  This paper presents a novel method for accelerating path planning tasks in
unknown scenes with obstacles by utilizing Wasserstein Generative Adversarial
Networks (WGANs) with Gradient Penalty (GP) to approximate the distribution of
the free conditioned configuration space. Our proposed approach involves
conditioning the WGAN-GP with a Variational Auto-Encoder in a continuous latent
space to handle multimodal datasets. However, training a Variational
Auto-Encoder with WGAN-GP can be challenging for image-to-configuration-space
problems, as the Kullback-Leibler loss function often converges to a random
distribution. To overcome this issue, we simplify the configuration space as a
set of Gaussian distributions and divide the dataset into several local models.
This enables us to not only learn the model but also speed up its convergence.
We evaluate the reconstructed configuration space using the homology rank of
manifolds for datasets with the geometry score. Furthermore, we propose a novel
transformation of the robot's configuration space that enables us to measure
how well collision-free regions are reconstructed, which could be used with
other rank of homology metrics. Our experiments show promising results for
accelerating path planning tasks in unknown scenes while generating
quasi-optimal paths with our WGAN-GP. The source code is openly available.
