DiffRP: Diffusion-Driven Promising Region Prediction for Sampling-Based Path Planning

Abstract

Utilizing neural networks to predict potential regions containing optimal paths in advance and subsequently biasing the sampling probability towards these promising regions has been proven to effectively enhance the path planning efficiency of sampling-based algorithms. %In complex scenarios, uniform sampling often leads to prolonged computation time, whereas the biased information provided by promising regions can guide the algorithm to reduce sampling in irrelevant areas, thereby significantly shortening the computation time. Undoubtedly, the accuracy of the promising regions is of paramount importance. Currently, the generalizability of many CNN- or Transformer-based promising region prediction models remains limited, often performing poorly in unknown environments. Incorrect region predictions may reduce the planning efficiency, sometimes even underperforming uniform sampling. This work aims to leverage diffusion models to generate more accurate promising regions, referred to as the DiffRP (Diffusion-based Region Prediction) model, thereby designing a non-uniform sampler to improve sampling efficiency and reduce computation time. We propose three paradigms for generating promising regions using diffusion models, among which we innovatively introduce a biased noise initialization method for the diffusion process. Specifically, we bias the mean of the noise distribution using obstacle maps and design a map-conditioned denoising model to progressively generate accurate promising regions from the biased noise. Experiments on public datasets demonstrate that our proposed DiffRP method outperforms existing state-of-the-art (SOTA) models by 30% in promising region prediction accuracy. Moreover, the non-uniform sampling alg