Routing Design Methodology for Collaborative Robots in the Car Painting Process Using Perturbative Heuristics

Different problems in the automotive industry focus on managing the components and processes of automobile manufacturing. Some of these problems are Collaborative Robotic Problems, Car Sequencing, Car-Painting, among others. The specific case of the car painting problem is a problem well documented in the State-of-Art. This problem has different scenarios according to the restrictions; for example, the first is to coordinate the paths of set robotics arms to paint a piece. Another variant describes the process of touching up paint in existing pieces. For both problems in this work, a Routing Design Methodology is proposed for collaborative robots. Our methodology is based on the design of CAD models of the cars that were created or drafts from real vehicles, and each model must have a set of n pieces for each model. An instance of this problem is made by a set of pieces meshed to obtain points of interest. After this process, each piece meshed will generate the point matrices that are the input of the algorithms. The matrix is processed by heuristic algorithms to construct and modify a path or paths (depending on the number of collaborative robots), and then a cost is assigned to it. Finally, the best algorithms are selected for a specific instance. We use a non-parametric test to determine the best algorithms for solving this problem.