Analysis and Mitigation of Inconsistencies in Blockchain-Enabled Robot Swarms

Recent research has demonstrated that blockchain-enabled robot swarms—where robots coordinate using blockchain technology—can secure robot swarms by neutralizing malicious and malfunctioning robots. This security is achieved through blockchain technology’s consistency properties. However, prior work addressed malfunctions at the information level, that is, it studied how to neutralize robots that stored information in the blockchain that did not correspond to the real-world state (i.e., it studied the oracle problem). In contrast, this study focuses on inconsistencies at the blockchain protocol level. We analyze how network partitions, which may arise from robots’ local-only communication capabilities, malfunctioning hardware, or external attacks, can lead to inconsistent information in a robot swarm. In order to mitigate these disruptions, we propose a decentralized approach to detect partitions and a corresponding response. We study our approach in a swarm robotics simulator, where we demonstrate its effectiveness in reducing blockchain inconsistencies.