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.

翻译：快速探索随机树（RRTs）是移动机器人自主探索中常用的技术。然而，RRTs采用的随机采样可能导致前沿提取效率低下且不准确，从而影响探索性能。针对路径规划速度慢和路径代价高的问题，我们提出一种基于广义Voronoi图（GVD）多选策略的机器人探索框架。该框架包含三个组件：一种新颖的建图模型，利用端到端神经网络实时构建环境GVD；基于GVD的启发式方案，可加速前沿提取并减少前沿冗余；以及一个多选前沿分配方案，该方案考虑不同类型的前沿，使机器人在探索过程中能够做出合理决策。我们通过仿真和真实世界实验评估了该方法，结果表明其在效率和鲁棒性方面优于基于RRT的探索方法。