Gaussian Splatting and Point Cloud-Based Workspace Prediction for Collision-Free Trajectory Planning in Collaborative Robots

Abstract

As multi-robot collaboration becomes increasingly prevalent in modern industrial settings, ensuring collision-free operation among robots sharing the same workspace remains a critical challenge. This paper proposes an integrated framework that combines 3D Gaussian Splatting (3D-GS) for high-fidelity scene reconstruction, Generalized Iterative Closest Point (GICP) with Fast Global Registration (FGR) for robust pose estimation, a Deep Graph Convolutional Neural Network (DGCNN) for joint angle regression from point cloud data, Dynamic Mode Decomposition (DMD) for trajectory prediction, and a Control Barrier Function (CBF) for real-time safety enforcement. Through experiments, we validated the trajectory prediction of 0DOF objects and confirmed that joint angle prediction is possible from 3D-GS-based PLY data using DGCNN-based regression, utilizing joint angle training data collected at intervals of 15 to 45 degrees.