# Trustful _Coopetitive_ Infrastructures for the New Space Exploration Era

Loïck Chovet  0000-0003-4025-9095 University of LuxembourgSpaceR Research
Group, SnT29, Avenue J.F KennedyKirchbergL-1855Luxembourg<EMAIL_ADDRESS>,  Renan Lima Baima  0000-0002-6281-8153 ,  Eduard Hartwich
0000-0001-7625-2085 University of LuxembourgFINATRAX Research Group, SnT29,
Avenue J.F KennedyKirchbergL-1855Luxembourg<EMAIL_ADDRESS><EMAIL_ADDRESS>,  Abhishek Bera  0000-0002-0196-5969 University of
LuxembourgSpaceR Research Group, SnT29, Avenue J.F
KennedyKirchbergL-1855Luxembourg<EMAIL_ADDRESS>,  Johannes Sedlmeir
0000-0003-2631-8749 University of LuxembourgFINATRAX Research Group, SnT29,
Avenue J.F KennedyKirchbergL-1855Luxembourg<EMAIL_ADDRESS>,
Gilbert Fridgen  0000-0001-7037-4807 University of LuxembourgFINATRAX Research
Group, SnT29, Avenue J.F KennedyKirchbergL-1855Luxembourg
<EMAIL_ADDRESS>and  Miguel Angel Olivares Mendez  0000-0001-8824-3231
University of LuxembourgSpaceR Research Group, SnT29, Avenue J.F
KennedyKirchbergL-1855Luxembourg<EMAIL_ADDRESS>
(2024; 29 September 2023; 10 December 2023; 05 January 2024)

###### Abstract.

In the new space economy, space agencies, large enterprises, and start-ups aim
to launch space multi-robot systems (MRS) for various in-situ resource
utilization (ISRU) purposes, such as mapping, soil evaluation, and utility
provisioning. However, these stakeholders’ competing economic interests may
hinder effective collaboration on a centralized digital platform. To address
this issue, neutral and transparent infrastructures could facilitate
coordination and value exchange among heterogeneous space MRS. While related
work has expressed legitimate concerns about the technical challenges
associated with blockchain use in space, we argue that weighing its potential
economic benefits against its drawbacks is necessary. This paper presents a
novel architectural framework and a comprehensive set of requirements for
integrating blockchain technology in MRS, aiming to enhance coordination and
data integrity in space exploration missions. We explored distributed ledger
technology (DLT) to design a non-proprietary architecture for heterogeneous
MRS and validated the prototype in a simulated lunar environment. The analyses
of our implementation suggest global ISRU efficiency improvements for map
exploration, compared to a corresponding group of individually acting robots,
and that fostering a _coopetitive_ environment may provide additional revenue
opportunities for stakeholders.

Blockchain, digital platform, in-situ resource utilization, open science,
multi-robot system, space economy

††copyright: rightsretained††doi: 10.1145/3605098.3635887††isbn:
979-8-4007-0243-3/24/04††conference: The 39th ACM/SIGAPP Symposium on Applied
Computing; April 8–12, 2024; Avila, Spain††journalyear: 2024††article:
4††price: 15.00††booktitle: The 39th ACM/SIGAPP Symposium on Applied Computing
(SAC ’24), April 8–12, 2024, Avila, Spain††ccs: Computing methodologies
Distributed computing methodologies††ccs: Applied computing Aerospace††ccs:
Applied computing Information integration and interoperability††ccs: Applied
computing Cross-organizational business processes††ccs: Computer systems
organization Peer-to-peer architectures††ccs: Computer systems organization
Self-organizing autonomic computing††ccs: Computer systems organization
Robotic autonomy††ccs: Computer systems organization External interfaces for
robotics

DLT

    distributed ledger technology
ISRU

    in-situ resource utilization
MRS

    multi-robot systems
ESA

    european space agency
ESRIC

    european space resources innovation centre
NFT

    non-fungible tokens
IPFS

    InterPlanetary File System
DSR

    design science research
DSRM

    design science research method
EL3

    european cooperative large logistic lander

##  1\. Introduction

The space industry has experienced substantial growth in recent years, pushed
by various government policies, international agreements (Leon, 2018), and
private investments, accounting for 80 % of the sector’s revenue over the last
16 years (Conn, 2021). Major space agencies, such as NASA (2016), have
emphasized the emergence of long-term human presence on space constellations
and MRS lunar missions for destination reconnaissance, resource prospecting,
and mapping (Crawford, 2015; Schuster et al., 2020; Arm et al., 2023; Lorenz,
2020), also known as ISRU. Combining this tendency with recent regulatory
framework changes — shifting from a strict antitrust approach to a more
consumer-focused free market (Orlova et al., 2020) — highlights the potential
for further expansion and advancement toward a market-based space exploration
approach. As the space industry moves towards decentralization and a
collaborative approach, there is a growing need to explore innovative
solutions that enable efficient coordination among multiple robots (Quinton et
al., 2023).

MRS involve groups of robots working together or supporting each other to
accomplish specific tasks (Parker, 2008). These systems can involve
_homogeneous_ or _heterogeneous_ groups of robots and are traditionally
categorized by their level of goal similarity, awareness of each other, and
interaction as _collective_ , _cooperative_ , or _collaborative_ (Parker,
2008). The emergence of _coopetitive_ systems, where competing agents
simultaneously choose to cooperate owing to economic incentives, further
expands the possibilities of MRS (Zhang et al., 2022) in ISRU driven
explicitly by economic incentives and shared objectives. Information-sharing
protocols and market-based approaches can often improve coordination among MRS
and robots’ resource utilization, cost-effectiveness, and exploration
capabilities (Quinton et al., 2023). These systems can effectively mitigate
adverse selection caused by information asymmetries — situations where few
market participants know more about products (e.g., water or iron positions)
or service quality (e.g., mapping data) than others (Akerlof, 1970), and as
such, contribute to efficiency in the market (Hartwich et al., 2023b) in line
with the growing acceptance of this technology in space missions (European
Space Agency, 2019).

Coordinating MRS in a market-based approach towards ISRU presents significant
challenges due to the involvement of multiple competing entities and nations,
with more than 60 countries involved in space activities (Borowitz, 2019).
Additionally, the legal (Smith et al., 2020) and technical requirements for
planetary mobility systems further complicate the coordination of MRS in space
exploration (Lorenz, 2020). This work’s technical and economic foundations
highlight the need for robust and adaptable ISRU systems that disincentivize
undesirable behavior (Leon, 2018).

Additionally, stakeholders involved in space missions highly value the
achievement of being the first to explore and acquire space and scientific
data, so companies should aim to make mission outputs (e.g., videos, images,
and audio) broadly accessible (e.g., via the web and media) (Cameron et al.,
2011). This perspective resonates with the principles of open science, which
promotes knowledge sharing, transparency, collaboration, and accessibility in
research (McKiernan et al., 2016). The evolving role of space ecosystems in
shaping the future of space exploration (Orlova et al., 2020) underscores the
need for a decentralized (i.e., non-proprietary), trustworthy, and transparent
digital platform. Such a platform can facilitate the seamless exchange of
information and value among stakeholders (Höß et al., 2021), enabling
autonomous MRS coordination for resource trading. DLT-based systems might
offer a suitable solution for this specific requirement (Butijn et al., 2020)
and, as a consequence, have been considered the foundation of both space MRS
(Filippi and Leiter, 2021) and open science platforms (Coelho et al., 2021).

DLT offers several advantages for machine cooperation, including in MRS
settings (Zivic et al., 2019; Hartwich et al., 2023b). These platforms can
automate tasks such as bidding for resource usage, publicly broadcasting
resource acquisition, and facilitating immediate operational cost
compensation. With a DLT-based digital platform, entities can provide idle
robot resources and stack intermediary profitable tasks to automatically
compensate operational costs and openly recognize pioneering exploratory
participants, thus increasing ISRU efficiency and enabling lower-cost space
exploration. Despite the advantages, DLT in space MRS is not without
challenges. For instance, space robots face harsh conditions and limited
resources, which conflict with the inefficient information processing of
blockchains’ intensive computation and storage replication (Butijn et al.,
2020; Guggenberger et al., 2022). Furthermore, despite stakeholders’ interest
in open science (Cameron et al., 2011), replicated information processing must
still be aligned with the need to protect the sensitive business information
of robots or organizations exposed through transactional (meta-) data
(Sedlmeir et al., 2022).

Therefore, tensions exist between the opportunities and challenges of using
DLT for space MRS, requiring closer investigation. This paper aims to
contribute to this understanding by designing an architecture for
_coopetitive_ MRS for ISRU, focusing on facilitating open science through DLT.
We drew upon the ESA-ESRIC space resources challenge as a specific use case
(Link et al., 2021) of mapping exploration and applied DLT to coordinate
automated cross-organizational economic interactions effectively. Our study
investigates the technical feasibility of leveraging DLT to enable
participation from universities, research institutes, and small companies in
low-cost explorative scientific research. Such a decision-making platform must
be holistically assessed to determine whether or not it can improve the global
efficiency of ISRU (Dias et al., 2006; Orlova et al., 2020), maintain targeted
information symmetry, and compensate space companies to create additional
revenue streams for their (idle) robotic resources. Hence, our research
question (RQ) is as follows:

RQ: Can an ISRU platform architecture based on DLT address the challenges of
coordinating _coopetitive_ space MRS mapping for open science?

In the evolving landscape of space exploration, which increasingly favors
collaborative approaches (Orlova et al., 2020), our case study, detailed in
Section 2, motivates the need for an architecture that empowers organizations
to utilize MRS capabilities for efficient ISRU data acquisition. Adopting the
design science research (DSR) method by Peffers et al. (Peffers et al., 2007)
to answer our RQ, we have conceptualized a decentralized (i.e., non-
proprietary) MRS platform. This platform, elucidated in Section 4, is grounded
in a thorough literature review, integrating insights from DLT (Butijn et al.,
2020), space MRS (Gao and Chien, 2017), DLT in robotics (Aditya et al., 2021),
DLT in distributed control and cooperative robots (Khan et al., 2019), and DLT
for the space industry (Ibrahim et al., 2021). As outlined in Section 4, we
develop a DLT-based _coopetitive_ MRS that supports space exploration,
primarily scientific ISRU mapping. These objectives were shaped by an in-depth
case study (Eisenhardt and Graebner, 2007) and a literature review to discern
the system’s requirements. By leveraging DLT, we strive to establish a
platform that ensures verifiable exchanges of information and value among
diverse, untrusted stakeholders. Our iterative development and evaluation
processes, described in Sections 4 and 5, have been instrumental in refining
the platform’s design to meet these objectives and address _coopetitive_ ISRU
challenges. The evaluations, conducted in three diverse environments, as
detailed in Section 5, have been pivotal in assessing the platform’s efficacy
and adaptability. The limitations and open challenges are discussed in Section
6. Section 7 highlights the platform’s potential to enhance the efficiency of
ISRU activities and create new revenue streams for stakeholders. Our findings
and supplementary resources will be publicly available on platforms like
GitHub and YouTube.

##  2\. Scenario: Requirements Elicitation

This section delves into our space mapping scenario that utilizes
decentralization and _coopetition_ to enhance efficiency in mapping
coordination among multiple robots. The purpose is to outline the scope and
specific use case we will refer to throughout the paper. Section 6 presents
further discussions and limitations of our artifact. We employed the
decentralized multi-robotic platform REALMS2 (Van Der Meer et al., 2023), it
uses three Leo Rovers® (Karalekas et al., 2020) and a LUVMI-X robot to
identify resources and analyze and map the environment (Garcet et al., 2019).

To illustrate this setting more precisely, we revisit the market-based mapping
scenario from Dias et al. (2006) and illustrate the result in Figure 1. This
approach, proven effective in patrolling, exploration, and pick-and-delivery
(Quinton et al., 2023), is grounded in market-based strategies for MRS. For
the sake of simplification, we are using exemplary amounts in the following
scenario. The celestial surface stratification follows the existing Goldberg
polyhedron approach (Goo et al., 2019) and uses the selenographic system to
refer to its surface positions. An initiator stationed on Earth, the service
orderer (SO), sends requests to a celestial stationed network of MRS service
providers (SP), as listed in Table 1. The SO proposes to pay a maximum of $50
for mapping the target region. SP D is 5 m away, while SP C is 10 m from the
target area. We consider an oversimplified abstraction of each robot’s cost
function, with a cost of $2 for each meter they travel. Consequently, SP D
wins the contract by bidding $10 compared to $20.

Figure 1. Moon’s Goldberg polyhedron diagram, each zone color represents a
company’s MRS operational area (Goo et al., 2019).

A diagram depicting the astrological constellation arranged in a Goldberg
polyhedron format. This surface comprises hexagons and pentagons and serves as
a geographical positional of the NFTs. Over the surface, various types of
symbolic robots and satellites represent the interacting MRS. Each system has
its name and color-coded areas (yellow, green, and blue) for easy
identification. The target to be mapped is color-coded as red.

Table 1. List of service providers.

SP | Color | Robotic Fleet | Price | Main Focus
---|---|---|---|---
A | Pink |  | Communications satellites,
---
Multiple antennas
- |  | Moon-earth
---
communications
B | Blue | Medium size offline fleet of robots | - |  Mapping
C | Green | Few robots w/ embedded sensors | $20 |  Resource analysis
D | Yellow | Large fleet of small robots | $10 |  | Fast mapping with
---
less precision
E | None | A single robot | $200 |  Mapping

The primary need of ISRU SOs is to locate and investigate resources from
celestial bodies (Cameron et al., 2011). This involves identifying areas with
water or ice and mapping out the geological features of the planet’s terrain.
This role could often be triggered and motivated by universities and research
institutions that seek to advance scientific knowledge and innovation in
space. However, the scarcity of resources available to SOs due to high budget
requirements for space-related research hampers advancements in this field.
Incomplete mapping data is also an issue owing to the limited coverage of
celestial geography (Crawford, 2015). Access to more precise information about
celestial resources and terrain would significantly improve scientific
outcomes (Lorenz, 2020).

The SPs, on the other hand, may represent start-ups (Crawford, 2015; Schuster
et al., 2020), large companies (des Satellites, 2021), space agencies (NASA,
2016), and organizations that already had some level of collaboration
internally but not necessarily with one another. These entities are willing to
work together to maximize their revenues and return on mission investment by
offering resources, such as idle robots, shelter time, or energy supply.
However, establishing trustworthy partnerships across various knowledge
domains (Kannengiesser et al., 2022), including international agreements
(Smith et al., 2020; Orlova et al., 2020) and individual initiatives (Israel,
2020; Conn, 2021; Schuster et al., 2020), poses a challenge. Their struggle
with inefficient use of exploration units, methods, and resources often
results in wasted opportunities and increased costs (Yan et al., 2013). The
most significant values of these entities are in guaranteeing mission
pioneering and promptly sharing exploratory data (Cameron et al., 2011). By
following open science principles, they can encourage collaboration and
innovation and gain access to valuable space exploration data (McKiernan et
al., 2016). We do not claim that these requirements are fully comprehensive or
sufficient, yet we consider them necessary.

##  3\. Foundations

Existing solutions for heterogenous MRS exploring the moon are still in their
development phase. As an example of current advancements, Corob-X (Dettmann et
al., 2022) represents a notable initiative in the domain of space robotics.
However, it is important to note that Corob-X is a proprietary solution that
relies on mutual collaboration and trust among stakeholders. In contrast, our
approach offers a non-proprietary, blockchain-based framework, addressing the
need for decentralized trust and open collaboration in MRS.

###  3.1. Market-based coordination for _coopetitive_ MRS and space

Coordinating multiple robots in market-based systems has been a focal point of
extensive research evaluating their quality, trade efficiency, and impact on
performance through theoretical analysis and experimental results (Dias et
al., 2006; Quinton et al., 2023). Centralized approaches often overlook the
complexities of cross-organizational interactions (Cánez et al., 2000). In
contrast, emerging distributed and decentralized market-based MRS offer
advantages in optimizing capital distribution, supply and demand matching, and
automated resource allocation while reducing risks associated with centralized
systems, such as single points of failure or direct peer-to-peer setups
(Kapitonov et al., 2018).

Auction-based mapping systems, though less complex than traditional solutions,
require greater participation and have been improved by integrating
contractual models to enhance efficiency in the space industry (Dias et al.,
2006; Belyaeva et al., 2021). Integrating these contracts into digital
platforms supported by reliable networks can enhance peer autonomy and
participation (Filippi and Leiter, 2021; Ibrahim et al., 2021). Given the
vulnerabilities of centralized systems to censorship and manipulation
(Calvaresi et al., 2018), combining market-based approaches with blockchain
technology offers a promising avenue for enhancing MRS coordination.

Decentralized approaches using DLT have emerged, with some employing
blockchain-based auction systems to determine resource usage in competitive
scenarios and ensure trustworthy services (Kapitonov et al., 2018). Such
approaches promote the efficiency, robustness, adaptability, fault tolerance,
effectiveness, and responsiveness of MRS (Kapitonov et al., 2018; Zlot et al.,
2002; Garcıa et al., 2018; García et al., 2020). However, fully distributed
systems sometimes produce suboptimal solutions (Dias et al., 2006; Quinton et
al., 2023). Thereby, given that the technical requirements for planetary
mobility, such as resource and latency constraints (Lorenz, 2020; Guggenberger
et al., 2022), add another layer of complexity, these challenges necessitate
innovative, real-time coordination solutions in ISRU scenarios. While existing
research has primarily focused on single-entity settings (Ferrer, 2019), there
remains a gap in the literature for blockchain and auction-based MRS solutions
in _coopetitive_ ISRU scenarios.

###  3.2. DLT for space

DLT provides decentralized transaction recording and validation across
multiple nodes, offering similar properties as common digital platforms but
without a central operator (Catalini and Gans, 2020). Blockchain, a subset of
DLT, is characterized by its replicated, append-only, hash-linked data
structures (Butijn et al., 2020). The technology allows for various levels of
openness regarding participation and access, ranging from permissionless to
permissioned and public to private networks (Beck et al., 2018). Blockchain’s
non-proprietary nature enhances trust among entities and enables programmable
value exchanges using fungible and non-fungible tokens (Hartwich et al.,
2023a). Smart contracts, often using standards like ERC20, facilitate
standardized transactions and programming logic (Meitinger, 2017; Ansari and
Kulkarni, 2020) Using blockchain technology to conduct direct peer-to-peer
transactions and facilitate smart contracts can reduce costs and increase
productivity in various industries (Afanasyev et al., 2019; Zivic et al.,
2019; Höß et al., 2021). Our work explores the potential benefits and
limitations of implementing DLT in the identified gap of MRS for ISRU. We
focus on enhanced data sharing and the automation of services using smart
contracts, thereby creating new revenue opportunities, such as offering robots
idle time and promoting non-discriminatory participation among stakeholders,
ultimately establishing a _coopetitive_ ISRU economy.

##  4\. Solution: requirements and architecture

We conducted a case study and literature review to collect design requirements
for our _coopetitive_ MRS based on DLT and focused on ISRU, as described here
and in sections 2 and 3. Although the requirement list is not final, we
integrated insights from the previous sections’ discussions to create a
comprehensive list addressing the unique challenges and complexities of
implementing DLT in an MRS for space. Our system architecture draws
inspiration from the works of Li et al. (2022), Abe et al. (2022), and
requirements from Lorenz (2020) and Cameron et al. (2011). It follows the
make-or-buy economic framework (Cánez et al., 2000) while prioritizing
transparency principles of open science (McKiernan et al., 2016) and the cost-
efficiency characteristics of _coopetitive_ settings (Ghobadi and D’Ambra,
2011). Our technical design enables geographically distributed robots to self-
coordinate (Garcıa et al., 2018) by using market-based strategies for service
provisioning (Quinton et al., 2023; Dias et al., 2006; Zlot et al., 2002).

###  4.1. Requirements analysis

Our design aims to generate new revenue streams through map selling and
granting recognition as pioneer explorers by meeting the following
requirements:

  1. i.

Network: Enabling clients to create job postings and broadcast them through
the mesh network, enabling robots to communicate and coordinate tasks
trustfully (Lorenz, 2020; Cameron et al., 2011).

  2. ii.

Data sharing transparency: Transparency about who shares which information at
which time – by monitoring explorers and job execution history – fosters a
_coopetitive_ environment with reduced information asymmetry (Ghobadi and
D’Ambra, 2011; McKiernan et al., 2016).

  3. iii.

Robot agnostic system: Compatibility with any robotic platform following the
established norms, with the capacity to scale the system (Coelho et al., 2021;
Quinton et al., 2023).

  4. iv.

Data loss resistance: Ensuring data remains intact and accessible despite
local system failures or network disruptions, safeguarding valuable
information (Belyaeva et al., 2021), e.g., operational map data.

###  4.2. Requirements specification

To be more precise, the significant functional and non-functional requirements
are identified as follows:

####  4.2.1. Functional requirements:

  1. (1)

Receive and process robots and SOs’ map requests (Zlot et al., 2002).

  2. (2)

Implement a descending-price auction mechanism for robots to allocate mapping
tasks (Zlot et al., 2002; Quinton et al., 2023).

  3. (3)

Verify the integrity and completeness of metadata associated with each job
request (Dias et al., 2006).

  4. (4)

Execute the payment process for completed tasks (Dias et al., 2006).

  5. (5)

Provide an interface for managing and monitoring job requests during the
mission (Dias et al., 2006).

  6. (6)

Enable mapping and ISRU performance assessment (Garcıa et al., 2018; Lorenz,
2020).

  7. (7)

Ensure an adaptable robotic network compatible with the different robot types
(Dias et al., 2006; Zlot et al., 2002; Garcıa et al., 2018).

  8. (8)

Support the seamless integration with possible existing infrastructures during
space missions, such as space decentral (Consensys Space, 2018), SpaceChain
(Zheng et al., 2018), and TruSat initiatives (Mousavinia et al., 2012; Garcıa
et al., 2018).

  9. (9)

Ensure compatibility with standard data formats and protocols for efficient
data exchange (Garcıa et al., 2018).

  10. (10)

Promote interoperability of robotic communication protocols to facilitate
seamless coordination (Garcıa et al., 2018).

####  4.2.2. Non-functional requirements:

  1. (1)

Conformity: Provide an interoperable economic framework interface for
efficient market-based coordination (Zlot et al., 2002).

  2. (2)

Robustness: Protection mechanisms to safeguard against the inconsistency or
loss of essential data owing to system failures or network disruptions
(Lorenz, 2020; Dias et al., 2006; Garcıa et al., 2018).

  3. (3)

Reliability: Enable agnostic data sharing among robots to ensure efficient
coordination (Dias et al., 2006).

  4. (4)

Openness: Foster a mission’s public network environment, enabling relevant
parties to participate on the platform (Zlot et al., 2002).

  5. (5)

Usability: Accessible job request statuses and pricing information for
processing and decision-making (Zavolokina et al., 2018).

  6. (6)

Compatibility: Ensure metadata requirements-driven data standards to diverse
mapping tasks and applications (Dias et al., 2006).

  7. (7)

Maintainability: System design incentivizes participants to take ownership of
maintaining the network (Garcıa et al., 2018).

  8. (8)

Portability: Keeping hardware requirements at a minimum to enable flexibility
in deploying robot missions (Rizk et al., 2019; Roehr et al., 2014; Aditya et
al., 2021).

  9. (9)

Interoperability: Ensure the system can handle traded data without significant
adjustments (Quinton et al., 2023; Zlot et al., 2002; Dias et al., 2006).

###  4.3. Architecture

Figure 2 features an overview of the architecture layers and their functions
in our decentralized system architecture for job requests and contract
creation. The process involves various entities, including the Client,
MeshNetwork, Robot, TemplateSmartContract, JobSmartContract, and JobContract.
The lifecycle begins with the Client initiating a JobRequest by sending a
message to the MeshNetwork. The MeshNetwork broadcasts the request to
available Robots, and each Robot optimizes and schedules its operation outside
the blockchain. Once the scheduling is complete, the Robot sends the
JobRequest through the TemplateSmartContract interface. Upon receiving the
JobRequest, the TemplateSmartContract creates a JobRequest’s unique
identifier. The TemplateSmartContract then shares the ID (transaction hash) of
the JobRequest with the Robot. If at least one Robot decides to participate, a
JobContract is generated and submitted to the TemplateSmartContract. The
TemplateSmartContract updates the proposed job options from the JobContract
with the most cost-effective proposal. The ID of the lowest-priced JobContract
is returned and linked to the corresponding JobRequest, while it informs the
current status and price to the Client.

Figure 2. Architecture layers.

A block diagram representing the architectural layer with three layers
interacting with each other, the upper layer representing the user and robot,
the second (middle) and third (bottom) layers are block diagrams representing
the blockchain and the storage layer, each representing the components,
composed of job contracts and smart contract template with each function, and
the data layer components

##  5\. Evaluation

This section has two subsections that examine the feasibility and performance
of our proposed architecture. In subsection 5.1, we focus on logically
validating the architectural requirements of our design. We discuss network
communication, data transparency, data integrity assurances, and the use of
smart contracts for automated economic decision-making. In subsection 5.2, we
evaluate our system’s fitness to meet accuracy targets in mapping tasks and
resilience in replicated celestial conditions through simulations and physical
experiments. This subsection also discusses usability and transaction
execution duration aspects.

###  5.1. Requirements validation

Our architecture, depicted in Figure 2, integrates the network infrastructure
with DLT for transparent, immutable, and _coopetitive_ robot coordination. By
utilizing an InterPlanetary File System (IPFS), the platform stores only image
hashes that define the images’ URLs on the blockchain, ensuring data
integrity. To ensure conformity ($NFR-1$), the economic automated decision-
making is based on the make-or-buy decision framework (Cánez et al., 2000).
The JobContract ($FR-4$), which records the execution of mapping tasks and
facilitates information trading, and the TemplateSmartContract acting as an
interface for robots to post and monitor JobRequests ($FR-5$), facilitating
efficient robot coordination and automating job assignment and compensation
($FR-1$) in a non-discriminatory environment ($FR-7$).

We initially used a mesh network for communication and control systems
suitable for decentralized peer-to-peer and low-latency settings ($NFR-3$).
However, we identified exploitation vulnerabilities regarding unauthorized
data modification during transmission between peers. Given the high knowledge-
sharing capabilities of _coopetitive_ platforms (Ghobadi and D’Ambra, 2011),
the system’s integration allows for the secure and transparent sharing of
mapping data among the participating robots, where robots add the data
requirements (i.e., mapping location) to be validated by the smart contract
($NFR-4$) via the specific stratified Goldberg polyhedron registry position
approach (Goo et al., 2019). When followed, it ensures the reliability of the
collected information ($FR-10$). The system publicly records who was the
pioneer in being the first to explore, investigate, or discover an area or
execute activities recognized as at the forefront of space exploration
initiatives. As such, this verifiable record can help to build valuable
partnerships and position universities as leaders in innovative
interdisciplinary research. Additionally, a commission model can compensate
the pioneer with every sale of the non-fungible tokens (NFT) associated with
the map on secondary markets (Hartwich et al., 2023a), translating into
additional revenue streams.

IPFS enables robust and decentralized storage of mapping data ($NFR-2$) and
protects from loss or censorship ($FR-8$). It accepts standard data formats
($NFR-2$) and detects tampering through hash verification. While transactions
within the blockchain are immutable, standard asset parameters can be
modified. With these parameters, entities can confidently re-configure,
destroy, mint, freeze, and even clawback addresses, ensuring that only
authorized participants can modify the mapping metadata within the system
($NFR-7$). The history of these assets is immutably recorded in the blockchain
and can be publicly verified. In our case, these systems do not depend on
specific specialized hardware and enable near real-time processing in space-
located facilities or, as we have successfully implemented, in a server-client
structure that simplifies data sharing ($NFR-8$). Thus, it affirms the
supposed improved performance of a distributed, decentralized system
consisting of multiple _coopetitive_ robots compared to single individual
mapping units.

During the bidding process, our robots engage in a descending-price auction.
By posing a lower bid than the previous lowest bid recorded in the smart
contract ($FR-2$), robots signal their willingness to execute a job. This
descending-price bidding allows the robots to continuously reassess their
capabilities and resources, resulting in an economically efficient solution
(Dias et al., 2006). The robots can negotiate multiple contracts
simultaneously, and different robots can initiate new contracts to find new
ways to execute previous contract proposals ($NFR-5$). As a result, several
JobRequests may attach to multiple JobContracts, coordinated by several
TemplateSmartContract instantiations ($NFR-9$). The TemplateSmartContract
evaluates the proposals and ranks the options as it reach the time limit. The
Client then considers the JobContract and decides whether to accept it. If
accepted, an atomic payment transfer process is initiated by rekeying the
contract signature to the TemplateSmartContract, potentially triggering
multiple intermediate contracts. The respective bidding robot can set a
maximum price threshold to ensure that auctioning outcomes align with it.

Regarding exploitation protection in this phase, to ensure the contract is not
vulnerable to skipping payment after task execution, it is only considered
ready for execution once the TemplateSmartContract receives the rekey power.
Once granted, the contract can be signed, and the payment can be made
automatically according to the contract agreement. However, if an attacker
keeps bidding lower than others, there is no automated protection against
spamming or the need to create new auctions. The solutions hence incorporate
the bidder’s reputation, which may be lower than others, and setting a time
limit to restrict possible spamming attacks. These attacks may involve
proposing multiple fake tasks or submitting fake lower bids, which the robot
never intended to execute, or it just wanted to prevent the transaction from
happening. Another potential exploitation is the failure to complete tasks
upon which parties previously agreed. Even though our robots are automatically
programmed to function independently, any deliberate harmful human
interference can still disrupt their operations. Nevertheless, the physical
limitations on communication time delays from Earth and the celestial body and
the smart contract time limit may offer some protection against such cases.
Despite unintended malfunctions that may still occur, this physical
protection, combined with the reputations of bidders, can improve trust in the
platform.

Figure 3. Experimental environment that mimics the environmental lunar
conditions (Lunalab), the EL3 and the _Gazebo_ ’s (Koenig and Howard, 2004)
virtual space for multi-robot simulation integrated with _ROS 2_ (Quigley et
al., 2009).

A collection of images featuring the lunar laboratory, which includes lunar
rovers and the European cooperative large logistic lander. It also presents
the virtual simulation environment and the mapping retraction software.

Once the payment is received, the TemplateSmartContract triggers the
participating robots to sign and execute the JobRequest and JobContract
($FR-4$). The robots carry out the specified tasks outlined in the contract:
mapping or selling the map exploration rights while adding the asset
description into the metadata and data ($NFR-6$). Before finalizing the
contracts, the TemplateSmartContract conducts a plausibility check of the
correctness and completeness of the mapping data retrieved from IPFS by
assessing the requirements described on the JobRequest and the NFT metadata.
This check ensures that the data adheres to the initial specifications
outlined in the JobRequest without any unauthorized modifications, such as via
the mutable asset characteristics. The possible mutability functions are:
freezing an asset until the user meets a specific requirement, clawback by
debiting a user’s account for defaulting in loan payments, and revoking the
ownership of assets belonging to users. Thus, the completion is successful if
the robot receives the payment according to the due terms of the JobContract.

In the last stage, due to the NFTs’ (meta-) data, the Client is recognized as
the pioneer explorer of the acquired mapping data or deliverables, signifying
the process completion. The TemplateSmartContract verifies that the retrieved
map metadata and IPFS data align with the original JobRequest and the acquired
mapping information ($FR-3$). After the verification process, the Client can
use the data for decision-making, knowing that the mapping coordination
process was transparent and trustworthy. We implemented a prototype of our
designed architecture to evaluate its practicality and discuss the lessons
learned.

###  5.2. Requirements verification in simulation and implementation

The evaluation of our system aimed to test and validate its accuracy and
resistance in similar celestial conditions, explicitly focusing on assessing
space-related limitations, such as latency and resource constraints. We
conducted three simulations to evaluate the architectural solution’s ability
to collect, coordinate, and maintain mapped area data. We started with a
turtlesim-based simulation111https://wiki.ros.org/turtlesim during the
conceptualization stage using _ROS 2_ (Quigley et al., 2009). While _ROS 2_
simplified the complex robotic development with open-source software tools and
libraries, in the second simulation, we used _Gazebo_ ’s realistic 3D
simulation platform (Koenig and Howard, 2004) for testing robot models and
algorithms, thus providing a closer reproduction of the final simulated
environment. Additionally, as virtual simulation may not sufficiently
replicate real-world situations (García et al., 2020), the third evaluation
represented a proof-of-concept conducted in a celestial-like environment
laboratory setting. Each test had an approximate duration of 30 minutes. The
robotic platform utilized a visual-SLAM algorithm during the last assessment
to create a map of the analogous lunar terrain facility, as illustrated in
Figure 3. The facility in which we trained the robots to create precise maps,
an $80\,\mathrm{m}^{2}$ rectangle filled with basalt, was designed to emulate
the surface of the Moon’s south pole, an area where researchers expect to find
resources.

As we are currently in the prototype phase, our primary focus is to assess the
technical feasibility and performance of the architecture. Our system’s
architecture, illustrated in Figure 4, is designed for efficient data storage,
NFT management, and trading with 3 stages: NFT creation, sale, and retrieval.
We harnessed the capabilities of _Python_ , $C++$, _Gazebo_ , and _ROS 2_ for
development, ensuring effective communication and simulation. To bridge the
communication gap between robots (LEO2 and LEO3) and the blockchain, we
integrated the PureStake connector via a _REST_ service
222https://www.purestake.com/.

Figure 4. Prototype in the laboratory of simulated Moon environment.

A collection of images featuring the architectural sequence implementation.
The first image represents the lunar laboratory described in Figure 3, with
the LEO3 mapping ZoneA and LEO2 wanting to buy the map. This represents the
scenario of robots as depicted in Figure 1. It follows the sequence with LEO3,
creating the NFT that represents the map of ZoneA. Finally, the last two
images on the left portray LEO2 and LEO3 assets wallets, representing that
LEO2 pays the cryptocurrency to LEO3, and in exchange, LEO2 now owns the NFT
that LEO3 transferred.

All communication flows, except for the smart contract, have been implemented,
encompassing the autonomous creation, negotiation, coordination, selling, and
verification of NFTs and mapping requirements. Despite not taking full
advantage of the smart contract automated behavior, the prototype already
leverages a blockchain network for trading NFTs and certain data storage
aspects, enabling real-time decision-making based on blockchain data. The
architecture uses IPFS for high-throughput content-addressed block storage
(Benet, 2014). Entities can govern their mapping NFTs metadata and settings,
from transferability up to their destruction, promoting value to a market for
data access and recognizing pioneering exploration. These NFTs encapsulate
specific map data and metadata, containing coordinates, resolution, sensors,
mapping algorithms, and map price, fostering trade within the network and
improving ISRU’s information symmetry.

Our evaluation assessed the system’s bandwidth usage and transaction duration
in simulated lunar network conditions. Despite transactions needing to be
fully optimized, the average bandwidth usage during uploads was less than 5 MB
over 4 seconds, with a median of 4.7 MB. The standard deviation was 0.5 MB,
indicating a relatively consistent transaction performance. The time required
for uploading maps and conducting sales transactions was approximately 10
seconds, with a median of 9 seconds. The standard deviation was 2 seconds,
indicating that most transactions were completed within a reasonable time
frame. These durations are relatively short compared to the time robots
typically take to navigate to the place and process cartographic data, which,
at best, can take several minutes to hours. As a result, the upload duration,
plus mapping and reaching a consensus, is negligible compared to considering
the inherent latencies of bidirectional communication between terrestrial
stations and robotic units (which, for the Moon case, takes a minimum of five
seconds as the signal needs to pass through the different relays on Earth and
satellites in space), let alone the possibility of other nations being open to
start cooperating. The system displayed reasonable transaction duration usage
and significantly reduced robot coordination delays compared to waiting for
human coordination.

##  6\. Discussion

Our evaluation demonstrates the technical feasibility of the proposed
architecture for MRS in collaborative scientific space exploration. The system
successfully collects, coordinates, and maintains mapped area data via the
blockchain metadata while evaluating the mean bandwidth usage and transaction
duration to validate the feasibility in real-world testing conditions (see
Figure 4). Moreover, our architecture offers precise map data that can be
leveraged for various applications, including celestial terrain coverage with
sustainable ISRU capabilities, thereby enhancing information symmetry and
decision-making processes.

As for scalability, while the initial setup requires resources to bootstrap
the constellation map through multiple interactions, the system is designed to
handle the growing data in DLT or IPFS without being overly strained. The
blockchain only stores limited map metadata, managing rights, properties, and
descriptions. Nevertheless, there may be challenges with multiple mapping data
streams in the IPFS system in the long run.

###  6.1. Limitations

This section outlines the limitations of our architecture, categorized into
technical challenges, economic implications, and future research directions.
Ensuring the reliability and integrity of shared mapping data and the
effectiveness of smart contracts remains a challenge. As such, future work
should focus on measurement systems, quality assurance metrics, and addressing
the practical limitations of smart contracts in decentralized systems
(Afanasyev et al., 2019). For instance, validation mechanisms (Li et al.,
2022), reputation systems (Calvaresi et al., 2018; Yu et al., 2013), and
different consensus mechanisms (Ferrer, 2019; Rizk et al., 2018) could be
integrated to enhance mapped data reliability. Continuous monitoring and
anomaly detection algorithms can also support detecting and mitigating
suspicious activities (Strobel et al., 2018). It is crucial to understand the
practical implications of public vs. private networks, permissionless vs.
permissioned designs, and the specific consensus mechanisms that minimize
computational burden, specifically for resource-constrained space MRS
(Alfraheed and Al-Zaghameem, 2013).

Although our implementation aims to solve the dependable communication
limitation for DLT-based platforms to thrive, the automated market-based
approaches for MRS, such as blockchain auctioning systems, are still in the
early stages. Auction systems are also considered intricate and less effective
in centralized situations and excessively complex in distributed situations
(Dias et al., 2006). Additionally, hierarchical decision-making and external
third-party systems can pose challenges in identifying trustworthy evaluators
for smart contract task execution (Li et al., 2022; Abe et al., 2022). Further
research should investigate efficient communication protocols for blockchain,
smart contracts, market mechanisms (Dias et al., 2006), and decentralized
combinatorial optimization systems (Baldor et al., 2013), which are crucial
for space missions.

The economic implications of DLT in space MRS require further investigation
and support from real-world examples and case studies. Narratives for
blockchain technology are essential for promoting its acceptance of new ways
of governing outer space (Filippi and Leiter, 2021). Real-world examples and
case studies exploring economic interactions facilitated by DLT in space
exploration, such as resource trading, service provisioning, and fair
compensation mechanisms, would enhance the credibility and depth of the
discussion (Baldesi, 2017; Filippi and Leiter, 2021; Ibrahim et al., 2021).
Future research should focus on real-world deployments and economic modeling
to quantify the economic impact of our architecture.

As Afanasyev et al. (2019) point out, the field must transition from
theoretical discussions to practical implementations. Our work aims to
contribute to this transition by implementing prototypes in celestial-like
conditions. We are addressing the limitations and challenges to ensure the
platform’s efficiency and economic viability in space exploration. We intend
to continue our research and development efforts to address these challenges,
keeping in line with the increasing interest from the scientific community,
space agencies (Baldesi, 2017; Israel, 2020), and industry (Ibrahim et al.,
2021).

###  6.2. Research gap and open challenges

High autonomy in limited resources: _Coopetitive_ platforms are known to
provide high knowledge-sharing capabilities (Ghobadi and D’Ambra, 2011), where
sophisticated techniques, such as ontologies, semantic web, and linked data
(Memduhoglu and Basaraner, 2018) would be an option, given that robots can
learn new abilities with minimal incremental rewarded data (Baima and
Colombini, 2021). However, it is challenging in environments with limited
resources to deeply understand data and manage its heterogeneity while
ensuring system stability and safety.

Global cooperation and open networks: A neutral _coopetitive_ platform’s
economic aspect may also benefit companies by allowing their robots to perform
low-priority tasks during idle time, potentially increasing equipment
utilization and return on investment and improving global ISRU efficiency.
Collaboration is crucial in reducing mission costs and achieving economies of
scale (Chow et al., CEST). Despite early proposals to utilize planning
knowledge to improve performance and efficiency (Muppasani et al., 2023) and
possibly learn incremental actions (Baima and Colombini, 2021), it is crucial
to establish ways to exchange norms, maintain team identity, and promote group
member confidence (Strobel et al., 2018).

Reliable communication mechanisms and automated DLT-based markets:
Hierarchical distributed decision-making structures have their strengths and
weaknesses for DLT-based automated markets (Yan et al., 2013) but relying on
external third parties still challenges identifying trustworthy evaluator
methods for DLT-based application layers, which could be an organization or a
digitally supplied system challenges to identify trustworthy evaluator methods
for DLT-based application layers as it may reduce the trustworthiness of the
network.

Economic and legal aspects remain open: While our research primarily focuses
on the technical feasibility and implementation of blockchain technology in
MRS for space exploration, we acknowledge the significance of economic and
legal aspects in this domain. The economic implications, including cost-
benefit analysis, funding models, and the financial viability of such systems,
remain crucial areas for future exploration. Likewise, legal considerations,
encompassing regulatory compliance, space law, and data governance, pose
complex challenges that are beyond the scope of this paper.

##  7\. Conclusions

Our primary scientific contribution lies in the architecture and requirement
engineering for blockchain applications in MRS. The prototype, while a crucial
element for demonstrating the feasibility of our approach, is secondary to the
theoretical framework we have adopted. Our architecture, developed through
interdisciplinary perspectives from information systems, engineering, and
economics, answered the RQ by demonstrating the potential of DLT. Our
platform’s flexibility and non-discriminatory aspect allow for leveraging idle
resources through a robot-as-a-service platform. The prototype enables
efficient mapping coordination via smart contracts, ensures data integrity via
NFTs, and facilitates revenue generation through market-based cryptocurrency
and NFT transactions, allowing organizations to trade valuable assets in space
missions. Blockchain is employed not as an end in itself but as a tool to
facilitate secure and reliable information exchange and coordination among
robots, addressing key challenges in robotic communication and operation Our
research provides a foundation for future advancements and implementations,
following the principles and guidelines of the DSR approach.

While our research presents promising results, several areas for further
exploration and improvement remain. The economic implications of DLT in space
MRS require more profound analysis, including identifying suitable use cases,
evaluating economic incentives, and examining governance and legal
considerations. Additionally, while addressing the environmental impact of DLT
in space missions, future research should evaluate the performance and
efficiency of our approach, consider transaction throughput and latency,
optimize energy efficiency, and explore scalability solutions such as sharding
and layer two protocols (Hartwich et al., 2023b). Future research could also
develop decision support on when DLT is required instead of digital signatures
and bilateral communication to securely and efficiently implement concurrent
access. However, this dilemma again emphasizes that while a DLT may be
desirable from an economic perspective, it can add substantial complexity to
design and setup. Thus, it is crucial to identify suitable use cases for DLT
in space MRS and understand service providers’ economic needs and incentives.

###### Acknowledgements.

This research was funded in part by the Sponsor Luxembourg National Research
Fund (FNR) https://doi.org/10.13039/501100001866 in the FiReSpARX Project,
ref. Grant #14783405, PABLO Project, ref. Grant #16326754, and by PayPal,
PEARL grant reference Grant #13342933/ Gilbert Fridgen. For the purpose of
open access, and in fulfillment of the obligations arising from the grant
agreement, the author has applied a Creative Commons Attribution 4.0
International (CC BY 4.0) license to any Author Accepted Manuscript version
arising from this submission.

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  *[MRS]: multi-robot systems
  *[ISRU]: in-situ resource utilization
  *[DLT]: distributed ledger technology
  *[DSR]: design science research
  *[IPFS]: InterPlanetary File System
  *[NFT]: non-fungible tokens
  *[NFTs]: non-fungible tokens