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

TL;DR

This paper introduces a novel approach using Wasserstein GANs with Gradient Penalty and diffusion conditioning to efficiently learn waypoint distributions for faster collision-free path planning in unknown environments.

Contribution

It proposes a new method combining WGAN-GP with diffusion processes and waypoint encoding to improve learning and convergence in path planning, with a fallback to uniform sampling for reliability.

Findings

Accelerates path planning in complex scenes.

Improves training convergence and model accuracy.

Ensures probabilistic completeness with fallback sampling.

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/.