Path Planning in Support of Smart Mobility Applications using Generative Adversarial Networks
This addresses path planning for various smart mobility users, such as those with disabilities or in evacuation scenarios, but is incremental as it applies an existing GAN method to a new domain.
The paper tackles path planning for smart mobility applications by proposing a Generative Adversarial Network (GAN) architecture to generate accurate and reliable navigation paths, achieving up to 99% classification accuracy and an 89% mean opinion score for path quality in indoor wayfinding experiments.
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%.