Fast End-to-End Generation of Belief Space Paths for Minimum Sensing Navigation

TL;DR

This paper introduces a deep learning-based approach using U-Net to rapidly generate belief space paths for minimum sensing navigation, significantly reducing computation time compared to traditional sampling-based planners.

Contribution

The paper presents a novel deep learning framework that predicts optimal belief space paths directly from problem images, improving efficiency in motion planning.

Findings

Reduces computation time compared to baseline algorithms

Uses U-Net architecture to learn path dependencies from data

Successfully reconstructs paths from U-Net output images

Abstract

We revisit the problem of motion planning in the Gaussian belief space. Motivated by the fact that most existing sampling-based planners suffer from high computational costs due to the high-dimensional nature of the problem, we propose an approach that leverages a deep learning model to predict optimal path candidates directly from the problem description. Our proposed approach consists of three steps. First, we prepare a training dataset comprising a large number of input-output pairs: the input image encodes the problem to be solved (e.g., start states, goal states, and obstacle locations), whereas the output image encodes the solution (i.e., the ground truth of the shortest path). Any existing planner can be used to generate this training dataset. Next, we leverage the U-Net architecture to learn the dependencies between the input and output data. Finally, a trained U-Net model is applied to a new problem encoded as an input image. From the U-Net's output image, which is interpreted as a distribution of paths,an optimal path candidate is reconstructed. The proposed method significantly reduces computation time compared to the sampling-based baseline algorithm.