用生成模型加速未知环境下的路径规划，提升效率与可靠性。
Enhancing Path Planning Performance through Image Representation Learning of High-Dimensional Configuration Spaces
- 用WGAN-GP结合扩散过程建模路径点分布，保持多维顺序。
- 训练后若模型失效，自动切换均匀采样，保证算法完备性。
- 无需人工调参，适合实时路径规划场景。
本文提出一种新方法，通过使用带梯度惩罚的 Wasserstein 生成对抗网络（WGAN-GP）来逼近快速探索随机树（RRT）在未知障碍物环境中生成无碰撞路径时的路径点分布，从而加速路径规划任务。该方法在连续潜在空间中引入前向扩散过程，有效处理多模态数据；同时将无碰撞路径的路径点编码为矩阵，自然保留其多维排序结构，不仅提升模型学习效果，还改善训练收敛性。此外，提出一种评估机制，判断训练模型是否未能准确捕捉真实路径点，若失败则自动回退至均匀采样，确保算法的概率完备性——这一过程传统上需人为设定最优比例，而本方法无需手动调节。实验表明，在严苛时间约束下，该方法显著加速了路径规划任务。源代码已公开于 https://bitbucket.org/joro3001/imagewgangpplanning/src/master/。
原文摘要 · Abstract (English)
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 waypoints for a collision-free path using the Rapidly-exploring Random Tree algorithm. Our approach involves conditioning the WGAN-GP with a forward diffusion process in a continuous latent space to handle multimodal datasets effectively. We also propose encoding the waypoints of a collision-free path as a matrix, where the multidimensional ordering of the waypoints is naturally preserved. This method not only improves model learning but also enhances training convergence. Furthermore, we propose a method to assess whether the trained model fails to accurately capture the true waypoints. In such cases, we revert to uniform sampling to ensure the algorithm's probabilistic completeness; a process that traditionally involves manually determining an optimal ratio for each scenario in other machine learning-based methods. Our experiments demonstrate promising results in accelerating path-planning tasks under critical time constraints. The source code is openly available at https://bitbucket.org/joro3001/imagewgangpplanning/src/master/.
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