Secure and secret cooperation in robot swarms

Eduardo Castello Ferrer

E. C. Ferrer, T. Hardjono, A. S. Pentland, M. Dorigo, Secure and secret cooperation in robot swarms. Sci. Robot. 6, eabf1538 (2021).


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 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 tasks 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. 

A high-level overview of the work follows. Please see the full paper, linked above, for technical details. 


When robots, devices, or humans in an online network want to work together as peers, they must communicate with each other to reach their collective goals. If the group’s messages are not secure, outsiders might learn the goals of the group or even impersonate its members. In this work, we demonstrate the first multi-agent system able to autonomously secure its peer-to-peer communication while collaborating to achieve a common goal, using an authenticated data structure known as a Merkle tree. The demonstration of Merkle trees as a successful decentralized cooperation mechanism has significant implications for any future complex systems application in which security or privacy plays a crucial role—networks of IoT devices, multi-robot factories, healthcare automation, and well beyond.

Swarm robotic missions

We show the implications of this approach for two different swarm robotics missions: 

  • Maze-formation: This mission takes place in a 5x5 grid, where each cell represents a 0.5x0.5 m2 space. In this mission, robots need to discover and occupy cells in order to form a custom maze encoded in a Merkle tree shared by the robots.  By exchanging cryptographic proofs, robots in the swarm are able to prove to their peers that they know specific pieces of information (i.e., already occupied cells) included in the swarm's mission and therefore that they are cooperating towards its completion.
  • Foraging: This mission takes place in the same arena as the maze-formation mission. However, in this scenario  robots, objects to be retrieved (represented as colored cells), and a target area (0.5 x 0.5 m2) located at the center are placed. The foraging mission is finished when all tasks in a sequence are completed—that is, when all colored objects have been delivered by robots in the right order at the target location.

New business models for  swarm robotic systems

In the last section of this work we present  a futuristic scenario where this research can be monetized. We introduce the IRIDIA Swarm Marketplace: a web-based service marketplace for swarm robotics whose logic is coded in a smart-contract and uploaded in the Ethereum blockchain. In the proposed app, organizations (in this case, IRIDIA, the artificial intelligence lab of the Université Libre de Bruxelles) with a robot swarm at their disposal can advertise the robotics services available to them (number of robots, duration of service, and price) (1). Then, customers are able to purchase these services and pay with their own crypto-wallets. Customers can upload a Merkle tree  with the list of sequential tasks the robots need to complete (2).  Robots receive the Merkle with all required tasks to complete a service (3). Once the service is completed, customers get the cryptographic proof that the robots completed all the tasks included in the Merkle tree, which allows them to trust the system and understand the service was not faked (4). Finally, customers get pictures and video footage of the final work the robots conducted. Videos are automatically uploaded by the system to a Youtube playlist (5).

A live demo can be found here:


In this work, we show that two of the main Merkle tree properties (i.e., correctness and security) open a new path towards secure and secret cooperation in robot swarms. Regarding the security aspect, by using this approach, in order to cooperate, the robots in a swarm are required to prove to their peers that they fulfilled certain actions or that they know or own some particular information, rather than merely rely on information received from other robots (sensor data, votes, etc.). This approach makes robots resistant against potential threats such as tampering attacks since any change in the task’s data (i.e., cell location, objects to be delivered) will necessarily change the proof’s outcome. Regarding the secrecy component, with the use of Merkle trees robot swarms are now able to separate the mission data from its verification. This allows robots to verify that a task was carried out by a member of the swarm without knowing what this task entailed or which robot took part in its completion. 

Additional information

Secure and secret cooperation in robot swarms is an academic publication within the Blockchain: a new framework for swarm robotic systems research project. More information about the synergy between swarm robotic systems and cryptographic technology can be found on the project's overview page, linked above.

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