  This paper describes and evaluates the use of Generative Adversarial Networks
(GANs) for path planning in support of smart mobility applications such as
indoor and outdoor navigation applications, individualized wayfinding for
people with disabilities (e.g., vision impairments, physical disabilities,
etc.), path planning for evacuations, robotic navigations, and path planning
for autonomous vehicles. We propose an architecture based on GANs to recommend
accurate and reliable paths for navigation applications. The proposed system
can use crowd-sourced data to learn the trajectories and infer new ones. The
system provides users with generated paths that help them navigate from their
local environment to reach a desired location. As a use case, we experimented
with the proposed method in support of a wayfinding application in an indoor
environment. Our experiments assert that the generated paths are correct and
reliable. The accuracy of the classification task for the generated paths is up
to 99% and the quality of the generated paths has a mean opinion score of 89%.
