用深度学习精准预测多机械臂手术机器人间距，提前预警碰撞风险。
Neural Minimum-Distance Estimation for Collision-Aware Operation of Multi-Arm Laparoscopy Surgical Robots Through Learning-from-Simulation
- 结合解析建模与仿真数据，训练神经网络预测机械臂间最小距离。
- 模型在测试集上误差仅28.7毫米，相关系数达0.94，预测稳定可靠。
- 适合用于多臂腹腔镜机器人系统，提升手术安全与操作效率。
本研究提出一种集成框架，通过学习仿真数据提升多机械臂腹腔镜手术机器人的安全性和操作效率。基于机械臂关节配置，构建解析模型以估算最小距离，作为验证基准；同时搭建3D仿真环境，模拟两台7自由度Kinova Gen3机械臂（加拿大魁北克省博伊斯布里安，Kinova公司），生成多样化配置数据集。利用这些数据训练深度残差神经网络，输入为关节配置，输出为距离预测。在独立验证集上，模型达到R² = 0.940，RMSE = 42.0 mm，MAE = 28.7 mm，均值偏差接近零，展现优异预测精度与泛化能力。该框架作为早期碰撞预警层，当预测的臂间距离低于0.2米时触发警报，对应机械臂表面间约50毫米间隙（基于Kinova Gen3横截面半径）。结果表明，解析建模与机器学习结合可显著提升多臂机器人系统的精度与可靠性。
原文摘要 · Abstract (English)
This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing minimum distance estimation between multi-arm manipulators and the associated collision-aware warning. 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 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 (Kinova inc., Boisbriand, QC, Canada), generating a diverse dataset of configurations for distance estimation and collision warning. Using these insights, a deep residual neural network model was trained with joint configurations as inputs. On the held out validation set, the model achieves R2 = 0.940, RMSE = 42.0 mm, MAE = 28.7 mm, and a near zero mean bias, demonstrating strong predictive accuracy and consistent generalization across the workspace. The framework is intended as an early collision warning layer, where a warning is triggered when the predicted inter-arm distance falls below a 0.2 m threshold, which corresponds to a surface to surface clearance of approximately 50 mm given the Kinova Gen3 (Kinova inc., Boisbriand, QC, Canada) cross sectional radius. This work demonstrates the effectiveness of combining analytical modeling with machine learning to enhance the precision and reliability of multi-arm robotic systems.
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