Neural-Network-Driven Method for Optimal Path Planning via High-Accuracy Region Prediction

TL;DR

This paper introduces RPNN-RRT*, a neural-network-driven path planning algorithm that uses high-accuracy region prediction to improve efficiency and success rate in complex environments.

Contribution

It presents a novel high-accuracy region prediction neural network with attention and hierarchical loss, enhancing sampling efficiency in path planning.

Findings

Achieves 89.13% accuracy in region prediction.

Demonstrates significant improvements in calculation time and success rate.

Outperforms other models in complex environment scenarios.

Abstract

Sampling-based path planning algorithms suffer from heavy reliance on uniform sampling, which accounts for unreliable and time-consuming performance, especially in complex environments. Recently, neural-network-driven methods predict regions as sampling domains to realize a non-uniform sampling and reduce calculation time. However, the accuracy of region prediction hinders further improvement. We propose a sampling-based algorithm, abbreviated to Region Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain the optimal path based on a high-accuracy region prediction. First, we implement a region prediction neural network (RPNN), to predict accurate regions for the RPNN-RRT*. A full-layer channel-wise attention module is employed to enhance the feature fusion in the concatenation between the encoder and decoder. Moreover, a three-level hierarchy loss is designed to learn the pixel-wise, map-wise, and patch-wise features. A dataset, named Complex Environment Motion Planning, is established to test the performance in complex environments. Ablation studies and test results show that a high accuracy of 89.13% is achieved by the RPNN for region prediction, compared with other region prediction models. In addition, the RPNN-RRT* performs in different complex scenarios, demonstrating significant and reliable superiority in terms of the calculation time, sampling efficiency, and success rate for optimal path planning.