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稀疏机器人群体:将群体应用于现实世界

Sparse Robot Swarms: Moving Swarms to Real-World Applications.

作者信息

Tarapore Danesh, Groß Roderich, Zauner Klaus-Peter

机构信息

School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom.

Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, United Kingdom.

出版信息

Front Robot AI. 2020 Jul 2;7:83. doi: 10.3389/frobt.2020.00083. eCollection 2020.

DOI:10.3389/frobt.2020.00083
PMID:33501250
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7805967/
Abstract

Robot swarms are groups of robots that each act autonomously based on only local perception and coordination with neighboring robots. While current swarm implementations can be large in size (e.g., 1,000 robots), they are typically constrained to working in highly controlled indoor environments. Moreover, a common property of swarms is the underlying assumption that the robots act in close proximity of each other (e.g., 10 body lengths apart), and typically employ uninterrupted, situated, close-range communication for coordination. Many real world applications, including environmental monitoring and precision agriculture, however, require scalable groups of robots to act jointly over large distances (e.g., 1,000 body lengths), rendering the use of swarms impractical. Using a dense swarm for such applications would be invasive to the environment and unrealistic in terms of mission deployment, maintenance and post-mission recovery. To address this problem, we propose the swarm concept, and illustrate its use in the context of four application scenarios. For one scenario, which requires a group of rovers to traverse, and monitor, a forest environment, we identify the challenges involved at all levels in developing a sparse swarm-from the hardware platform to communication-constrained coordination algorithms-and discuss potential solutions. We outline open questions of theoretical and practical nature, which we hope will bring the concept of sparse swarms to fruition.

摘要

机器人集群是由一群机器人组成的,每个机器人仅基于局部感知以及与相邻机器人的协作来自主行动。虽然当前的集群实现规模可能很大(例如,1000个机器人),但它们通常被限制在高度受控的室内环境中工作。此外,集群的一个共同特性是潜在的假设,即机器人彼此靠近行动(例如,相距10个机身长度),并且通常采用不间断的、基于情境的近距离通信进行协作。然而,许多现实世界的应用,包括环境监测和精准农业,需要可扩展的机器人组在远距离(例如,1000个机身长度)上联合行动,这使得使用集群变得不切实际。对于此类应用使用密集集群会对环境造成干扰,并且在任务部署、维护和任务后恢复方面也不现实。为了解决这个问题,我们提出了稀疏集群的概念,并在四个应用场景的背景下说明其用途。对于其中一个需要一组漫游车穿越并监测森林环境的场景,我们确定了在开发稀疏集群的各个层面所涉及的挑战——从硬件平台到受通信限制的协调算法——并讨论了潜在的解决方案。我们概述了理论和实践性质的开放性问题,希望这些问题能使稀疏集群的概念得以实现。

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