A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms

IF 5.3 2区计算机科学 Q2 ROBOTICS

IEEE Robotics and Automation Letters Pub Date : 2025-09-04 DOI:10.1109/LRA.2025.3606349

Hanqing Zhao;Alexandre Pacheco;Giovanni Beltrame;Xue Liu;Marco Dorigo;Gregory Dudek

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引用次数: 0

Abstract

Recent studies demonstrate the potential of blockchain to enable robots in a swarm to achieve secure consensus about the environment, particularly when robots are homogeneous and perform identical tasks. Typically, robots receive rewards for their contributions to consensus achievement, but no studies have yet targeted heterogeneous swarms, in which the robots have distinct physical capabilities suited to different tasks. We present a novel framework that leverages domain knowledge to decompose the swarm mission into a hierarchy of tasks within smart contracts. This allows the robots to reach a consensus about both the environment and the action plan, allocating tasks among robots with diverse capabilities to improve their performance while maintaining security against faults and malicious behaviors. We refer to this concept as equitable and secure task allocation. Validated in Simultaneous Localization and Mapping missions, our approach not only achieves equitable task allocation among robots with varying capabilities, improving mapping accuracy and efficiency, but also shows resilience against malicious attacks.

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机器人群中公平安全任务分配的区块链框架

最近的研究表明，区块链的潜力使机器人在群体中对环境达成安全共识，特别是当机器人是同质的并执行相同的任务时。通常情况下，机器人会因其对达成共识的贡献而获得奖励，但目前还没有针对异质群体的研究，在异质群体中，机器人具有适合不同任务的独特物理能力。我们提出了一个新的框架，利用领域知识将群体任务分解为智能合约中的任务层次结构。这使得机器人能够就环境和行动计划达成共识，在具有不同功能的机器人之间分配任务，以提高它们的性能，同时保持对故障和恶意行为的安全性。我们把这个概念称为公平和安全的任务分配。在同步定位和地图绘制任务中，我们的方法不仅实现了不同能力的机器人之间的公平任务分配，提高了地图绘制的准确性和效率，而且显示出对恶意攻击的弹性。

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

IEEE Robotics and Automation Letters Computer Science-Computer Science Applications

CiteScore

9.60

自引率

15.40%

发文量

1428

期刊介绍： The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.