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

2025-01-11

AI Summary

To address real-time, safety-guaranteed path planning in unknown obstacle environments, this paper proposes a waypoint representation and planning framework grounded in generative modeling. Our method employs a diffusion-based Wasserstein GAN with gradient penalty to jointly model waypoint distributions over a continuous latent space, enabling robust multimodal generalization. Key contributions include: (1) a novel matrix-based waypoint encoding that explicitly preserves multidimensional spatial ordering; (2) an integrated forward diffusion process within the generative model to capture complex waypoint correlations; and (3) a distribution-confidence-driven automatic fallback mechanism ensuring probabilistic completeness without manual hyperparameter tuning. Experiments demonstrate substantial acceleration of RRT-family algorithms’ convergence in high-dimensional configuration spaces, alongside improved planning success rates and real-time performance. The implementation is publicly available.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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/.

Problem

• Pathfinding
• Efficiency
• Obstacle Avoidance

Innovation

• WGANs
• Path Planning
• Diffusion Process