Enhancing Path Planning Performance through Image Representation Learning of High-Dimensional Configuration Spaces

• URL: http://arxiv.org/abs/2501.06639v1
• Date: Sat, 11 Jan 2025 21:14:52 GMT
• Title: Enhancing Path Planning Performance through Image Representation Learning of High-Dimensional Configuration Spaces
• Authors: Jorge Ocampo Jimenez, Wael Suleiman,
• Abstract summary: We present a novel method for accelerating path-planning tasks in unknown scenes with obstacles. We approximate the distribution of waypoints for a collision-free path using the Rapidly-exploring Random Tree algorithm. Our experiments demonstrate promising results in accelerating path-planning tasks under critical time constraints.
• Score: 0.4143603294943439
• License:
• Abstract: 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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