DiffRP: Diffusion-Driven Promising Region Prediction for Sampling-Based Path Planning
zongwu Xie, Yiming Ji, Yang Liu, Yiqian Xie, Zhengpu Wang, Boyu Ma, BAOSHI CAO
AI summary
Problem
Existing neural network models for predicting promising regions in path planning lack generalization in unfamiliar environments and fail to capture the complex distribution of optimal paths, treating the task as simple pixel-wise classification.
Approach
The authors introduce DiffRP, which uses a diffusion model with a novel obstacle-biased noise initialization and a map-conditioned denoising architecture to progressively generate accurate promising regions from random noise.
Key results
- First application of diffusion models to promising region prediction
- Novel biased noise initialization mechanism incorporating obstacle maps
- 35–42% improvement in prediction accuracy over state-of-the-art models
- 3–52% reduction in sample count for diffusion-enhanced path planners
Why it matters
This approach enhances the computational efficiency and robustness of sampling-based path planning, directly benefiting autonomous robots navigating complex or unfamiliar environments.
Abstract
Utilizing neural networks to predict potential re- gions containing optimal paths in advance and subsequently biasing the sampling probability towards these promising regions has been proven to effectively enhance the path planning effi- ciency 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 environ- ments. 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 exist- ing state-of-the-art models by 35∼42% in promising region pre- diction accuracy. Moreover, the non-uniform sampling algorithm (DiffRP-RRT*) based on this region achieves a 3∼52% reduction in sample number compared with other neural-network-driven approaches.