Shape-Based Path Adaptation and Simulation-Based Velocity Optimization of Initial Tool Trajectories for Robotic Spray Painting

Abstract

Automatic path planners in robotic spray painting aim to generate trajectories for the paint gun based on a paint deposition model and a model of the object's surface. Current path planners often ignore shadowing effects and are therefore limited to regular surfaces with low curvatures. In addition, a robot model is typically not included so that executability is not ensured. This paper proposes a novel path planner which (1) spatially adapts initial trajectories based on the shape of the target surface while taking the robot workspace limits into account and (2) optimizes the execution speed of the robot according to a desired trade-off between paint uniformity and cycle time while taking the robot acceleration limits into account. Simulation results validate the approach for the painting of two types of complex free-form objects using an artificial 5-DOF robot arm of which the workspace and joint limits significantly restrict the generated robot trajectory. The proposed path planner offers programmers of robotic spray painting tasks an approach for planning executable and time-efficient robot trajectories for complex free-form surfaces.