A swarm of autonomous robots can provide a new way to bring reliable real-world data to the blockchain without relying on centralized sources.
The idea, detailed in a new preprint study titled Swarm Oracle: Untrusted Blockchain Contracts by Robot Swarms, is based on previous peer review studies in which researchers demonstrate that mobile robots can reach credible consensus in chaos, cyberattacks, or even hostile environments. A new study introduces an approach to persistent problems in blockchain design: how to obtain validated real data into smart contracts without introducing new trust points.
Blockchain Oracle is a service that securely provides external real data to blockchain smart contracts, allowing those contracts to be executed based on information that exists outside the blockchain network.
“Oracle problems” refers to the challenge of supplying off-chain data to a distributed system. Blockchains like Ethereum are built to be unreliable. However, the same design prevents smart contracts from accessing external information, such as weather reports, price supplies, and sensor measurements, without third-party input.
Today’s blockchain oracles like ChainLink aggregate data from multiple sources to reduce reliance on either feed. However, they can still reintroduce concentration risks, either through opaque aggregation methods or a single point of failure.
Swarm Oracle proposes another model: a swarm of robots. The system uses a collection of simple, low-cost mobile robots equipped with basic sensors and communication hardware to collect environmental data and reach consensus through Byzantine fault resistance protocols. Once consensus is reached, Swarm can publish the findings to the blockchain, where data is available in smart contracts.
This concept expands previous work by integrating blockchain disclosure into the decision-making process of robot swarms. In a 2023 nature study, researchers showed how the herds maintain the accuracy of consensus even when a third of robots are compromised, data is misreported, and they refrain from voting or physically hampering other robots.
In the new system, the robots will host permitted blockchains locally, allowing data to be stored and verified without the need for continuous Internet access. If necessary, you can upload your final contract to a public blockchain like Ethereum. Local chains reduce communication overhead while enabling transparency.
The herd includes a built-in reputation system. Robots that attempt to operate the system gradually lose their ability to participate. This provides a mechanism for “self-healing” with false or malicious robots that have been excluded from future consensus rounds.
Researchers tested the herd’s oracle protocol in simulations and used a ground-based device equipped with a Raspberry PI board using a physical robot called Pi-Pucks. The experiments used the same robot from a single lab, but the system was designed to support a wide variety of herd types.
Swarm Oracle use cases include validating claim disaster damage, monitoring air or water quality, or supporting a distributed physical infrastructure network (depins). By operating independently across a variety of terrains, the robot can reach areas that are inaccessible or too expensive to monitor.
However, researchers acknowledge that challenges remain. A malicious agent can try to mimic an honest robot. The robot can recover from temporary disconnection, but long distances can be strained with communication.
The idea of robots as blockchain participants is nothing new. Projects like Helium have investigated distributed hardware oracles for specific tasks such as network connectivity.
This concept has become increasingly interested in using autonomous agents to make economic decisions, such as routing delivery and grid load management. Robot developers also embed cryptocurrency wallets in autonomous systems to perform user transactions.
Whether Swarm Oracle can move from simulation to real-world deployment is still unknown, with general distrust of cost, robot availability, and AI adoption delays.
