# A Decentralized Cooperative Navigation Approach for Visual Homing Networks

Mohamed Rahouti, Damian Lyons, Senthil Kumar Jagatheesaperumal, and Kaiqi
Xiong M. Rahouti is with the Department of Computer & Information Science,
Fordham University, NY, NY USA (email: mrahouti@fordham.edu). D. Lyons is with
the Department of Computer & Information Science, Fordham University, Bronx,
NY USA (email: dlyons@fordham.edu). S.K. Jagatheesaperumal is with the
Department of Electronics & Communication Engineering, Mepco Schlenk
Engineering College, Sivakasi, Tamil Nadu, India (e-mail:
senthilkumarj@mepcoeng.ac.in). K. Xiong is with Cyber Florida and ICNS Lab,
University of South Florida, Tampa, FL USA (email: xiongk@usf.edu). Manuscript
received XX, 2023

###### Abstract

Visual homing is a lightweight approach to visual navigation. Given the stored
information of an initial ‘home’ location, the navigation task back to this
location is achieved from any other location by comparing the stored home
information to the current image and extracting a motion vector. A challenge
that constrains the applicability of visual homing is that the home location
must be within the robot’s field of view to initiate the homing process. Thus,
we propose a blockchain approach to visual navigation for a heterogeneous
robot team over a wide area of visual navigation. Because it does not require
map data structures, the approach is useful for robot platforms with a small
computational footprint, and because it leverages current visual information,
it supports a resilient and adaptive path selection. Further, we present a
lightweight Proof-of-Work (PoW) mechanism for reaching consensus in the
untrustworthy visual homing network.

###### Index Terms:

Blockchain, visual homing, navigation, robot, consensus.

##  I Introduction

Visual homing is a navigation strategy employed often in mobile robots in
which a motion vector towards a target destination is calculated by comparing
the image captured at a particular instant with that of the destination. For
real-time autonomous navigation of mobile robots, and based on industrial
demands, visual homing is gaining more popularity with its reliable
characteristics inspired by biological navigation.

Several approaches exist for visual homing calculations [1]: holistic methods
transform whole images, while feature-based methods, like the average landmark
vector (ALV), extract and compare features. The ALV requires compass
information, but correspondence-based methods don’t, utilizing instead
relative motion vectors and potentially SIFT scale [2] or stereo information
for depth cues. While visual homing aids in local-area navigation when
combined with obstacle avoidance [3], a challenge is that the robot must
initially spot the destination in its field of view (FOV). Often, a panoramic
or wide FOV system is used. We suggest using FOVs from multiple robots in a
team to address this, as a target might be in one robot’s FOV even if another
can’t see it [4].

In such a scenario, the main challenge is how to establish trustworthy
communication for driving the autonomous navigation of robots. First, in a
visual homing network, the target position can be dynamic and needs to be
communicated to the robots involved in the cooperative navigation. Second,
trustworthy communication infrastructures are needed for establishing
sustainable links among the visual homing network. The trustworthiness is
ensured as each data entry in the ledger is cryptographically linked to the
previous one, forming a chain of blocks. This ensures that any unauthorized
modifications or tampering attempts can be detected, promoting data integrity
and authenticity.

This paper addresses the limitations of visual homing-enabled robotic
environments by proposing a novel blockchain-enabled visual homing (BC-VH)
framework that leverages decentralized blockchain technology for lightweight
navigation and FOV sharing. This study explores challenges related to
efficient navigation and resource consumption, presents the BC-VH
architectural design, and discusses its implementation, use case scenario,
consensus mechanism, and security assessment. The main components of the
proposed architecture are depicted in Fig. 1. A lightweight consensus
mechanism is further deployed to enable timely verification and validation of
FOV updates. This mechanism enables visual homing team robots to achieve
consensus (agreement) in a cooperative manner rather than competitive. The
article addresses the challenges in visual homing networks with the idea of
BC-VH solutions focused on the following contexts.

Figure 1: Architectural design of the blockchain-enabled visual homing
network.

  * •

An architecture to enhance visual homing environments: The proposed
architecture will allow individual team robots to efficiently share and
identify up-to-date common landmarks at a low operational cost and in a timely
manner.

  * •

A consensus suitable for resource-constrained robot navigation systems:
Creating a blockchain typically requires significant computation power, which
thus depletes the computational resources of individual robots in visual
homing environments. The proposed consensus mechanism is lightweight, and team
robots share the same access control list (ACL) secured by the blockchain.
Therefore, burdens on the computational complexity are alleviated for
individual robots.

In this article, we first present the core aspects of visual homing and
highlight relevant research from the literature. Next, we provide a blockchain
framework to establish trustworthy decentralized communication for visual
homing. Further, we describe use case scenarios, configurations, and metrics
for the evaluation of BC-VH. We also show how the proposed consensus mechanism
can be incorporated into BC-VH for its assessment.

##  II Background

Visual homing as a robotic navigation technique arguably originated in
computer models with the inspiration from insect navigation in 1983. The FOV
for an insect can be very wide, even 3600, and for this reason, a panoramic
camera is also often used for robot visual homing [5].

###  II-A Visual Homing

The process of visual homing implementation involves image acquisition,
feature extraction, and image warping to transform and align the acquired
image with a reference frame. By comparing the warped image with the
reference, the robot’s precise location is determined, allowing for effective
navigation and control toward the desired destination. Approaches to solving
the visual homing problem can be separated into whether they consider each
image as a whole (holistic) or whether information or features are extracted
from each image and compared to each other (feature-based comparison). A
remarkable approach that can be used is warping, a holistic approach where the
current image is warped according to different movement vectors, and an
optimal movement vector is selected based on the warped image most similar to
the home image. Image distance methods rely on observing that image distance
measures will increase proportionally to the spatial distance between
locations at which the images have been taken. A matched-filter image distance
algorithm can be used for visual homing. Further, in [6], the authors focused
on using stereo-depth information for adaptive correction of landmarks to
perform real-time homing tasks.

###  II-B Wide Area (Scalable) Visual Homing

The restriction that the home location is in the initial FOV for homing to
begin has been addressed by a number of researchers. A traditional method is
based on ant navigation in which the robot stores images as it moves in the
environment. Navigation to a location that is not in view is accomplished by
reprising visual homing to the sequence of images recorded on a previous trip
to the distant location. Stelzer et al. [7] addressed this problem for
‘scalable’ visual homing using an algorithm based on bearing angles, where
they proposed a data structure that stores and efficiently leverages the
intermediate imagery as configurations of landmark bearings called view
frames. Image sequence approaches to wide-area visual navigation require that
the robot explores the work area, and determines when to store a new
intermediate image by inspecting the distance traveled, and estimating the
landmark dissimilarity [7]. All of these aforementioned approaches construct a
map data structure representing the motion and observations of the robot over
the working area.

The wide area visual navigation (WAVN) [4] approach addresses the problem of
visual homing to a distant (out of sight) location in a different way. Prior
approaches carry forward the original insect inspiration for visual homing,
for instance, a single insect wandering in a landscape. WAVN starts with a
team of robots distributed over a wide area and in wireless communication with
each other. If a team member is driven by a task to go to a location that is
not within its FOV, then it can leverage the fact that the location may be
visible or have recently been visible to one of the other team members (or
indeed fixed camera assets, as may exist in an urban area).

If team member $A$ needs to travel to a specific home location, but that
location is not in view, then robot $A$ tries to find another robot $B$ that
can see the home location. If robot $B$ can see the home location, then $A$
and $B$ must next identify a common visual landmark that can be used as an
intermediate visual target that $A$ can use to travel to the vicinity of $B$
and from there to the final destination. While this example uses only two
robots and one shared landmark, the approach can be generalized to a sequence
of intermediate landmarks. Lyons and Petzinger [4] evaluated approaches to
identifying these common landmarks, proposing a combination of deep-learning
and SIFT-based techniques that significantly outperform a purely feature-based
approach. The WAVN approach does not require a map data structure and it is
hence useful for implementation on small or computation-constrained platforms
and in certain situations where GPS is not (readily or consistently)
available. The visual information in WAVN is always up to date, reflecting the
current visibility of the robot team. However, it is appropriate for long-term
deployments in areas whose appearance may change with weather or seasons and
so forth and also in which maps might easily become out of date. Furthermore,
the communication of visual information among the team of robots is a crucial
part of this approach, which raises security concerns.

###  II-C Security of Imaging

Challenging scenarios do occur during cooperative perception in a visual
homing network. Malicious nodes could send false information about image cues
to the robot team members and thereby influence or alter their navigation
policies. During the navigation, different security factors need to be
commonly considered, where the unpredictable behavior of the fake robotic
nodes in the visual homing networks could be dealt with ease. Apart from the
commercial off-the-shelf security frameworks, the launch of blockchain has
created opportunities for visual homing networks in framing cooperative
navigation strategies. Additionally, blockchain frameworks are typically being
deployed in robotic applications for accurate prediction of false information
from unauthenticated nodes and safeguarding them against deception. With the
communication among the robots in the visual homing network, being established
through the blockchain framework, tamper-proof records of information are
maintained in the network and the robot helps to identify the inconsistencies.
Depending on the scenarios, every single robotic node in the work area will be
registered in the blockchain network. Obviously, with the increase of the
nodes in the network, a smart contract and consensus management need to be
strengthened for secure communication and framing trustworthy navigation
strategies.

The integration of features from multiple images streamed from various nodes
of visual homing networks also poses a great threat in being potentially
exploitable by unauthorized nodes. Introducing blockchain frameworks could
discover the related features and focus to identify the risky feature extracts
or tampered features from the malicious nodes in the visual homing network. To
enable non-trivial feature extraction and trustworthy communication among the
nodes in the visual homing network, the blockchain framework can harness the
feature extraction process by introducing secure ledger transactions. A block
in the network ensures continuous end-to-end connectivity through smart
contracts established on a feature extraction process in the network.

##  III State of the Art

Several research studies have integrated blockchain technology into a wide
range of applications, including, but not limited to, the Internet of Things
(IoT), smart cities, connected autonomous vehicles (CAVs), and robotic
systems. The integration of such a decentralized technology in robotic
environments, such as visual homing, can help tackle the security challenges
and limitations beyond distributed/decentralized decision-making for robotic
navigation tasks. Castello et al. [8] presented a blockchain-enabled framework
to improve the decision-making and communication security in swarm systems,
while Fernandes and Alexandre [9] proposed an event management framework by
leveraging the decentralized blockchain and Tezos technology. Strobel et al.
[10] further developed blockchain-based collaborative decision-making in
byzantine robots using smart contact coordination strategies.

Empowering blockchain characteristics combine the advantages of security and
energy efficiency for the practical implementation of the swarm of UAVs
together [11]. They involve multi-drone connectivity and collaboration to
enhance security and achieve consensus for exploring their navigating
environment. Although the means to address the lifecycle and trust management
of blockchain might be still short, the flexibility of the UAVs in joining the
network, gathering quality data, and preventing assaults can address such
issues by hovering across the target area. Further, it also expands the
benefits of enhancing the energy efficiency and connectivity of a navigating
environment.

When exploring the ways to formally deploy blockchain-based trust for multi-
robot collaborative tasks [12], it turns out that the remarkable efforts
originate from the edge knowledge inference model. In particular, we can
formulate an edge inference process in terms of the collaborative knowledge-
based blockchain consensus strategy proposed by Li et al. [13] in the contest
establishing collaborative tasks among multi-robotic systems. To do so, an
emergency rescue application is chosen as a case study to evaluate the
framework that measures the accuracy and latency of the edge knowledge
inference.

Distributed landmarks can be created and deployed for establishing VH to
enable strategic navigation for mobile robots based on requests [14]. By
considering equal distance assumptions on the chosen landmarks, the VH
strategy can be implemented based on the pseudo-isometric distribution vector
to offer adaptive navigation and alleviate environmental challenges. The
unevenness and randomness issues in the distribution of landmarks can be
addressed by the vector pre-assign mechanism based on pseudo-isometric
characteristics. Such a VH strategy can enable better precision and be capable
of maintaining the computation speed with the efficient implementation of
multiple robots in the scenario.

Blockchain technology can be used as a communication tool within multirobot
systems, for leaders to broadcast directions to the whole group [15].
Distributed ledger and cryptocurrency platforms, such as IOTA, can also be
leveraged along with Robot Operating System (ROS) to provide scalable robotic
systems and network partition tolerance [16]. Additionally, blockchain can be
employed for certificate verification purposes using robotic technologies
[17].

Although existing state-of-art solutions enable specialized robotic teams to
carry out individual-specific tasks (e.g., navigation, flocking, etc.), very
little to no work considers leveraging a decentralized technology with visual
homing-based robotic platforms. Given the trustworthiness property, low
operational cost, efficient access control, and provenance, the proposed BC-VH
solution will not only enhance navigation tasks in a visual homing environment
but also enable further smart city-based robot-driven applications and use
cases. The proposed BC-VH solution allows the individual team robots to
efficiently and reliably share and identify up-to-date common landmarks in a
timely, trustworthy, and secure manner, with a low overhead/operational cost
[18].

##  IV The Design Principle of BC-VH

In this section, we propose a novel architecture based on blockchain and its
associated design principles involved with consensus and smart contract
management such that the proposed architecture has the potential to address
some of the aforementioned challenges in the visual homing network. It is
worth noting that communication between team robots is assumed secure in a
locally controlled and trusted network environment. This is primarily due to
the limited access and trust in the local environment. Specifically, access to
the network is typically limited to authorized devices within the defined
physical area (i.e., this assumption is based on the understanding that only
trusted team robots and devices are connected to the controlled network). A
manufacturing plant, for instance, is expected to have stringent access
controls, both physically (entry and exit points) and digitally (network
access). Thus, the assumption of secure communication in locally-controlled
robotic network arises from the inherent controlled nature of the environment,
the isolation from external networks, and the rigorous access controls, both
physical and digital.

###  IV-A Architectural Design of BC-VH

Fig. 1 depicts the architectural design of our proposed BC-VH prototype based
on the conventional workflow of blockchain technology. The blockchain network
can be simulated using tools like Hyperledger Fabric or Ganache, which is a
local Ethereum blockchain for development and testing purposes. In cases where
resource-constrained devices are involved, alternative blockchain technologies
must be designed to be lightweight and optimized for constrained visual homing
environments. Thus, we propose a distributed ledger framework that eliminates
the need for traditional blocks and miners, resulting in a lightweight and
scalable solution for resource-constrained robotic systems. A FOV represents a
set of six panoramic images, and it efficiently fits into a block transaction
as an ordinary data field.

In Fig. 1, $BC-R_{s}$ generates the block hash (BH) for the next block to
include valid recent panoramic views of individual team robots. The
established block is then broadcasted to all $BC-R_{s}$ inside the visual
homing environment for verification. Once the new block is successfully
validated upon reaching a network consensus, it will be appended to the local
ledger of each team robot.

###  IV-B Use Case and BC-VH Working Scenario

To prove the concept discussed in the aforementioned sections, we consider a
case study derived the inspiration from the existing deployment of visual
homing, for which we have included a blockchain framework for the trustworthy
cooperative navigation of robots.

(a) |  (b)
---|---
(c) |  (d)
Figure 2: Navigation use case in a small-scale visual homing environment.

In a small-scale visual homing environment with three team robots, $R_{1}$,
$R_{2}$, and $R_{3}$, the blockchain-supported navigation scenario represented
in Fig. 2 is established as follows:

Figure 3: Visual homing consensus establishment in BC-VH.

  1. 1.

If Robot $R_{1}$ is unable to see the goal directly, it will first check which
robot ($R_{2}$ or $R_{3}$ or both) is able to see the target location. In the
scenario given in Fig. 2(b), robot $R_{3}$ can see the target location. It is
checked by the robot $R_{1}$ by looking in the transaction record ($BC-R_{1}$
ledger) of the last block (i.e., the most up-to-date FOVs) and $R_{1}$
navigates to the target location.

  2. 2.

$R_{1}$ next checks if that robot has a common landmark with $R_{2}$ or
$R_{1}$ by checking the ledger. In the presented scenario, there is one common
landmark between robots $R_{3}$ and $R_{2}$, Fig. 2(b).

  3. 3.

$R_{1}$ next checks if $R_{2}$ and $R_{1}$ have a common landmark by checking
the ledger. In this case, one common landmark is found.

  4. 4.

Finally, robot $R_{1}$ can now know the landmark path to the desired
destination and complete the navigation, Fig. 2(d).

##  V Consensus Mechanism in BC-VH

The consensus mechanism represents the core of blockchain technology, enabling
a decentralized verification and validation of transactions. Specifically, PoW
is deployed in the proposed prototype as the consensus mechanism to enable
fairness among the team robots over the decentralized environment [10]. As
there are memory and energy constraints in visual homing robots, energy
consumption, memory utilization, and convergence time are key concerns to
consider when one designs a consensus mechanism in such a system. Therefore,
we propose using a lightweight, enhanced PoW to provide a decentralized
consensus mechanism for validating transactions and maintaining the integrity
of the blockchain network. Such a lightweight consensus can also be suitable
for resource-constrained platforms such as fog and cloud environments.

In this consensus mechanism, the process of reaching an agreement (consensus)
by the team robots is depicted in Fig. 3. The transaction representing the FOV
update for a particular robot $R_{i}$ will be broadcast to the corresponding
$BC-R_{i}$ for verification before it is broadcast to all $BC-R_{s}$ of other
team robots for validation. Further, $SHA-512$ is the designated hash function
used for hashing computation. Once the $BC-R_{s}$ ($BC-R_{1}$, $BC-R_{2}$, and
$BC-R_{3}$) receive the broadcast transaction, they will cooperatively operate
to compute the block hash and update their corresponding ledger.

It is important to note that the proposed application of blockchain to visual
homing is an adaptation of specific aspects of PoW rather than the complete
consensus mechanism itself. It aims to address some challenges (e.g.,
integrity, trustworthiness, and decentralized data sharing) in lightweight
robot communication and navigation tasks by leveraging the key concepts from
PoW, including block verification/validation and forking resolution. This
adaptation’s actual implementation and effectiveness may also depend on the
specific requirements and characteristics of another robot system in question.
The proposed blockchain-assisted visual homing can be feasibly reproduced on
physical/real-world navigation robots in large-scale, GPS-denied environments
[19].

##  VI Preliminary Performance and Security Assessment

Figure 4: The top figure depicts example robot pair positions showing a
ROS/Gazebo 3D Urban landscape, and the bottom two show panoramic views from
the robot cameras.

One of the common and widely used landmark recognition software/testbeds is
written in Robot Operating System (ROS), which is a popular open-source
middleware. Gazebo, a 3D simulation engine, has also been integrated with ROS
to provide robot software simulation testing. Further, in [4] modified UCIC
software was used in conjunction with two Pioneer P3AT robots equipped with
cameras for this simulation. The modifications to the software include the
blockchain and consensus implementation.

Fig. 4 depicts an example scene from our ROS/Gazebo suburban simulation. This
simulation runs over a Digital Storm Aventum equipped with an Intel Core-i9
processor and GeForce RTX 3080 GPU and models a $130\times{}180m^{2}$ flat
suburban area filled with vehicles, grass, buildings, trees, and other
objects. The robot team has simulated and executed using a model based on
ROS/Gazebo, with Pioneer 3-AT robots operating in an environment where their
FOV has both shared and occluded portions.

###  VI-A Mission Success and Overhead

To carry out a preliminary examination of the proposed BC-VH framework, we
assessed the overhead associated with the end-to-end navigation (mission) in
comparison with the traditional visual homing strategy (without blockchain).
Here, we forced all robots to look around before the motion started rather
than looking around as needed. We ran two missions with the blockchain feature
on and two with it off, replicating the experiment twice with 8 missions in
total.

Fig. 5 depicts the overall time associated with mission completion by Robots
$R_{1}$ and $R_{2}$. With blockchain implementation enabled, the robots can
complete their mission with an end-to-end delay that is nearly equal to that
of the blockchain-free visual homing system. That is, given all the benefits
(i.e., tamper-proof data sharing, trustworthiness, decentralization, etc.)
blockchain technology can bring into a distributed environment such as visual
homing, it does not incur any significant computational delay in mission
completions. The reason $R_{1}$ mission time is higher than that of $R_{2}$
could be due to the complex or cluttered environment and limited visibility
conditions (e.g., obstacles). Table I further presents the average
delay/communication overhead associated with our blockchain solution over
simulated FoVs with varying visual homing robot positions. The table
demonstrates the significantly low overall delay associated with the
substantial operations in the system, namely, ledger state update and
panoramic view retrieval.

From the time complexity perspective, given $n$ evenly distributed robots
(participating nodes) in the workspace:

  * •

Path planning: The maximum path length is $\ell\approx log_{b}(n+1)$,
logarithmic in the size of the robot team. Assuming that each robot sees
landmarks just in its vicinity, a worst-case path length would be $O(n)$.

  * •

Blockchain operations: Multiple nodes collaborate to validate and record
transactions. The time complexity of blockchain operations, such as
transaction verification and block creation using PoW has a time complexity of
$O(n)$, where $n$ is the computational difficulty set by the network.

  * •

Communication and consensus: Assuming network propagation delay as nodes
broadcast valid blocks to other nodes, the proposed PoW-based consensus is
reached when the majority of nodes agree on the longest valid blockchain. The
time complexity of communication and consensus in PoW networks is typically
$O(n)$. However, the time complexity of PoW itself could be exponential in the
worst case.

###  VI-B Security Analysis

Several security evaluation metrics are considered to investigate the
effectiveness of the BC-VH framework employed for solving the cooperative
navigation among the robots. We analyzed the effects of the following attacks
in the BC-VH framework on the visual homing network performance. STRIDE [20],
a comprehensive threat modeling methodology was used to analyze and assess the
strengths and limitations of the proposed prototype against a design of six
categories.

####  VI-B1 Spoofing

Even the most diligent robotic nodes in the visual homing network could be
tricked by sophisticated spoofing attacks. Here, the intruding robots can
masquerade as a trusted source and illegitimately gain access to vital data
shared by the authorized robots in the network. Spoofing generally takes
advantage of the vulnerable visual homing networks, thereby collecting
information for gaining network access controls.

####  VI-B2 Tampering

Sustainable maintenance of tamper-proof records is one of the inherent
properties of the blockchain. When a block is created, the hash of the
previous block will be added as a seal to the new block. In the BC-VH
framework, in order to tamper with a block in the blockchain, the intruder has
to tamper with every subsequent individual block in the chain associated with
the robots in the network. Authenticated control over almost 51% of the blocks
in the BC-VH network is mandatory in order to recognize the tampered node,
which is a challenging trend.

####  VI-B3 Repudiation

Repudiation provides proof of the occurrence of activity in the network made
by a legitimate robot in the visual homing network. With repudiation, the
transmitting robot will have proof of delivery, and the receiving robot in the
network will have sufficient proof of the identity of the sender. Trustworthy
issues are normally exploited with the repudiation, by denying the
authenticated information shared by the robots to other legitimate nodes,
which could be effectively addressed through the BC-VH framework.

Figure 5: End-to-end delay in BC-VH vs. traditional visual homing strategy. BC and No BC indicate the usage of BC-VH and traditional/centralized visual homing (without blockchain), respectively. TABLE I: Average delay overhead in BC-VH over simulated FOVs with varying robot positions. # Positions |  Update Time (ms) |  Retrieval Time (ms)
---|---|---
40 |  0.82998 x$10^{-3}$ |  1.79 x$10^{-5}$
200 |  0.100515 x$10^{-2}$ |  1.99 x$10^{-5}$
500 |  1.09 x$10^{-3}$ |  1.98 x$10^{-5}$

####  VI-B4 Information Disclosure

Information disclosure or leakage occurs when a robot in the visual homing
network intentionally reveals sensitive information to potential attackers.
Through smart contracts and audibility deployment in the BC-VH framework, a
certain degree of assurance could be ensured on information disclosures in the
visual homing network.

####  VI-B5 Denial of Service (DoS)

In DoS attacks, the hacker disrupts the availability of data or authenticated
robots in the visual homing network through reduction, connection closures,
data destruction, or resource exhaustion. The BC-VH solution addresses this
issue by offering incentive-based mitigation through reward mechanisms.
Attacks on the PoW mechanism in BC-VH would require significant mining power
to undermine the information exchange among the robots in the visual homing
network.

####  VI-B6 Elevation of Privilege

Elevation of privilege refers to a vertical privilege escalation scheme where
lower privileged users grant themselves permissions to higher privileges. In
the visual homing network, this could be exploited by slave robots to escalate
their privileges using reserved means and authentications meant for master
robots. To mitigate this, smart contracts can be deployed in the BC-VH
framework to detect and counter privilege elevation attacks through early
detection and appropriate measures.

##  VII Conclusion

This work introduces a novel framework to address limitations in robotic
visual homing environments. The framework utilizes decentralized blockchain
technology for lightweight navigation of a heterogeneous robot team in a wide,
out-of-line-of-sight area. Challenges related to navigation tasks and FOV
sharing are explored, and the BC-VH architectural design is proposed to reduce
resource consumption. Use case scenarios, consensus mechanism establishment,
and a security assessment framework are discussed to evaluate the efficiency
and trustworthiness of BC-VH.

## References

  * [1] C. Lee and D. Kim, “Visual Homing Navigation with Haar-like Features in the Snapshot,” _IEEE Access_ , vol. 6, pp. 33 666–33 681, 2018.
  * [2] X. Ji, Q. Zhu, J. Ma, P. Lu, and T. Yan, “Three Landmark Optimization Strategies for Mobile Robot Visual Homing,” _Sensors_ , vol. 18, no. 10, p. 3180, 2018.
  * [3] F. Fu and D. Lyons, “An Approach to Robust Homing with Stereovision,” _SPIE Defense & Security Conference on Unmanned Systems Technology XX_, April 2017.
  * [4] D. Lyons and N. Petzinger, “Visual Homing for Robot Teams: Do You See What I See,” _SPIE Unmanned Systems Technology XXIV_ , April 2022\.
  * [5] D. M. Lyons, B. Barriage, and L. Del Signore, “Evaluation of Field of View Width in Stereo-vision-based Visual Homing,” _Robotica_ , vol. 38, no. 5, pp. 787–803, 2020.
  * [6] X. Ji, G. Sun, M. J. Er, and Z. Wang, “Adaptive correction of landmark for visual homing in mobile vehicles,” _IEEE Transactions on Intelligent Vehicles_ , 2022.
  * [7] A. Stelzer, M. Vayugundla, E. Mair, M. Suppa, and W. Burgard, “Towards Efficient and Scalable Visual Homing,” _The International Journal of Robotics Research_ , vol. 37, no. 2-3, pp. 225–248, 2018.
  * [8] E. Castelló Ferrer, “The Blockchain: A New Framework for Robotic Swarm Systems,” in _Proceedings of the future technologies conference_. Springer, 2018, pp. 1037–1058.
  * [9] M. Fernandes and L. A. Alexandre, “Robotchain: Uing Tezos Technology for Robot Event Management,” _Ledger_ , vol. 4, 2019.
  * [10] V. Strobel, E. Castelló Ferrer, and M. Dorigo, “Managing byzantine robots via blockchain technology in a swarm robotics collective decision making scenario,” in _Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems_ , ser. AAMAS ’18. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2018, p. 541–549.
  * [11] S. H. Alsamhi, A. V. Shvetsov, S. V. Shvetsova, A. Hawbani, M. Guizan, M. A. Alhartomi, and O. Ma, “Blockchain-empowered security and energy efficiency of drone swarm consensus for environment exploration,” _IEEE Transactions on Green Communications and Networking_ , 2022.
  * [12] P. T. Morón, S. Salimi, J. P. Queralta, and T. Westerlund, “Uwb role allocation with distributed ledger technologies for scalable relative localization in multi-robot systems,” in _2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)_. IEEE, 2022, pp. 1–8.
  * [13] J. Li, J. Wu, J. Li, A. K. Bashir, M. J. Piran, and A. Anjum, “Blockchain-based trust edge knowledge inference of multi-robot systems for collaborative tasks,” _IEEE Communications Magazine_ , vol. 59, no. 7, pp. 94–100, 2021.
  * [14] G. Sun, X. Ji, and M. J. Er, “A Novel Mobile Robot Visual Homing Strategy Based on Pseudo-Isometric Distribution of Landmarks,” in _5th International Conference on Intelligent Autonomous Systems (ICoIAS)_. IEEE, 2022, pp. 231–237.
  * [15] E. C. Ferrer, E. Jiménez, J. L. Lopez-Presa, and J. Martín-Rueda, “Following leaders in byzantine multirobot systems by using blockchain technology,” _IEEE Transactions on Robotics_ , vol. 38, no. 2, pp. 1101–1117, 2021.
  * [16] F. Keramat, J. P. Queralta, and T. Westerlund, “Partition-tolerant and byzantine-tolerant decision-making for distributed robotic systems with iota and ros 2,” _IEEE Internet of Things Journal_ , 2023.
  * [17] N. Malsa, V. Vyas, J. Gautam, R. N. Shaw, and A. Ghosh, “Framework and smart contract for blockchain enabled certificate verification system using robotics,” in _Machine Learning for Robotics Applications_. Springer, 2021, pp. 125–138.
  * [18] U. S. Aditya, R. Singh, P. K. Singh, and A. Kalla, “A Survey on Blockchain in Robotics: Issues, Opportunities, Challenges and Future Directions,” _Journal of Network and Computer Applications_ , vol. 196, p. 103245, 2021.
  * [19] D. M. Lyons and M. Rahouti, “Wavn: Wide area visual navigation for large-scale, gps-denied environments,” in _2023 IEEE International Conference on Robotics and Automation (ICRA)_. IEEE, 2023, pp. 2039–2045.
  * [20] T. UcedaVelez and M. M. Morana, _Risk Centric Threat Modeling: Process for Attack Simulation and Threat Analysis_. John Wiley & Sons, 2015.