Multi-robot cooperative autonomous exploration via task allocation in terrestrial environments.

IF 2.6 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Neurorobotics Pub Date : 2023-01-01 DOI:10.3389/fnbot.2023.1179033

Xiangda Yan, Zhe Zeng, Keyan He, Huajie Hong

引用次数: 0

Abstract

Cooperative autonomous exploration is a challenging task for multi-robot systems, which can cover larger areas in a shorter time or path length. Using multiple mobile robots for cooperative exploration of unknown environments can be more efficient than a single robot, but there are also many difficulties in multi-robot cooperative autonomous exploration. The key to successful multi-robot cooperative autonomous exploration is effective coordination between the robots. This paper designs a multi-robot cooperative autonomous exploration strategy for exploration tasks. Additionally, considering the fact that mobile robots are inevitably subject to failure in harsh conditions, we propose a self-healing cooperative autonomous exploration method that can recover from robot failures.

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基于任务分配的多机器人协同自主探索。

协作自主探索是多机器人系统面临的一项具有挑战性的任务，它可以在更短的时间或路径长度内覆盖更大的区域。使用多个移动机器人进行未知环境的协同探索比单个机器人更有效，但多机器人协同自主探索也存在许多困难。多机器人协作自主探索成功的关键是机器人之间的有效协调。针对勘探任务，设计了一种多机器人协同自主勘探策略。此外，考虑到移动机器人在恶劣条件下不可避免地会发生故障，我们提出了一种可以从机器人故障中恢复的自修复协作自主探索方法。

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来源期刊

Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS

CiteScore

5.20

自引率

6.50%

发文量

250

审稿时长

14 weeks

期刊介绍： Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.