Neural Collision Detection for Multi-arm Laparoscopy Surgical Robots Through Learning-from-Simulation

2026-01-21

AI Summary

This study addresses the challenge of collision risk and minimum distance estimation in multi-arm laparoscopic surgical robots by proposing a real-time collision detection framework that integrates analytical modeling, 3D physics simulation, and deep learning. The approach constructs an analytical geometric model of a 7-degree-of-freedom robotic arm and generates diverse configuration data to train a deep neural network that predicts inter-arm minimum distances using joint states and relative poses as inputs. By uniquely combining analytical modeling with simulation-driven deep learning, the method achieves high-accuracy spatial relationship generalization and enables real-time collision warnings. Experimental results demonstrate the model’s accuracy and generalization capability, yielding a mean absolute error of 282.2 mm and an R² score of 0.85 in minimum distance prediction.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Natural Language Processing: Safety and Robustness
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• collision detection
• minimum distance estimation
• multi-arm surgical robots
• laparoscopic surgery
• robotic safety

Innovation

• neural collision detection
• learning-from-simulation
• minimum distance estimation
• surgical robotics
• deep neural network