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. Undoubtedly, the accuracy of the promising regions is of paramount importance. Currently, the generalizability of many CNN- or Transformer-based models remains limited, often performing poorly in unknown environments. To enhance generalization capability, we reformulate the promising region prediction problem as a conditional generation task and address it using a diffusion model, referred to as the DiffRP (Diffusion-based Region Prediction). We propose three paradigms for generating promising regions, 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 models by 35$\sim$42% in promising region prediction accuracy. Moreover, the non-uniform sampling algorithm (DiffRP-RRT*) based on this region achieves a 3$\sim$52% reduction in sample number compared with other neural-network-driven approaches.