文献类型: 会议论文
第一作者: Yuxin Jin
作者: Yuxin Jin 1 ; Franco Pinan Basualdo 1 ; Antonio Marino 2 ; Yongfeng Mei 3 ; Paolo Robuffo Giordano 4 ; Claudio Pacchierotti 4 ; Sarthak Misra 5 ;
作者机构: 1.Department of Biomechanical Engineering, Surgical Robotics Laboratory, University of Twente, Enschede, AE, The Netherlands
2.Université de Rennes, CNRS, Inria, IRISA - Rennes, France
3.Department of Materials Science & State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, P. R. China|International Institute of Intelligent Nanorobots and Nanosystems, Fudan University, Shanghai, P. R. China|Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Fudan University, Shanghai, P. R. China
4.CNRS, Université de Rennes, Inria, IRISA-Rennes, France
5.Department of Biomechanical Engineering, Surgical Robotics Laboratory, University of Twente, Enschede, AE, The Netherlands|Department of Biomaterials and Biomedical Technology Engineering, Surgical Robotics Laboratory, University of Groningen and University Medical Centre Groningen, Groningen, GZ, The Netherlands
关键词: Three-dimensional displays;Target tracking;Heuristic algorithms;Prediction algorithms;Feature extraction;Control systems;Robots
会议名称: International Conference on Manipulation, Automation and Robotics at Small Scales
主办单位:
页码: 1-6
摘要: 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.
分类号: tp2
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