Resumen
This work considers global path planning enabled by generative adversarial networks (GANs) on a 2D grid world. These networks can learn statistical relationships between obstacles, goals, states, and paths. Given a previously unseen combination of obstacles, goals, and an initial state, they can be asked to guess what a new path would look like. We performed experiments on a 64 × 64 pixel grid that generated a training set by using randomly positioned obstacles and goals. The heuristic search algorithm A* was used to create training paths due to its significant presence in the literature and ease of implementation. We experimented with architectural elements and hyperparameters, converging to a pix2pix-based architecture in which the generator was trained to generate plausible paths given obstacles and two points. A discriminator tried to determine whether these maps were real or fake. Additionally, we defined a qualitative path-generation ?success rate? metric derived from the Fréchet inception distance (FID) and optimized our architecture?s parameters, ultimately reaching a 74% success rate on the validation set. Furthermore, we discuss the applicability of this approach to safety-critical settings, concluding that this architecture?s performance and reliability are insufficient to offset the downsides of a black-box approach to path generation.