1

The blockchain: a new framework for robotic swarmsystems

Eduardo Castelló Ferrer

MIT Media Lab, 75 Amherst St., Cambridge, MA.ecstll@mit.edu

Abstract—Swarms of robots will revolutionize manyindustrial applications, from targeted material delivery toprecision farming. However, several of the heterogeneouscharacteristics that make them ideal for certain futureapplications — robot autonomy, decentralized control,collective emergent behavior, etc. — hinder the evolutionof the technology from academic institutions to real-worldproblems.

Blockchain, an emerging technology originated in theBitcoin field, demonstrates that by combining peer-to-peer networks with cryptographic algorithms a group ofagents can reach an agreement on a particular state ofaffairs and record that agreement without the need for acontrolling authority. The combination of blockchain withother distributed systems, such as robotic swarm systems,can provide the necessary capabilities to make roboticswarm operations more secure, autonomous, flexible andeven profitable.

This work explains how blockchain technology canprovide innovative solutions to four emergent issues inthe swarm robotics research field. New security, decisionmaking, behavior differentiation and business models forswarm robotic systems are described by providing casescenarios and examples. Finally, limitations and possiblefuture problems that arise from the combination of thesetwo technologies are described.

I. THE BLOCKCHAIN: A DISRUPTIVETECHNOLOGY

In September 2008, Satoshi Nakamoto introducedtwo influential ideas in his white paper “Bitcoin:A Peer-to-Peer Electronic Cash System”1. The firstwas “Bitcoin” — a decentralized, peer-to-peer, on-line currency able to maintain value without anybacking from a central authority. After garnering anincreasing amount of attention from early adopters[27] and law makers [7], Bitcoin became recognizedas a cheap, rapid, and reliable method of movingeconomic value across the internet in a decentralizedmanner. With over 4 million users as seen in Fig.1(a)2, and over 125,000 transactions per day as seenin Fig. 1(b)3, Bitcoin has transformed into one ofthe most powerful computing networks in existence[12].

1http://bitcoin.org/bitcoin.pdf2Source: http://blockchain.info/charts/my-wallet-n-users3Source: http://blockchain.info/charts/n-transactions-total

(a)

(b)

Fig. 1. (a) Total number of users of the most popular Bitcoin client— MyWallet — during the Sep 2014 - Sep 2015 period. (b) Totalnumber of Bitcoin transactions during the Sep 2014 - Sep 2015 period.

Fig. 2. A simple section of a blockchain

The second, equally important idea was the“blockchain”, which is a public chronologicaldatabase of transactions recorded by a network ofagents. Individual transactions containing details ofwho sent what to whom are grouped into datasetsreferred to as “blocks”, as illustrated in Fig. 2.

Each block contains information about a certainnumber of transactions, a reference to the precedingblock in the blockchain, and an answer to a complexmathematical challenge known as the “proof ofwork”. The concept of proof of work is used tovalidate the data associated with that particular block

arX

iv:1

608.

0069

5v4

[cs

.RO

] 2

5 Ju

n 20

17

2

as well as to make the creation of blocks computa-tionally “hard”, thereby preventing attackers fromaltering the blockchain in their favor4. It is basedon cryptographic techniques — SHA256 in the caseof Bitcoin —, which output unpredictable numericvalues, also known as hashes, that encapsulate alltransactions within a block in a digital fingerprint.Any differences in the input data — transactionorder, quantities, receivers, etc. — will producedifferences in the output data — proof of work hash— and, thus, a different digital fingerprint.

Fig. 3. A graphical representation of the blockchain

After ensuring that all new transactions to beincluded in the block are valid and do not invalidateprevious transactions, e.g., through double-spending,a new block is added to the end of the blockchainby an agent in the network, hereafter referred to asa miner. At that moment, the information containedin the block can no longer be deleted or modified,and it is available to be certified by everyone on thenetwork. A copy of the blockchain, similar to theone illustrated in Fig. 3, is stored by every agentand is periodically synchronized in a peer-to-peerfashion to ensure that they all share the same pub-lic database. With these properties, the blockchainbecomes a permanent record that all agents on thenetwork can use to coordinate an action, verify anevent, and reach an agreement in an auditable waywithout the need for a centralized authority. How-ever, due to its decentralized nature, the blockchainsometimes produces orphaned blocks, depicted bythe grey blocks in Fig. 3, which occur naturallywhen two miners produce a block at a similar time.Initially accepted by a part of the network, theseblocks are later rejected when proof of a longerblockchain is received.

Several projects are currently exploring the po-tential benefits of blockchain technology in a wide

4Recently, new techniques, such as “proof of stake”, requiringno computational work to validate blocks, have been introducedto expand blockchain technology to resource-limited devices. Moreinformation about “proof of stake” systems can be found in: http://peercoin.net/assets/paper/peercoin-paper.pdf

range of sectors such as intellectual property, real es-tate, etc. [36]. Beyond this, two of the most promis-ing projects concerning blockchain technology areBitcongress5 and Colored Coins6. Bitcongress is adecentralized voting platform intended for nations,states, or communities, to ease the legislation andrule-making process by providing a secure and au-ditable voting system. The Colored Coins project isfocused on creating digital assets that can representreal-world value. By attaching metadata to Bitcointransactions, digital tokens on the blockchain can beused to store information — documents, certificates,etc. —, provide prove of ownership rights, or issuefinancial assets such as shares. Due to the latter,“colored coins” can be used to create DistributedCollaborative Organizations (DCO), which are ba-sically virtual entities with shareholders. In thosesituations, the blockchain helps to keep track of acompany’s ownership structure, as well as to createand distribute shares for DCOs in a transparent andsecure way.

Blockchain technology demonstrates that by com-bining peer-to-peer networks with cryptographic al-gorithms, a group of agents can reach an agreementon a particular state of affairs and record that agree-ment in a secure and verifiable manner without theneed for a controlling authority. Due to its decentral-ized nature, and key underlying principles such asrobustness and fault-tolerance, blockchain technol-ogy may be useful in combination with emergentfields including automated transportation, logisticand warehouse systems or even cloud computing.The aim of this work is to outline the potentialbenefits of combining blockchain technology withrobotics — specifically, swarm robotics and state-of-the-art robotic hardware, which have garneredincreasing attention in both academic and industrialsectors — and to emphasize how this synergy canease the transition from academic research to real-world applications and eventual widespread indus-trial use.

II. SWARM ROBOTICS: THE EMERGENT FIELD

With a strong initial influence from nature andbio-inspired models [30], [48], [5], swarm systemsare known for their adaptability to different environ-ments [4] and tasks [6]. Key advantages of roboticswarms are robustness to failure and scalability, bothof which are due to the simple and distributed natureof their coordination. Due to these characteristics,global behaviors are not explicitly stated, and insteademerge from local interactions between robots. As

5http://bitcongress.org/6http://coloredcoins.org/

3

Fig. 4. Total number of research documents on swarm roboticsystems published annually from 2000 - 2014.

a result, swarm robotics research has recently beengaining popularity, as demonstrated in Fig. 47.

As the cost of robotic platforms continues todecrease, the number of applications involving robotswarms is increasing. These include targeted ma-terial transportation [10], where groups of smallrobots are used to carry tall and potentially heavyobjects, precision farming [16], [50], where a fleet ofautonomous agents shift operator activities in agri-cultural tasks, and even entertainment systems [1],[19], where multiple robots come together to forminteractive displays. Several breakthroughs originat-ing in this field have had a direct impact on theemergence of technologies such as Unmanned AerialVehicles (UAVs) [8], [46] and nanorobotics [21],[31], [9].

These examples, along with the growing de-velopment of robotic hardware [38], [11], suggestthat commercial applications for swarms are withinreach. However, as new swarm robotics companies[13], [39] have started to emerge, it is clear that thereare problems in effectively transferring knowledgeand technology from academic institutions to theindustry [3]. Previous works have emphasized thelack of general methods to tackle topics such assafety analysis, testing mechanisms [49], [40] orsecurity protocols [18] for swarm robotic systems,which hinder the progress to more broader commer-cial applications.

One of the main axioms in the swarm roboticsfield has been the absence of global knowledgeor explicit communication models between swarmrobots. Traditionally, swarm robotic systems exclu-sively rely on local communication — e.g., betweenadjacent robots in a flocking mission —, and noglobal knowledge is maintained within the swarm.Therefore, the use of blockchain technology in com-bination with swarm robotic systems might be seenas a diversion from the main research approach.However, the use of global knowledge in swarm

7Source: Scopus research database.

(a)

(b)

Fig. 5. (a) A swarm of 1024 Kilobot [38] robots. The Kilobot robotdemonstrated that a low-cost platform — $14 worth of parts — canbe a viable solution for producing swarms of hundreds or thousandsof members. (b) Microtug robot carrying a weight. Microtug robot[11] towing a weight. Microtug robots use an innovative adhesivetechnology to move 2000 times their own weight.

robotic systems has been proved useful for differentapplications such as cooperative techniques to copewith unknown environments [20] or the synchroniza-tion between different swarm teams [25].

These findings suggest that the combination ofboth types of information — local and global— might co-exist [3] without compromising therobustness to failure and scalability properties ofthese systems. In addition, recent achievements inhardware design and manufacturing, such as theRaspberry Pi8 or Intel Galileo9 motherboards, enablenowadays robots to count with increasing processingcapabilities as well as low-power communicationdevices. These advancements open the door to in-clude explicit communication and global knowledgemodels in swarm robotic systems.

In the following, I will discuss how blockchain

8http://www.raspberrypi.org/9http://www.intel.com/content/www/us/en/embedded/products/

galileo/galileo-overview.html

4

and its underlying principles can be useful fortackling four emergent issues in the swarm roboticsfield by using the robots as nodes in a network andencapsulating their transactions in blocks.

A. Security

One of the main obstacles to the large-scaledeployment of robots for commercial applicationsis security. Previous research has highlighted thenecessity of developing systems in which swarmmembers can detect and trust their counterparts[52]. This is especially important, since it has beendemonstrated that the inclusion of swarm membersthat are “faulty” or have malicious intentions couldbe a potential risk for the swarm’s goals [28] as wellas a security breach.

Security in any environment, including swarmrobotic systems, is fundamentally about the provi-sion of core services such as data confidentiality,data integrity, entity authentication, and data originauthentication. In contrast to other fields in whichsecurity-related research is being actively conducted,swarm robotic systems suffer a lack of practical so-lutions to these problems [18]. The security topic hasbeen overlooked by state-of-the-art research mainlydue to the complex and heterogeneous characteris-tics of robotic swarm systems — robot autonomy,decentralized control, a large number of members,collective emergent behavior, etc. Technology suchas blockchain can provide not only a reliable peer-to-peer communication channel to swarm’s agents,but are also a way to overcome potential threats,vulnerabilities, and attacks.

Fig. 6. Different types of robots share the blockchain communicationchannel using their public keys as main identifiers.

In the blockchain encryption scheme, techniquessuch as public key and digital signature cryptog-raphy are accepted means of not only makingtransactions using unsafe and shared channels, butalso of proving the identity of specific agents in anetwork. A pair of complementary keys, public and

private, are created for each agent to provide thesecapabilities, as illustrated in Fig. 6. Public keys arean agent’s main accessible information, are publiclyavailable in the blockchain network, and can beregarded as a special type of account number. Incontrast, private keys are an agent’s secret informa-tion — similar to passwords in traditional systems— and are exclusively used to validate an agent’sidentity and the operations that it may execute.

Fig. 7. Public key cryptography allows robots to be sure thatthe content of a message can only be read by the owner of thecorresponding sending address.

In the case of swarm robotics, public key cryp-tography as depicted in Fig. 7 allows robots toshare their public keys with other robots who wantto communicate with them. Therefore, any robotin the network can send information to specificrobot addresses, knowing that only the robot thatpossesses the matching private key can read themessage. Since the public key cannot be used todecrypt messages, there is no risk if it falls intothe wrong hands. In addition, it prevents third-partyrobots from decrypting such information even if theyshare the same communication channel.

Fig. 8. Digital signature cryptography provides a way to prove theownership of a specific address — public key.

Complementing the above, digital signature cryp-tography, as illustrated in Fig. 8, allows robots to use

5

their own private key to encrypt messages. Otherrobots can then decrypt them using the sender’spublic key. As any robot has access to the sender’spublic key, the contents of the message will not bea secret, but the fact that it was encrypted using thesender’s private key proves that the message couldnot have been sent by anyone else, thereby provingits authorship.

On one hand, public key cryptography ensuresthat the content of a message — encapsulated ina blockchain transaction, for instance — can onlybe read by the robot owning a specific address.On the other hand, digital signature cryptographycan provide entity authentication and data originauthentication between robots or third-party agents.

Applications that can potentially benefit from thesecurity features provided by blockchain technologyinclude the military [26], where the need for reliableand trustworthy systems is self-evident, and disasterrelief [24], [41], where accurate identification be-tween aid agencies is crucial, especially in the casewhere multiple swarms are in joint operation [42].Another relevant example is the I-ward project [44],in which robot teams provide assistance to health-care workers in the transportation of medicines andpatients’ medical records. In this case, entity authen-tication and data confidentiality may be the mostimportant security requirements.

B. Distributed decision making

Distributed decision making algorithms haveplayed a crucial role in the development of swarmsystems. One of the most prominent examples isthe use of robot swarms connected through ad-hoc networks — MANET — [15], [23] to achievedistributed sensing applications. These systems havethe capability to sense information from multipleviewpoints and, thus, increase the quality of dataobtained. However, the robots in the swarm needto reach a global agreement regarding the object ofinterest — e.g., paths to traverse, shape to form, orobstacles to avoid. Hence, there is a need to de-velop distributed decision making protocols [22] thatensure guaranteed convergence towards a commonoutcome.

Distributed decision making algorithms have beenadopted in many robotic applications, including dy-namic task allocation [14], collective map build-ing [2], and obstacle avoidance [32]. However, thedeployment of large quantities of agents with dis-tributed decision-making is still an open problem[35]. Several well-known trade-offs, such as speedversus accuracy during collective decision-makingprocesses, have been identified [17], [37], [45], andare a key aspect for consideration before real-world

deployments. Therefore, more autonomous and flex-ible solutions to robot decision making in distributedsystems are required to tackle the new wave ofchallenges facing the industry. Blockchain is an out-standing technology for ensuring that all participantsin a decentralized network share an identical viewof the world. For instance, blockchains allow for thepossibility of creating distributed voting systems forrobot swarms that need to reach an agreement.

Figure 9 outlines a simple example of howblockchain technology can be used to assist in thedecision making process of robotic swarms. Everytime a swarm member is in a situation requiringan agreement, it can issue a special transaction,creating an address associated with each of thepossible options the robotic swarm has to choosefrom, as shown in Fig. 9(a). After being includedin a block, the information is publicly available andother swarm members can vote according to theirsituation by, for example, transferring one token tothe address corresponding to their chosen option,as shown in Fig. 9(b). Agreements — e.g., by themajority rule — can be obtained rapidly and ina secure and auditable way since all robots canmonitor the balance of addresses involved in thevoting process as shown in Fig. 9(c).

Furthermore, the inclusion of blockchain technol-ogy in robotic swarms opens the path to achievingmore advanced collaborative models between robotsusing multi-signature (multisig) techniques. Multisigtechniques rely on addresses and transactions thatare associated with more than one private key. Thesimplest type of multisig address is called an m-of-n address — where m < n — , which is anaddress associated with n private keys that requiressignatures from at least m keys to transfer infor-mation. Complex collaborative missions especiallydesigned for heterogeneous groups of robots are easyto formalize, publish, and carry out in this way.

Figure 10 provides a simple overview of thepotential capabilities of multisig addresses in swarmcollaborative missions. In this case, an UnmannedTerrestrial Vehicle (UTV) with the need to avoidan obstacle — a river — can create a partiallysigned transaction representing a call for assistance,as shown in Fig. 10(a), and distribute it acrossthe network. At that moment, a suitable robot unitsuch as an Unmanned Aerial Vehicle (UAV), asshown in Fig. 10(b), or an Unmanned Underwa-ter Vehicle (UUV), as shown in Fig. 10(c), cansign its part of the transaction responding to thecall. This action will unlock information such asthe UTV’s position and even the tokens containedwithin the multisig address as payment to completethe action. Under this collaboration scheme, moreautonomous and emergent behaviors can arise within

6

(a)

(b)

(c)

Fig. 9. (a) One of the swarm members recognizes an object of interestduring the mission. In order to reach an agreement the swarm robotexecutes two transactions, creating two special addresses representingthe possible options and registering them in the blockchain. (b)The rest of the swarm gathers around the object to obtain differentperspectives. Each swarm member issues a new transaction to theaccount matching their classification algorithm. (c) When the votingprocess ends, the entire swarm reaches an agreement about the objectbased on the voting results.

(a)

(b)

(c)

Fig. 10. An Unmanned Terrestrial Vehicle (UTV) faces the problemof crossing a river bed. It creates a multisig address in which 2-of-3 keys are needed to establish the collaboration and solve theproblem. (b) Possible Solution 1: An available Unmanned AerialVehicle (UAV) provides its key to unlock the multisig address andretrieve the UTV position. The problem is solved by permitting theUAV to carry the UTV to the other side. (c) Possible Solution 2:An available Unmanned Underwater Vehicle (UUV) provides its keyto unlock the multisig address and retrieve the UTV’s position. Theaction is fulfilled by letting the UUV carry the UTV to the other side.

7

the robot swarm. For instance, robots in unfavorablecircumstances — e.g., low battery levels, poor sensorreadings, etc. — could be more reactive to requestsfor assistance from other robots who provide validtokens, doing so to improve their own situationwithin the swarm. Robots could purchase battery re-fills, obtain higher-quality sensor readings, or simplyrequest other services from other robots in order tomaximize their own, personal goals.

Finally, the adoption of blockchain technologyin the distributed decision processes of roboticswarms can provide additional benefits to the roboticswarms’ maintainers and operators. Due to the factthat all agreements and all related transactions arestored in the blockchain, there is no need to investtime in learning and training phases for new robotsjoining the swarm. Instead, these new robots will beable to automatically synchronize with the rest ofthe swarm by downloading the ledger containing thehistory of all agreements and knowledge previouslydiscovered and stored in the blockchain.

C. Behavior differentiation

The combination of blockchain technology withclassical swarm control techniques can be useful intackling problems beyond security and distributeddecision making issues. According to recent surveys[3], [6], even though state-of-the-art algorithms haveenabled specialized teams of robots to handle indi-vidual specific behaviors — aggregation, flocking,foraging, etc. —, robot swarms deployed in thereal world will likely need to handle a numberof different behaviors, for example, by switchingfrom one control algorithm to another to accom-plish a given objective. The combination of differ-ent behaviors in a swarm has not been diligentlystudied in the literature [3]. However, blockchaintechnology provides the possibility of linking severalblockchains in a hierarchical manner, also knownas pegged sidechains10, which would allow roboticswarm agents to act differently according to theparticular blockchain being used, where different pa-rameters, such as mining diversity, permissions, etc.,can be customized for different swarm behaviors.

For instance, open-source projects, such as Mul-tiChain11 in combination with pegged sidechain al-gorithms10, provide a simple way to create multipleblockchain ledgers connected to each other that areable to run in parallel. Figure 11(a) represents atypical blockchain configuration in which the miningdiversity — the possibility of becoming a miner— is distributed among network agents using a

10http://www.blockstream.com/sidechains.pdf11http://www.multichain.com/download/MultiChain-White-Paper.

pdf

(a)

(b)

Fig. 11. (a) A typical blockchain configuration in which all agentsin the network can become miners. This configuration emphasizesthe decentralized control approach since all robots help to buildthe blockchain ledger. (b) Several agents of an already establishedblockchain create a different blockchain ledger — sending a transac-tion to a special address — in which the mining diversity parameter ischanged to produce a single miner configuration. This configurationemphasizes a centralized approach in which only the miner can takecontrol of the block creation process, thus transforming the blockchaininto a leader-follower control scheme.

round-robin planner. In these situations, the controlbehind the decision regarding which transactionsbecome part of the blockchain is distributed anddecentralized.

However, several members of the swarm have thepossibility of creating a parallel pegged sidechainsimply by making a special type of transactionand transferring a small portion of their assets tothe alternative chain. In this sidechain, differentparameters can be optimized to obtain a differentbehavior. Figure 11(b) provides an overview of howa decentralized mining scheme can be turned intoa centralized mining scheme. In the bottom part ofFig. 11(b), a single agent that has monopoly over thetransactions included in the blockchain emphasizesa leader-follower control approach instead of a com-pletely decentralized model. Using this approach,different robot behaviors can be obtained using thesame robot’s control law, therefore, not increasingthe complexity of robot’s controller.

8

D. New business modelsAlthough this document has explored and empha-

sized several blockchain applications beyond cur-rency, it should be remembered that blockchaintechnology can also be seen as an ideal ApplicationProgramming Interface (API) for economic applica-tions, which may allow swarms of robots to directlytake part in an economy. For this reason, blockchaintechnology has the potential to stimulate the useof swarm robotics in industrial and market-basedapplications.

One of the most obvious prototypical imple-mentations regarding the use of robotic swarms ineconomic applications is the process of exchangingdata for currency between a robot and a requester.Sensing-as-a-Service (S2aaS) [34], [29], [33] is anemerging business model pattern, which is rising inthe Internet of Things (IoT) field. S2aaS helps tocreate multi-sided markets for sensor data in whichone or more customers — the markets’ buying side— subscribe to and pay for data that is provided byone or more sensors — selling side.

This model, which was initially designed to matchthe characteristics of sensor networks distributed insmart cities and controlled areas [51], can be ex-tended with the use of robot swarms to develop moreresilient and adaptive mission control for whatevertarget application the user may desires.

Fig. 12. Process of exchanging data for currency in a Sensing-as-a-Service model.

Figure 12 outlines a possible working model inwhich swarm robotics and blockchain technologyare combined to develop effective S2aaS applica-tions. In Fig. 12, (1) individual robots register intoa swarm where they can be found by a requester.Robotic swarms in this case can be regarded as a listof addresses where additional information, includingthe location of each agent, price of data provided,etc., can be found. In more advanced scenarios,robotic swarms can even build Decentralized Collab-orative Organizations (DCOs) like those mentionedin the first section of this document. (2) The re-quester can ask for a complete list of these robotsand their sensing services, (3) which is sent back by

the swarm based on the robots currently available. Ifthe requester decides to purchase the sensing serviceprovided by a specific robot, (4) he/she can sendits corresponding payment directly to the robot’spublic address. (5) This initial transaction is includedin the blockchain and a payment notification issent to the corresponding sensing robot. (6) At thispoint, the hired robot can start working and senda transaction containing the sensing data. Previousresearch [53] in privacy-oriented blockchain appli-cations has demonstrated that encrypting links to anoff-chain site with the requester’s public key andencapsulating them in the data field of a transactionprevents blockchain’s congestion and ensures thatonly the requester can read the intended message.(7) Finally, the requester can obtain access to his/herpaid data through the transaction sent by the sensingrobot.

This IoT-swarm model may be relevant for differ-ent types of private organizations. For example, carmanufacturers may require information about roadconditions, especially when bad weather conditionsarise or in disaster-relief missions where first-handinformation is crucial. Furthermore, farmers andprecision agriculture/aquaculture companies may re-quire accurate weather forecasts for large productionareas where different types of robots are able toprovide a global view.

Finally, blockchain technology can be crucial insituations in which multiple swarms from competitorcompanies have to coexist in the same environment,such as in mining scenarios, intelligent transporta-tion environments, or search & rescue missions. Thepossibility that different company systems can sharea secure communication medium in which the trans-action order and timestamps are taken into accountopens a path to providing a suitable framework forcompetitive swarm systems.

Fig. 13. A UUV discovers several objects of interest — e.g., treasure,mineral resources, or archaeological findings — and files a discoveryform claiming rights over the discovered objects.

9

Robotic agents that have been programmed tofind resources, objects, tokens, etc., as part of theiractivities may be able to claim ownership or ex-ploitation rights on behalf of their owner. Figure 13outlines a simple deep seabed exploration scenarioin which a UUV discovers several objects of interestand files a discovery document claiming rights overthem. The document may contain key informationabout the discovery, including the location of thediscovered objects, preliminary descriptions and re-ports, and even graphical data. After calculatingthe document’s hash, this can be included into ablockchain’s transaction and stored in the publicledger as a proof of discovery.

This is possible due to two powerful blockchaintechniques known as hashing and time-stamping. Asmentioned above, a hash string can act as a uniqueand private identifier for a piece of informationor a file’s contents. The hash represents the exactcontent of an original piece of information, muchlike a digital fingerprint. Furthermore, the hash isshort enough to be included as text in a blockchaintransaction, thus providing a secure time-stampingfunction of when a specific attestation transactionoccurred. Via the hashing functionality, the orig-inal document content can be encoded into theblockchain without being disclosed. In this way, theblockchain can be used to prove the existence of theexact contents of a document or other digital asset ata certain time. Whenever a proof of existence needsto be confirmed, if the recomputed hash is the sameas the original hash registered in the blockchain, thedocument can be verified as unchanged.

In this sense, blockchain technology may providean infrastructure for ensuring that robotic swarmsystems follow specified legal and safety regulationsas they become increasingly integrated into humansociety, and may result in the creation of newbusiness models for swarm operation.

III. LIMITATIONS AND PROBLEMS TO OVERCOME

Even though the combination of blockchain tech-nology and swarm robotics can provide useful solu-tions to tackle the aforementioned issues, a numberof technical challenges related to the blockchainhave been identified [43] and shall require investiga-tion by future researchers. Solutions to these issuesmight not have a direct impact on the developmentof new services and businesses based on blockchaintechnology per se; however, they would be necessarysteps towards mainstream adoption.

A. LatencyCurrently, with the most widely used version of

the blockchain — Bitcoin — a block takes around

10 minutes to be processed. This means that atransaction takes approximately 10 minutes to beconfirmed. Even though this rule can be modifiedin private blockchains via the addition of differentmining policies, such as proof-of-stake, users in theBitcoin network normally wait until two or threeblocks are appended to the blockchain to confirmtheir transactions. This way users decrease their riskof suffering a double-spending attack. Therefore,latency appears in the form of the time differencebetween the moment a transaction is sent and themoment it is confirmed.

The latency issue becomes highly relevant whenrobots are used in formation control or cooperativetasks. In these situations, fast and reliable infor-mation is required to orchestrate the movements ofthe swarm. Collisions or other inconveniences mightarise in situations when there is a mismatch betweenthe current state of affairs and the one in which thetransaction was originated.

Innovative research is needed to address the la-tency issue and to investigate which applicationsare most suited for both ends of the security vs.speed trade-off. One possible solution to mitigatethis problem might be to create affiliation-basedsystems in which robots belonging to the sameorganization or company are not required to waitlong periods of time to accept or process transactionsamong themselves. A reputation system could beconstructed from lists of previous accepted transac-tions within the group to cut these waiting times.

B. Size, throughput and bandwidth

If large quantities of robots are deployed for longperiods of time, they might expand the blockchainto a point where they cannot keep a copy of thefull ledger of transactions anymore. This problem,which the Bitcoin community calls “bloat” [47], isof particular importance in swarm robotics wheresimple robots with limited hardware capabilities areused.

Private blockchains, such as the ones presented inthis report, are intended to have a relatively smallsize. However, the reality is that if a blockchainwere scaled to function in mainstream applications,it would need to be big enough to allocate severaltypes of information.

Future researchers in the blockchain field have totrial different accessibility methods to find which isthe most suitable for obtaining information from ablockchain. New interfaces such as Chain12 may beable to facilitate automated calls to a blockchain byproviding address balances and balance change, as

12http://chain.com/index.html

10

well as notifying agents when new transactions orblocks are created on the network.

Even though important parameters such as theblock size — how many transactions are includedin each block — can be changed, it is importantto note that the most widely used blockchain im-plementation can only handle a maximum of seventransactions per second [43]. This limitation severelycompromises the throughput of the system in busynetworks with a large number of agents. One way totackle this issue is to raise the number of transactionsa block can contain. However, this leads to otherissues related to blockchain size and bloat. Anothersolution would be to create parallel blockchainswhere block size and frequency parameters are op-timized for different types of information.

IV. CONCLUSIONS

Blockchain technology demonstrates that by com-bining peer-to-peer networks with cryptographic al-gorithms, a group of agents can reach a agreementon a particular state of affairs, and can recordthat agreement in a verifiable manner without theneed for a controlling authority. Even though thistechnology is in its infancy, it is already capable ofextended functionalities outside its original applica-tion, and shows promise for the creation of state-of-the-art models in combination with other emergingtechnologies.

Due to the latest advances in the field, swarmrobotic systems have been gaining popularity inthe last few years and are expected to reach themarket in the near future. However, several of thecharacteristics that make them ideal for certain fu-ture applications —– robot autonomy, decentralizedcontrol, collective emergent behavior, etc. — hinderthe evolution of the technology from academic insti-tutions to use in real-world problems, and eventuallyto widespread industrial use.

In this work, we discussed how the combinationof blockchain technology and swarm robotic systemscan provide innovative solutions to four emergentissues, by using the robots as nodes in a network andencapsulating their transactions in blocks. First, newsecurity models and methods can be implemented inorder to give data confidentiality and entity valida-tion to robot swarms, therefore making them suitablefor trust-sensitive applications. Second, distributeddecision making and collaborative missions can beeasily designed, implemented, and carried out byusing special transactions in the ledger, which enablerobotic agents to vote and reach agreements. Third,robots may be able to function in diverse and chang-ing environments if their operation corresponds todifferent blockchain ledgers that use different param-eters, without any change in their control algorithm.

In short, these improvements would increase robots’flexibility without increasing the complexity of theswarm design. Finally, blockchain technology mayprovide an infrastructure for ensuring that roboticswarm systems follow specified legal and safety reg-ulations as they become increasingly integrated intohuman society, and could even result in the creationof new business models for swarm operation.

The addition of blockchain models to roboticswarms does have its limitations, and some criticsmight see it as a deviation from the minimalistic ap-proach usually followed in swarm robotics research.This path is subject to debate, and decisions on itmust be made in relation with the state of the artin technology. A promising trend is recent advance-ments in low-power communication and processingchips, which give advanced capabilities robots inswarm-related activities and reduce their price, lead-ing to the possibility of obtaining “more-advanced”swarm robotic units.

In conclusion, the integration of blockchain tech-nology could be the key to serious progress inthe field of swarm robotics. This step could openthe door not only to new technical approaches,but also to new business models that make swarmrobotics technology suitable for innumerable marketapplications.

REFERENCES

[1] Javier Alonso-Mora, Roland Siegwart, and Paul Beardsley.Human - robot swarm interaction for entertainment: Fromanimation display to gesture based control. In Proceedings ofthe 2014 ACM/IEEE International Conference on Human-robotInteraction, HRI ’14, pages 98–98, New York, NY, USA, 2014.ACM.

[2] R. Aragues, J. Cortes, and C. Sagues. Distributed consensus onrobot networks for dynamically merging feature-based maps.Robotics, IEEE Transactions on, 28(4):840–854, Aug 2012.

[3] Levent Bayındır. A review of swarm robotics tasks. Neurocom-puting, August 2015.

[4] Carlos Bentes and Osamu Saotome. Dynamic Swarm Formationwith Potential Fields and A* Path Planning in 3D Environment.In 2012 Brazilian Robotics Symposium and Latin AmericanRobotics Symposium, pages 74–78. IEEE, October 2012.

[5] Eric Bonabeau, Marco Dorigo, and Guy Theraulaz. SwarmIntelligence: From Natural to Artificial Systems. 1999.

[6] Manuele Brambilla, Eliseo Ferrante, Mauro Birattari, and MarcoDorigo. Swarm robotics: a review from the swarm engineeringperspective. Swarm Intelligence, 7(1):1–41, January 2013.

[7] J Brito and Andrea Castillo. Bitcoin: A Primer for Policymakers.Mercatus Center: George Mason University., 29(4):3–12, 2013.

[8] Ugur Cekmez, Mustafa Ozsiginan, and Ozgur Koray Sahingoz.A UAV path planning with parallel ACO algorithm on CUDAplatform. In 2014 International Conference on UnmannedAircraft Systems (ICUAS), pages 347–354. IEEE, May 2014.

[9] S. Chandrasekaran and Dean F. Hougen. Swarm intelligence forcooperation of bio-nano robots using quorum sensing. In 2006Bio Micro and Nanosystems Conference, pages 104–104. IEEE,2006.

[10] Jianing Chen, M. Gauci, and R. Gross. A strategy for trans-porting tall objects with a swarm of miniature mobile robots.In Robotics and Automation (ICRA), 2013 IEEE InternationalConference on, pages 863–869, May 2013.

11

[11] D.L. Christensen, E.W. Hawkes, S.A. Suresh, K. Ladenheim,and M.R. Cutkosky. Enabling microrobots to deliver macroforces with controllable adhesives. In Robotics and Automation(ICRA), 2015 IEEE International Conference on, pages 4048–4055, May 2015.

[12] Reuven Cohen. Global Bitcoin Computing Power Now 256Times Faster Than Top 500 Supercomputers, Combined! -Forbes, November 2013. [Online; posted 28-November-2013].

[13] Raffaello D’Andrea. Guest Editorial: A Revolution in theWarehouse: A Retrospective on Kiva Systems and the GrandChallenges Ahead. IEEE Transactions on Automation Scienceand Engineering, 9(4):638–639, October 2012.

[14] G.P. Das, T.M. McGinnity, S.A. Coleman, and L. Behera. A fastdistributed auction and consensus process using parallel taskallocation and execution. In Intelligent Robots and Systems(IROS), 2011 IEEE/RSJ International Conference on, pages4716–4721, Sept 2011.

[15] K. Derr and M. Manic. Adaptive control parameters fordispersal of multi-agent mobile ad hoc network (manet) swarms.Industrial Informatics, IEEE Transactions on, 9(4):1900–1911,Nov 2013.

[16] Luis Emmi, Mariano Gonzalez-de Soto, Gonzalo Pajares, andPablo Gonzalez-de Santos. New Trends in Robotics for Agri-culture: Integration and Assessment of a Real Fleet of Robots.The Scientific World Journal, 2014:1–21, 2014.

[17] Nigel R. Franks, Anna Dornhaus, Jon P. Fitzsimmons, andMartin Stevens. Speed versus accuracy in collective decisionmaking. Proceedings of the Royal Society of London B:Biological Sciences, 270(1532):2457–2463, 2003.

[18] F. Higgins, A. Tomlinson, and K.M. Martin. Survey on securitychallenges for swarm robotics. In Autonomic and AutonomousSystems, 2009. ICAS ’09. Fifth International Conference on,pages 307–312, April 2009.

[19] Horst Hörtner, Matthew Gardiner, Roland Haring, ChristopherLindinger, and Florian Berger. Spaxels, pixels in space - A novelmode of spatial display. In SIGMAP and WINSYS 2012 - Pro-ceedings of the International Conference on Signal Processingand Multimedia Applications and International Conference onWireless Information Networks and Systems, Rome, Italy, 24-27 July, 2012, SIGMAP is part of ICETE - The InternationalJoint Conference on e-Business and Telecommunications, pages19–24, 2012.

[20] Aryo Jamshidpey and Mohsen Afsharchi. Advances in ArtificialIntelligence: 28th Canadian Conference on Artificial Intelli-gence, Canadian AI 2015, Halifax, Nova Scotia, Canada, June2-5, 2015, Proceedings, chapter Task Allocation in RoboticSwarms: Explicit Communication Based Approaches, pages 59–67. Springer International Publishing, Cham, 2015.

[21] Boonserm Kaewkamnerdpong and Peter J. Bentley. ModellingNanorobot Control Using Swarm Intelligence: A Pilot Study.pages 175–214. 2009.

[22] Tao Li, Minyue Fu, Lihua Xie, and Ji-Feng Zhang. Distributedconsensus with limited communication data rate. AutomaticControl, IEEE Transactions on, 56(2):279–292, Feb 2011.

[23] Yong Li, Shufei Du, and Younghan Kim. Robot swarm manetcooperation based on mobile agent. In Robotics and Biomimetics(ROBIO), 2009 IEEE International Conference on, pages 1416–1420, Dec 2009.

[24] Bailong Liu, Pengpeng Chen, and Guanjun Wang. A Model ofRescue Task in Swarm Robots System. In 2013 InternationalConference on Computational and Information Sciences, pages1296–1299. IEEE, June 2013.

[25] Stephen M. Majercik. Self-Organizing Systems: 6th IFIP TC 6International Workshop, IWSOS 2012, Delft, The Netherlands,March 15-16, 2012. Proceedings, chapter Initial Experiments inUsing Communication Swarms to Improve the Performance ofSwarm Systems, pages 109–114. Springer Berlin Heidelberg,Berlin, Heidelberg, 2012.

[26] Christopher J.R. McCook and Joel M. Esposito. Flocking forHeterogeneous Robot Swarms: A Military Convoy Scenario. In2007 Thirty-Ninth Southeastern Symposium on System Theory,pages 26–31. IEEE, March 2007.

[27] Robert McMillan. World’s First Bitcoin ATM Set to Go Live

Tuesday - WIRED, October 2013. [Online; posted 25-October-2013].

[28] Alan G. Millard, Jon Timmis, and Alan F. T. Winfield. TowardsExogenous Fault Detection in Swarm Robotic Systems. pages429–430. 2014.

[29] R. Mizouni and M. El Barachi. Mobile phone sensing as aservice: Business model and use cases. In Next Generation Mo-bile Apps, Services and Technologies (NGMAST), 2013 SeventhInternational Conference on, pages 116–121, Sept 2013.

[30] Sifat Momen and Amanda J C Sharkey. From ants to robots :A decentralised task allocation model for a swarm of robots. InCyrille Bertelle, Gerard H E Duchamp, and Rawan Ghnemat,editors, Proceedings of the Swarm Intelligence Algorithms andApplications Symposium, number April, pages 3–11, 2010.

[31] Touchakorn Nantapat, Boonserm Kaewkamnerdpong, TiraneeAchalakul, and Booncharoen Sirinaovakul. Best-So-Far ABCBased Nanorobot Swarm. In 2011 Third International Confer-ence on Intelligent Human-Machine Systems and Cybernetics,volume 1, pages 226–229. IEEE, August 2011.

[32] Iñaki Navarro and Fernando Matía. A framework for thecollective movement of mobile robots based on distributeddecisions. Robotics and Autonomous Systems, 59(10):685 – 697,2011.

[33] K. Noyen, D. Volland, D. Wörner, and E. Fleisch. When MoneyLearns to Fly: Towards Sensing as a Service Applications UsingBitcoin. ArXiv e-prints, September 2014.

[34] Charith Perera, Arkady B. Zaslavsky, Peter Christen, and Dim-itrios Georgakopoulos. Sensing as a service model for smartcities supported by internet of things. CoRR, abs/1307.8198,2013.

[35] S. Pourmehr, V.M. Monajjemi, R. Vaughan, and G. Mori. Youtwo! take off! : Creating, modifying and commanding groupsof robots using face engagement and indirect speech in voicecommands. In Intelligent Robots and Systems (IROS), 2013IEEE/RSJ International Conference on, pages 137–142, Nov2013.

[36] Giulio Prisco. Bitcoin Governance 2.0: Let’s Block-Chain Them- CCN: Financial Bitcoin & Cryptocurrency News, October2014. [Online; posted 13-October-2014].

[37] Wei Ren, R.W. Beard, and E.M. Atkins. A survey of consensusproblems in multi-agent coordination. In American ControlConference, 2005. Proceedings of the 2005, pages 1859–1864vol. 3, June 2005.

[38] M. Rubenstein, C. Ahler, and R. Nagpal. Kilobot: A low costscalable robot system for collective behaviors. In Robotics andAutomation (ICRA), 2012 IEEE International Conference on,pages 3293–3298, May 2012.

[39] A Ruckelshausen, P Biber, M Dorna, H Gremmes, R Klose,A Linz, F Rahe, R Resch, M Thiel, D Trautz, et al. Bonirob–anautonomous field robot platform for individual plant phenotyp-ing. Precision agriculture, 9(841):1, 2009.

[40] Erol Sahin and Alan Winfield. Special issue on swarm robotics.Swarm Intelligence, 2(2):69–72, 2008.

[41] D.P. Stormont. Autonomous rescue robot swarms for firstresponders. In CIHSPS 2005. Proceedings of the 2005 IEEEInternational Conference on Computational Intelligence forHomeland Security and Personal Safety, 2005., pages 151–157.IEEE, 2005.

[42] D.P. Stormont, A. Bhhatt, B. Boldt, S. Skousen, and M.D.Berkemeier. Building better swarms through competition:lessons learned from the AAAI/robocup rescue robot compe-tition. In Proceedings 2003 IEEE/RSJ International Confer-ence on Intelligent Robots and Systems (IROS 2003) (Cat.No.03CH37453), volume 3, pages 2870–2875. IEEE, 2003.

[43] Melanie Swan. Blockchain : Blueprint for a new economy.chapter 6, pages 83–86. O’Reilly Media, Sebastopol CA, 2015.

[44] Simon Thiel, Dagmar Habe, and Micha Block. Co-operativerobot teams in a hospital environment. In 2009 IEEE Inter-national Conference on Intelligent Computing and IntelligentSystems, volume 2, pages 843–847. IEEE, November 2009.

[45] Gabriele Valentini, Heiko Hamann, and Marco Dorigo. Efficientdecision-making in a self-organizing robot swarm: On thespeed versus accuracy trade-off. In Proceedings of the 2015

12

International Conference on Autonomous Agents and MultiagentSystems, AAMAS ’15, pages 1305–1314, Richland, SC, 2015.International Foundation for Autonomous Agents and Multia-gent Systems.

[46] Gervasio Varela, Pilar Caamamo, Felix Orjales, Alvaro Deibe,Fernando Lopez-Pena, and Richard J. Duro. Swarm intelligencebased approach for real time UAV team coordination in searchoperations. In 2011 Third World Congress on Nature and Bi-ologically Inspired Computing, pages 365–370. IEEE, October2011.

[47] Andrew Wagner. Ensuring network scalibility: How to fightblockchain bloat - bitcoin magazine, November 2014. [Online;posted 6-November-2014].

[48] J H Walker and M S Wilson. Task allocation for robots usinginspiration from hormones. Adaptive Behavior, 19(3):208–224,2011.

[49] Alan FT Winfield, Christopher J Harper, and Julien Nembrini.Towards dependable swarms and a new discipline of swarmengineering. In Swarm robotics, pages 126–142. Springer, 2005.

[50] Sajjad Yaghoubi, Negar Ali Akbarzadeh, Shadi SadeghiBazargani, Sama Sadeghi Bazargani, Marjan Bamizan, andMaryam Irani Asl. Autonomous robots for agricultural tasks andfarm assignment and future trends in agro robots. InternationalJournal of Mechanical and Mechatronics Engineering, 13(3):1–6, 2013.

[51] Y. Zhang and J. Wen. An iot electric business model basedon the protocol of bitcoin. In Intelligence in Next GenerationNetworks (ICIN), 2015 18th International Conference on, pages184–191, Feb 2015.

[52] I. A. Zikratov, I. S. Lebedev, A. V. Gurtov, and E. V. Kuzmich.Securing swarm intellect robots with a police office model.In 2014 IEEE 8th International Conference on Application ofInformation and Communication Technologies (AICT), pages 1–5. IEEE, October 2014.

[53] G. Zyskind, O. Nathan, and A. Pentland. Decentralizing privacy:Using blockchain to protect personal data. In Security andPrivacy Workshops (SPW), 2015 IEEE, pages 180–184, May2015.