control inputs used to navigate the micro agents. We combine the power of Convolutional Neural Networks and Graph Neural Networks to effectively extract features from image sources, and combine them with historical data and control inputs. The primary novelty of this algorithm is its ability to make predictions when the target is occluded in the 2D detection results. The proposed system achieved a tracking error of 0.15 mm, outperforming standard model-based tracking techniques.