GVD-Exploration: An Efficient Autonomous Robot Exploration Framework Based on Fast Generalized Voronoi Diagram Extraction

TL;DR

This paper introduces a novel robot exploration framework utilizing real-time GVD construction via neural networks, significantly improving exploration efficiency and robustness over traditional RRT-based methods.

Contribution

The paper presents a new GVD-based exploration framework with real-time neural network mapping, heuristic frontiers extraction, and rational multi-choice frontier assignment.

Findings

Outperforms RRT-based methods in efficiency

Reduces frontiers redundancy and speeds up exploration

Demonstrates robustness in real-world experiments

Abstract

Rapidly-exploring Random Trees (RRTs) are a popular technique for autonomous exploration of mobile robots. However, the random sampling used by RRTs can result in inefficient and inaccurate frontiers extraction, which affects the exploration performance. To address the issues of slow path planning and high path cost, we propose a framework that uses a generalized Voronoi diagram (GVD) based multi-choice strategy for robot exploration. Our framework consists of three components: a novel mapping model that uses an end-to-end neural network to construct GVDs of the environments in real time; a GVD-based heuristic scheme that accelerates frontiers extraction and reduces frontiers redundancy; and a multi-choice frontiers assignment scheme that considers different types of frontiers and enables the robot to make rational decisions during the exploration process. We evaluate our method on simulation and real-world experiments and show that it outperforms RRT-based exploration methods in terms of efficiency and robustness.