机器人群体中的安全与保密协作
Secure and secret cooperation in robotic swarms
AI总结：
本文提出利用默克尔树封装群体机器人任务蓝图，使机器人通过交换密码学证明验证同伴完整性，在觅食和迷宫编队任务中验证了无需知晓高层目标即可安全协作的可行性。
摘要：
群体机器人系统在学术研究和现实应用中的重要性正在稳步提升。然而，要实现广泛采用，需要开发能够确保大规模机器人群体安全协作的新模型。本文提出了一种新方法，将协作式机器人任务封装在一种被称为默克尔树（Merkle tree）的认证数据结构中。通过这种方法，操作者可以提供群体任务的“蓝图”，而无需公开其原始数据。换句话说，数据验证可以与数据本身分离。我们提出了一种系统，在该系统中，群体中的机器人为了协作完成任务，必须通过交换密码学证明来向同伴“证明”自身的完整性。我们展示了该方法在两种不同群体机器人任务中的意义：觅食和迷宫编队。在这两项任务中，群体机器人能够在没有明确了解任务高层目标的情况下进行协作并执行顺序操作。本文展示的结果证明了在仿真和真实机器人实验中，使用默克尔树作为群体机器人系统协作机制的可行性，这对未来安全至关重要的去中心化机器人应用具有意义，例如环境监测、基础设施监控和灾害管理。
英文摘要：
The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a novel method to encapsulate cooperative robotic missions in an authenticated data structure known as Merkle tree. With this method, operators can provide the "blueprint" of the swarm's mission without disclosing its raw data. In other words, data verification can be separated from data itself. We propose a system where robots in a swarm, to cooperate towards mission completion, have to "prove" their integrity to their peers by exchanging cryptographic proofs. We show the implications of this approach for two different swarm robotics missions: foraging and maze formation. In both missions, swarm robots were able to cooperate and carry out sequential operations without having explicit knowledge about the mission's high-level objectives. The results presented in this work demonstrate the feasibility of using Merkle trees as a cooperation mechanism for swarm robotics systems in both simulation and real-robot experiments, which has implications for future decentralized robotics applications where security plays a crucial role such as environmental monitoring, infrastructure surveillance, and disaster management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
