Surgical Robotic Arm Guidance System Based on Point Laser Precise Navigation
Automated surgical guidance systems are increasingly important in clinical settings,driven by advancements in image detection technologies and the growing demand for surgical procedures.However,the need for the system to have real-time visual precision guidance restricts the range of applications in clinical surgery.When a visual signal guides the robotic arm for path planning,the inefficiency of traditional algorithms in low planning can hinder the real-time capability of the system.To address these problems,a navigation control system based on a point-laser-guided surgical robotic arm is proposed.The visual part is based on the YOLOv5 network and preprocessed using the super-resolution reconstruction algorithm.Fusion feature aggregation and single-scale recognition improvement strategies are proposed to achieve rapid and accurate point-laser tracking.For motion planning,a rapidly-exploring random tree(RRT)algorithm that integrates target bias and bidirectional expansion is proposed to constrain the target point attitude using lesion point cloud information for collision pre-detection and planning decision during path generation.The validity and feasibility of the proposed algorithm were verified through experiments,demonstrating that the optimized algorithm achieves an AP50 recognition accuracy of 97.6%and an AP75 recognition accuracy of 83.5%.Moreover,the improved RRT algorithm accurately and rapidly plans the optimal obstacle avoidance path,achieving a 7.2 percentage points improvement over YOLOv5 in traditional video target recognition.
YOLOv5multi-scale integrationrapidly-exploring random treespostural restraintspath planning
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