Automatic Path and Trajectory Planning for Robotic Spray Painting

In this paper, a new automated optimum path and trajectory generation system for robotic painting process is presented. Usually, a direct self-learning manual method is usually adopted, i.e. the operator drives the robot manually through a complete spraying cycle. Recently, in order to-avoid this time-consuming procedure, CAD-based or "acquire-comparerecognize" methods have been developed. Here, a general technique that avoids the need for manual programming or CAD drawings and allows to obtain an optimal path and trajectory by using the graph theory and operative research methods is developed. After an image acquisition phase, a partitioner splits the object into a set of primitives, then the proposed algorithm is run on the graph in order to generate the optimal path; then, a optimum trajectory planning phase is performed. Kinematic and dynamic simulators of the CMA Robotics robots have been developed and validated in order to allow a correct planning and evaluation of the results. The new path planning algorithm has been implemented in Matlab and Visual-Studio.NET environments. Practical applications are presented.