# Generative Adversarial Network based Heuristics for Sampling-based Path Planning

arXiv: https://arxiv.org/abs/2012.03490 · HTML: https://mlsanity.com/paper/2012.03490/

## Key facts

- Cited by 7 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Improving Path Planning Performance through Multimodal Generative Models with Local Critics](https://mlsanity.com/paper/2306.09470/) (2023)
- [Trajectory Planning for Autonomous Driving in Unstructured Scenarios Based on Graph Neural Network and Numerical Optimization](https://mlsanity.com/paper/2406.08855/) (2024)
- [IE-GAN: An Improved Evolutionary Generative Adversarial Network Using a New Fitness Function and a Generic Crossover Operator](https://mlsanity.com/paper/2109.11078/) (2021)
- [CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning](https://mlsanity.com/paper/2305.10442/) (2023)
- [Visualizing High-Dimensional Configuration Spaces: A Comprehensive Analytical Approach](https://mlsanity.com/paper/2312.10918/) (2023)
- [Enhancing Path Planning Performance through Image Representation Learning of High-Dimensional Configuration Spaces](https://mlsanity.com/paper/2501.06639/) (2025)
- [SenseExpo: Spatial Exploration and Navigation via Scene Estimation from Expeditious Predictive Operators](https://mlsanity.com/paper/2503.16000/) (2025)

## Abstract (arXiv)

Sampling-based path planning is a popular methodology for robot path planning. With a uniform sampling strategy to explore the state space, a feasible path can be found without the complex geometric modeling of the configuration space. However, the quality of initial solution is not guaranteed and the convergence speed to the optimal solution is slow. In this paper, we present a novel image-based path planning algorithm to overcome these limitations. Specifically, a generative adversarial network (GAN) is designed to take the environment map (denoted as RGB image) as the input without other preprocessing works. The output is also an RGB image where the promising region (where a feasible path probably exists) is segmented. This promising region is utilized as a heuristic to achieve nonuniform sampling for the path planner. We conduct a number of simulation experiments to validate the effectiveness of the proposed method, and the results demonstrate that our method performs much better in terms of the quality of initial solution and the convergence speed to the optimal solution. Furthermore, apart from the environments similar to the training set, our method also works well on the environments which are very different from the training set.