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A multi-arm robot for mandible reconstruction assisted surgery is developed. It has three manipulators and eighteen degrees of freedom. In order to prevent the collision of the manipulator caused by the overlap of the motion space of the manipulator during the space positioning of the whole mandible reconstruction operation, a collision avoidance path planning method for manipulator based on C-space is proposed. In order to obtain the space non-interference posture of one manipulator, and the interference posture of the other manipulators is taken as the obstacle mapping in C-space to obtain the free space after removing obstacles. At the same time, combining with the space position of the system manipulator, the C-space mapping mathematical model of feature points and line segments is studied, and the mapping mathematical model of any point and line segment in the plane is obtained. By introducing admissibility proof lemma to weigh the evaluation function, the improved heuristic A* search algorithm is used to search the best path for collision avoidance. The collision avoidance experiment is carried out through simulation and robot platform, and finally the animal experiment is carried out. These experimental results verify the feasibility and validity of the collision avoidance path planning method for the space manipulator.

Robotic-assisted platforms are expected to guarantee the accuracy of surgical operation and accelerate its learning curve. Iris tracking can guide the robotic manipulator during the operation. However, few researches focused on it during surgery. It is a big challenge due to the deformation of the iris and occlusion caused by instruments. A novel real-time iris tracking method based on a regression network are proposed to meet the speed and accuracy requirements of the ophthalmic robotic system. It utilizes the low-level visual features and high-level semantic meanings from different layers to capture the discriminative representation of the iris target. Then the bottleneck layers are added to improve computation efficiency. Furthermore, a multi-loss function is designed by jointly learning Absolute loss and Euclidean loss. Finally, the experimental results under the typical surgical scene demonstrate that iris tracker achieves an accuracy of 89.16% and a real-time speed of 134fps with GPU, which is suitable for the ophthalmic robotic system to perform real-time robotic manipulation.

Ambiguous appearance discrimination plays an important role in the impurity detection task. Among the majority of deep learning models, images from every sequence are processed separately instead of being considered collectively. Therefore, the outputs of these models given a single region proposal might not be accurate. In this paper, a gallery-guided graph architecture is proposed and integrated to overcome such limitations. Specifically, region proposals are firstly generated using a two-stream fusion network; then their feature embeddings are extracted from a convolutional neural network by reducing intra-class variations while increasing inter-class ones. Secondly, a graph representing clusters among different training sequences updates relationships between region proposals in the test sequence. Finally, the features of the graph are classified by a graph convolutional neural network. Different from those learned weights in conventional common object detectors, region features from all the training sequences are explicitly integrated into a gallery-guided graph architecture. Extensive experiments on IML-DET dataset demonstrate that our proposed method can obtain competitive performances compared with previous state-of-the-art object detection approaches transferred into this task.