基于点激光精确导航的手术机械臂引导系统

Surgical Robotic Arm Guidance System Based on Point Laser Precise Navigation

随着图像检测相关技术的发展以及对手术技术需求的增加,自动化手术引导系统在临床场景中日渐重要。然而该系统需要具备实时性的视觉精确引导,限制了临床手术的应用范围。当视觉信号引导机械臂进行路径规划时,传统算法规划效率低的不足限制了系统实时性。针对上述问题,提出一种基于点激光引导手术机械臂的导航控制系统,视觉部分基于YOLOv5网络,利用超分辨率重建(SRCNN)算法进行预处理,提出融合特征聚合及单尺度识别改进策略,快速精确跟踪点激光。在运动规划方面,提出一种结合目标偏置以及双向扩展的快速随机搜索树(RRT)算法,利用病灶点云信息约束目标点姿态,对生成路径进行碰撞检测和规划决策。通过实验验证了上述方法的有效性和可行性,优化算法在交并比(IoU)阈值0.5下的平均精度均值(AP50)为97.6%,AP75识别精度达83.5%。相比传统视频目标识别的YOLOv5算法提升幅度达7.2百分点,改进RRT*算法能准确快速地规划出最优避障路径。

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.