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.

翻译：本文提出一种新颖方法，利用带有梯度惩罚的Wasserstein生成对抗网络近似自由条件配置空间的分布，以加速未知障碍场景下的路径规划任务。我们的方法在连续潜在空间中结合变分自编码器对WGAN-GP进行条件约束，以处理多模态数据集。然而，对于图像到配置空间问题，使用WGAN-GP训练变分自编码器具有挑战性，因为Kullback-Leibler损失函数常收敛至随机分布。为克服此问题，我们将配置空间简化为高斯分布集合，并将数据集划分为多个局部模型。这不仅实现了模型学习，还加速了收敛过程。我们利用流形同调秩结合几何评分对重构的配置空间进行评估。此外，我们提出一种新型机器人配置空间变换方法，可衡量无碰撞区域的重构质量，且该变换能与其他同调度量指标配合使用。实验表明，该方法在未知场景中生成准最优路径的同时，能显著加速路径规划任务。源代码已公开。