This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing key challenges in collision detection and minimum distance estimation. By combining analytical modeling, real-time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering precise theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7-DOF Kinova robotic arms, generating a diverse dataset of configurations for collision detection and distance estimation. Using these insights, a deep neural network model was trained with joint actuators of robot arms and relative positions as inputs, achieving a mean absolute error of 282.2 mm and an R-squared value of 0.85. The close alignment between predicted and actual distances highlights the network's accuracy and its ability to generalize spatial relationships. This work demonstrates the effectiveness of combining analytical precision with machine learning algorithms to enhance the precision and reliability of robotic systems.

翻译：本研究提出了一种集成框架，旨在通过解决碰撞检测和最小距离估计中的关键挑战，提升腹腔镜手术中机械臂的安全性和操作效率。该框架结合了分析建模、实时仿真和机器学习，为保障机器人安全操作提供了稳健的解决方案。研究开发了一个分析模型，用于基于机械臂的关节构型估计其之间的最小距离，提供了精确的理论计算，既可作为验证工具，也可作为基准。为补充此模型，研究创建了一个3D仿真环境，对两个7自由度Kinova机械臂进行建模，生成了用于碰撞检测和距离估计的多样化构型数据集。基于这些数据，研究训练了一个深度神经网络模型，以机械臂的关节执行器状态和相对位置作为输入，实现了282.2毫米的平均绝对误差和0.85的R平方值。预测距离与实际距离的紧密吻合凸显了该网络的准确性及其泛化空间关系的能力。本工作证明了将分析精度与机器学习算法相结合，能够有效提升机器人系统的精确性和可靠性。