Point Cloud-Based Multi-Robot Collaborative Spray-Painting Trajectory Planning and Production Line Simulation for Automotive Body

Abstract

This paper presents a point-cloud-driven framework for collaborative robotic spray painting of automotive bodies. The method reduces dependence on complete CAD models by extracting geometric constraints from unorganized scan data. A body-coordinate-constrained partitioning strategy assigns the vehicle surface to six painting robots while preserving regional continuity and production-line compatibility. An iterative threshold adjustment mechanism improves workload balance without destroying the left/right and upper/middle/lower workspace structure. Based on the partitioned point cloud, a slicing, sorting and smoothing procedure generates continuous spray trajectories with gap segmentation and B-spline smoothing. A RoboDK-based digital-twin loop then verifies inverse kinematics, collisions, cycle time and program executability through the Python API, and triggers local replanning when physical constraints are violated. Experiments on a heterogeneous six-robot cell show that the method maintains a load imbalance below 8%, eliminates singularities and collisions, reduces simulation cycle time, and automatically generates ABB RAPID and EFORT programs.