Secure and secret cooperation of robotic swarms by using Merkle trees.

Swarm robotics systems are envisioned to become an important component of both academic research and real-world applications. However, in order to reach widespread adoption, new models that ensure the secure cooperation of these systems need to be developed. This work proposes a novel model to encapsulate cooperative robotic missions in Merkle trees, one of the fundamental components of blockchain technology. With the proposed model, swarm operators can provide the "blueprint" of the swarm's mission without disclosing raw data about the mission itself. In other words, data verification can be separated from data itself. We propose a system where swarm robots have to "prove" their integrity to their peers by exchanging cryptographic proofs. This work analyzes and tests the proposed approach for two different robotic missions: foraging (where robots modify the environment) and maze formation (where robots become part of the environment). In both missions, robots were able to cooperate and carry out sequential operations in the correct order without having explicit knowledge about the mission's high-level goals or objectives. The performance, communication costs, and information diversity requirements for the proposed approach are analyzed. Finally, conclusions are drawn and future work directions are suggested.