Combining spatial clustering and tour planning for efficient full area exploration

IF 1.9 4区 计算机科学 Q3 ROBOTICS
Robotica Pub Date : 2024-09-13 DOI:10.1017/s0263574724001085
Jiatong Bao, Sultan Mamun, Jiawei Bao, Wenbing Zhang, Yuequan Yang, Aiguo Song
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引用次数: 0

Abstract

Autonomous exploration in unknown environments has become a critical capability of mobile robots. Many methods often suffer from problems such as exploration goal selection based solely on information gain and inefficient tour optimization. Recent reinforcement learning-based methods do not consider full area coverage and the performance of transferring learned policy to new environments cannot be guaranteed. To address these issues, a dual-stage exploration method has been proposed, which combines spatial clustering of possible exploration goals and Traveling Salesman Problem (TSP) based tour planning on both local and global scales, aiming for efficient full-area exploration in highly convoluted environments. Our method involves two stages: exploration and relocation. During the exploration stage, we introduce to generate local navigation goal candidates straight from clusters of all possible local exploration goals. The local navigation goal is determined through tour planning, utilizing the TSP framework. Moreover, during the relocation stage, we suggest clustering all possible global exploration goals and applying TSP-based tour planning to efficiently direct the robot toward previously detected but yet-to-be-explored areas. The proposed method is validated in various challenging simulated and real-world environments. Experimental results demonstrate its effectiveness and efficiency. Videos and code are available at https://github.com/JiatongBao/exploration.

结合空间聚类和游览规划,实现高效的全区域探索
在未知环境中进行自主探索已成为移动机器人的一项重要能力。许多方法往往存在探索目标选择仅基于信息增益、巡回优化效率低等问题。最新的基于强化学习的方法没有考虑全区域覆盖,而且无法保证将所学策略迁移到新环境中的性能。为了解决这些问题,我们提出了一种双阶段探索方法,它结合了可能探索目标的空间聚类和基于旅行推销员问题(TSP)的局部和全局巡回规划,目的是在高度复杂的环境中进行高效的全区域探索。我们的方法包括两个阶段:探索和迁移。在探索阶段,我们从所有可能的本地探索目标簇中直接生成本地导航目标候选。本地导航目标是通过巡回规划确定的,利用的是 TSP 框架。此外,在重新定位阶段,我们建议对所有可能的全局探索目标进行聚类,并应用基于 TSP 的巡回规划来有效地将机器人引向先前探测到但尚未探索的区域。我们在各种具有挑战性的模拟和现实环境中对所提出的方法进行了验证。实验结果证明了该方法的有效性和效率。视频和代码请访问 https://github.com/JiatongBao/exploration。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Robotica
Robotica 工程技术-机器人学
CiteScore
4.50
自引率
22.20%
发文量
181
审稿时长
9.9 months
期刊介绍: Robotica is a forum for the multidisciplinary subject of robotics and encourages developments, applications and research in this important field of automation and robotics with regard to industry, health, education and economic and social aspects of relevance. Coverage includes activities in hostile environments, applications in the service and manufacturing industries, biological robotics, dynamics and kinematics involved in robot design and uses, on-line robots, robot task planning, rehabilitation robotics, sensory perception, software in the widest sense, particularly in respect of programming languages and links with CAD/CAM systems, telerobotics and various other areas. In addition, interest is focused on various Artificial Intelligence topics of theoretical and practical interest.
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