Robotics and blockchain in supply chain management: cutting-edge developments and identified barriersAbstract
Abstract In Industry 4.0, robotics is reshaping supply chain management across sectors such as transportation, logistics, agriculture, and manufacturing. This article delivers the first comprehensive review of robot deployments in these sectors, highlighting their intersection with blockchain and examining both implementation barriers and integration challenges. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed 37 studies across various areas of the supply chain. Key findings highlight the maturity of the “Transportation and Logistics” area, where the most widely utilized robots are Automated Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs), and Unmanned Aerial Vehicles (UAVs). Regarding blockchain integration with robotics, we observed that the primary use is to secure communication between robots, employing mechanisms such as smart contracts and proof-of-work. Most barriers to robot implementation within the supply chain fall within the technological category. Two recurrent challenges in this domain include limited travel range (by air or land) and difficulties with collision avoidance, particularly for UAVs and AMRs. Finally, regarding the integration of robots with blockchain, the most significant barriers identified are limited storage capacity, response delays, high power consumption, and insufficient computing power in robots to implement blockchain mechanisms effectively.
Keywords:
Technology; Case studies; Systematic review; Industry 4.0
Resumo
Resumo Na Indústria 4.0, a robótica está transformando a gestão da cadeia de suprimentos nos setores de transporte, logística, agricultura e manufatura. Este artigo apresenta a primeira revisão abrangente das implementações de robôs nesses segmentos, destacando sua interseção com a tecnologia blockchain e examinando tanto as barreiras à implementação quanto os desafios de integração. Utilizando o protocolo Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), analisamos 37 estudos de diversas áreas da cadeia de suprimentos. Os principais achados ressaltam a maturidade do setor de “Transporte e Logística”, no qual os robôs mais utilizados são Veículos Guiados Automaticamente (AGVs), Robôs Móveis Autônomos (AMRs) e Veículos Aéreos Não Tripulados (UAVs). No que se refere à integração da blockchain com a robótica, observamos que seu uso principal é a comunicação segura entre robôs, por meio de mecanismos como contratos inteligentes e prova de trabalho. A maioria das barreiras à implementação de robôs na cadeia de suprimentos é de natureza tecnológica. Dois desafios recorrentes são o alcance de deslocamento limitado (aéreo ou terrestre) e as dificuldades em evitar colisões, especialmente para UAVs e AMRs. Por fim, no contexto da integração entre robôs e blockchain, as principais barreiras identificadas são a capacidade de armazenamento limitada, atrasos na resposta, elevado consumo de energia e o poder computacional insuficiente dos robôs para implementar eficazmente os mecanismos de blockchain.
Palavras-chave
Tecnologia; Estudo de casos; Revisão sistemática; Indústria 4.0
1 Introduction
Robotics plays a pivotal role in supply chain management, particularly within the context of Industry 4.0 technologies. Robotic systems are increasingly used across various supply chain domains, including transportation, logistics (such as warehousing and goods delivery), agriculture, e-commerce, and manufacturing. Cammarano et al. (2024) highlights the immense potential of integrating technologies such as Unmanned Aerial Vehicles (UAVs), autonomous vehicles (AVs), and other robotic systems to enhance warehouse operations, transportation, and delivery services. Nevertheless, the initial introduction of robotic technologies may entail substantial investments and knowledge management challenges. This is primarily due to the need to tailor solutions to unique requirements and to seamlessly integrate various devices within the same workspace, such as a factory.
In the context of blockchain literature reviews, existing studies predominantly focus on specific areas or types of robots. For instance, Fernández-Caramés & Fraga-Lamas (2019) Conduct a literature review on blockchain-based Industry 4.0 applications, partially addressing the integration of autonomous robots and vehicles, particularly Autonomous Mobile Robots (AMR) and UAVs. Another example is Kumar et al. (2023), which conducts a literature review on blockchain use, focusing specifically on underwater applications and Unmanned Underwater Vehicles (UUVs). Huo et al. (2022) perform a literature review on blockchain applications in the Industrial Internet of Things (IIoT) without referencing a specific robot implementation.
This systematic literature review (SLR) provides a structured and comprehensive overview of the state of the art in blockchain-integrated robotics for supply chain management, identifying research gaps to guide future investigations. Moreover, it aims to assist scholars in identifying key advancements and challenges in integrating and implementing robots in supply chains, providing valuable insights into which robotic applications and devices have been implemented or tested and warrant consideration for broader adoption. To the best of our knowledge, this work represents the first comprehensive review in this emerging field.
The structure of this article proceeds as follows: Section 2 provides the necessary definitions and classifications for analyzing the literature review results. Section 3 outlines the methodology for conducting a SLR and the research parameters that will be used. Section 4 details the SLR results and examines the status of blockchain integration with the identified robotic systems. Section 5 discusses the findings, systematically addressing the barriers to robot implementation in the supply chain and their integration with blockchain as highlighted in the literature. Finally, Section 6 offers conclusions and recommendations for future research.
2 Background
According to the International Federation of Robotics (IFR), a robot is a programmable mechanism with multiple axes and some autonomy that operates in its environment to execute specific tasks (Müller, 2023) Mechanisms with fewer programmable axes or that are entirely teleoperated, which meet the criteria for industrial or service robots, are referred to as robotic devices. The IFR categorizes robots into two groups: industrial and service robots. Industrial robots are defined as reprogrammable, self-controlled, and versatile manipulators. They are composed of three or more axes and can be mobile or stationary (Müller, 2023). The IFR emphasizes that the distinction between an industrial robot and a service robot lies in the purpose of their application. Therefore, an industrial robot is specifically designed for industrial applications, such as manipulating components on an assembly line. In contrast, a service robot performs tasks on behalf of humans or equipment, such as providing support in contexts like elderly care, where they can enhance emotional well-being and help reduce loneliness (Barbosa et al., 2024). In a similar vein, Saenz et al. (2023) note that the adaptability of modern robotic technologies enables the reuse of the same robotic hardware across various applications. This is commonly referred to as robotics' “cross-domain” characteristic.
The IFR distinguishes between personal and professional service robots. A personal service robot is utilized for non-commercial tasks. It is typically operated by individuals without specialized training, while professional service robots are employed for commercial purposes and are generally operated by adequately trained personnel. (Müller et al., 2023). ISO 8373:2012 further distinguishes between professional and personal service robots, emphasizing their commercial versus non-commercial utilization (ISO, 2012). Service robots encompass a range of robotic devices, including ground-based, water-based, and aerial vehicles, provided they possess autonomous navigation capabilities that surpass basic autopilot functions, such as maintaining course, altitude, or depth.
Passenger transportation in AVs utilizes navigation technologies like those found in robotics. However, IFR categorizes passenger transportation as part of the automotive industry rather than within the realm of robotics. As a result, it is not classified as an industrial or service robot. Consequently, this study will not involve AVs used for passenger transportation.
2.1 Blockchain technology
As outlined by various sources, blockchain technology is described by Strobel et al. (2020) as databases and computing platforms that are replicated and shared within a peer-to-peer (P2P) networks. According to Dutta et al. (2021), blockchain is characterized as a distributed tamper-resistant database accessible to all, with no single controlling entity. Furthermore, Kumar et al. (2023) define blockchain as a distributed and transactional database that facilitates safe data storage and processing among numerous network members. Huo et al. (2022) state that blockchain technology is an immutable distributed ledger that records transactions in a chronological chain of blocks linked through hash values.
All these blockchain definitions share standard features that underscore their unique attributes. Firstly, it operates as a distributed system, with databases and computing platforms replicated and shared among participants within a P2P network. This decentralized nature ensures that no single entity controls the tamper-resistant database, thereby emphasizing the security and integrity of the stored information. The technology functions as a secure, distributed ledger, enabling secure data storage and processing across a vast network of participants. Notably, the chronological recording of transactions in a chain of blocks linked via hash values ensures an immutable and transparent transaction history. In summary, the typical characteristics of blockchain, according to these definitions, include decentralization, tamper-resistance, secure database functionality, and an immutable, chronological transaction chain.
In Fernández-Caramés & Fraga-Lamas (2019), access regulation, permission types, incentive types, and operating mode determine blockchain classification. Table 1 shows this.
Huo et al. (2022) state that blockchain architecture comprises five layers: network, data, consensus, control, and application. The network layer emphasizes P2P communication, employing structured, unstructured, and hybrid P2P networks. Communication involves propagation, connection, and interaction logic layers fortified by security mechanisms that ensure data integrity. The data layer focuses on defining data connections and structure, using algorithms to enable efficient verification and ensure tamper-proof characteristics. The consensus layer addresses ledger data consistency, tackling challenges such as the Byzantine Generals Problem through protocols like Proof of X (PoX), Byzantine-fault-tolerant (BFT), and Crash-fault-tolerant (CFT). These protocols employ reward and punishment mechanisms, voting, and crash tolerance mechanisms to ensure data consistency and security. The control layer serves as the hub for application-led interaction, encompassing a processing model, control contract, and execution environment, thereby ensuring stable portability and interoperability. Finally, the application layer facilitates interaction between layers and applications, such as decentralized applications (DApps), cryptocurrencies, smart cities, and enterprise applications, through APIs.
A detailed description of the inner workings of blockchain technology is out of the scope of this study. Still, the interested reader can find further information on the basics of blockchain in Fernández-Caramés & Fraga-Lamas (2019), Huo et al. (2022), Kumar et al. (2023) and Strobel et al. (2020).
3 Research design
The study employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. (Moher et al., 2009). PRISMA comprises evidence-based elements designed to enhance the reporting of systematic reviews and meta-analyses.
3.1 Study protocol
Table 2 presents the main guiding question, objectives, keywords, and inclusion and exclusion criteria for this search.
3.2 Study selection
Scopus was selected as the primary database for this research due to its extensive coverage of relevant studies in technology and engineering. The following keywords were used for the search: (“survey” OR “review” OR “systematic review” OR “systematic literature review” OR “state-of-the-art”) AND (“robot”) AND (“logistics” OR “supply chain” OR “blockchain”). Given the vast number of studies on “robots” and “supply chain,” retrieving and reviewing all available literature would be a formidable task. Therefore, this study incorporated the keyword “systematic literature review” for two primary purposes: first, to identify existing literature reviews that specifically address the integration of robots in the supply chain with blockchain technology—the central research question of this systematic review—and second, to refine the search results.
After applying filters to include only journal studies written in English and available as open access, 237 documents were retrieved. The exported articles were input into Microsoft Excel for analysis. The study selection process is illustrated in Figure 1, which is based on the elaboration by Haddaway et al. (2022). First, the titles, abstracts, and keywords were screened to identify studies that matched or potentially matched the research objective. Then, the full texts of potentially matching studies were reviewed to determine eligibility, in accordance with the exclusion criteria outlined in Table 2. Finally, 34 studies were included in this process.
In addition to the PRISMA methodology, three distinct documents were incorporated into the SLR due to their significant contributions to integrating blockchain with robotic technology. These documents were retrieved from reputable databases, specifically Web of Science, ScienceDirect, and IEEE Xplore, one document from each source. As a result, the total number of studies included in this systematic review amounts to 37.
4 Results
The 37 selected studies encompass topics related to the implementation of robots in the supply chain and the integration of robotics with blockchain technology. These studies were categorized by the following parameters: year of publication, study type, primary application areas, robot type, and blockchain application.
4.1 Bibliometric review
This section provides statistics related to the year and keywords derived from the metadata of the studies included in the review. Figure 2 illustrates the studies by year, spanning 10 years. There is an observable upward trend in the number of studies and literature reviews on robots in the supply chain, with a significant increase over the last three years (2021-2023). Approximately 35% of the studies in this review are from 2023, and 78% are from the last three years.
Figure 3 displays the keyword frequencies from the studies included in this review, organized by frequency. The top five most relevant terms within the keywords are “smart” and “logistics,” each appearing 10 times, followed by “robotics,” “robots,” and “robot,” with 9, 8, and 7 occurrences, respectively. In total, terms related to robots account for 24 instances, making it the most frequently used keyword category.
4.2 Studies classification
Figure 4 illustrates the proportion of studies included in this review, distinguishing between case studies and literature reviews. There are 17 case studies and 20 literature reviews, including state-of-the-art reviews, surveys, systematic reviews, and other studies related to this concept.
4.3 Main application areas
To categorize the areas of application, we relied on the top industries that report emergency practices in Cammarano et al. (2024). The areas of application for the studies included in this review are presented in Figure 5.
The “Transportation and logistics” area includes delivery, logistics, and warehousing, as shown in Figure 6. In the delivery field, we find studies such as Boysen et al. (2021) which explores various transportation modes for last-mile delivery and addresses specific decision challenges in establishing and operating them. These challenges encompass infrastructure setup, including the design and capacity planning of storage facilities; staffing and fleet sizing; workforce planning for delivery personnel; and routing and scheduling for alternatives such as UAVs and AMR robots.
In the logistics field, Tsolakis et al. (2022) conduct a literature review to identify the advantages of employing Automated Guided Vehicles (AGVs) for port container loading and unloading. The study also examines how Artificial Intelligence (AI) can improve AGVs performance and enhance container port management. Finally, it proposes two algorithms: “Loop Routing” and “Shortest Distance Loop Routing” to optimize AGV travel time while considering associated environmental impacts. This is demonstrated through a case study examining the effect of gaseous emissions and energy consumption associated with AGVs.
Within the warehousing field, Żuchowski (2022) conducts a literature review on the implementation of consulting projects for warehouses specializing in the processing of pelletized goods. The study aims to determine the feasibility of using available storage technologies to replace human labor in creating smart warehouses.
Within the “General” area, studies do not focus on a specific industry but encompass multiple sectors. For instance, Saenz et al. (2023) conduct a literature review on collaborative robot (cobot) safety standards and develop a web-based toolkit, COVR, that presents safety standards tailored to the robot's specific application. These applications can range from healthcare (as demonstrated by the DOROTHY project, utilizing two industrial cobots for rehabilitation treatment), agriculture (seen in the HYDROCOBOTICS project, employing a gripper and AGV for assisting in hydroponic indoor environments), to manufacturing (as illustrated by the COSMO project, using a gripper to minimize the time required for setting up and adjusting workstations for new products). The studies explain that the same robot can be used for various applications, emphasizing that safety standards depend more on the type of application than on the robot itself.
In “Agriculture,” we encounter Katiyar & Farhana (2021), which describes the automation of agricultural activities using sensors, AI, and robots such as robotic arms and UAVs to enhance productivity. In Dadi et al. (2021), the authors conducted a literature review on the transformation of the supply chain in the agri-food sector through emerging Industry 4.0 technologies, including AI, Internet of Things (IoT), robotics, blockchain, big data, and RFID. Regarding robotics, the review identifies studies and processes that utilize AMRs and UAVs.
Misra et al. (2022) present a literature review on the role of IoT, big data, and AI in the future of the agri-food sector. The discussion covers the role of IoT and big data analysis in applications such as greenhouse monitoring, intelligent farm machines, and UAV-based crop imaging. The author also explores the use of robots, including robotic grippers, AVs, and AGVs, to automate agricultural activities.
In Rose & Bhattacharya (2023), a survey conducted in the UK explores the factors influencing the adoption of autonomous robots on soft-fruit farms. The study adopts a farmer’s perspective, investigating soft fruit growers' opinions on the implementation of robots. The findings reveal several key factors that influence implementation, including high costs, infrastructure, data ownership, cybersecurity, skill requirements, and trust. Ciruela-Lorenzo et al. (2020) present a literature review of the development of digital technologies, including IoT, robots (such as UAVs), AI, big data, and blockchain, in agri-cooperatives. The study proposes a digital diagnostic tool to measure the level of digital innovation in agri-cooperatives and aid decision-making processes. Dutta et al. (2021) explore the status and challenges of multi-robot information gathering in precision agriculture. The authors clarify that precision agriculture uses hardware and software technologies for informed decision-making, and this information gathering is satisfied by AMR and UAV robots. The study also analyzes how blockchain, with consensus protocols among robots, ensures integrity against attacks while optimizing information collection despite adversarial influences.
In the “Manufacturing” area, Xie et al. (2023) The authors propose a model to improve robotic grasping for novel objects with diverse shapes and textures. The approach involves soft grippers and data-driven learning in a hyper-personalization line. They conduct market research on tactile sensors and soft grippers, considering factors such as price, sensitivity, simplicity, and modularity.
In “Construction,” AlRushood et al. (2023) present an optimization model for warehouse supply chain management using UAVs and AI. Integrating UAVs and IoT ensures inventory accuracy and real-time visibility, enabling remote access for warehouse workers. The case study focuses on the prefabrication of pipe spools. In the “Food and beverage” area, Mahmood et al. (2021) propose a model for analyzing intralogistics automation on the production floor. This involves AMRs, 3D visualization, simulation, and IoT sensors. The aim is to assist managerial decision-making. The authors emphasize the importance of developing 3D visualization and simulation tools to assess the feasibility and effectiveness of mobile robots before their implementation in production.
In “Retail,” Costanzo et al. (2018) introduce a real-time, flexible motion-planning method for robotic gripper-based object manipulation in in-store logistics. State-of-the-art techniques are used for object recognition and localization to achieve robustness and accuracy. The goal is to ensure collision-free trajectories for unexpected objects. Finally, in the “water” area, Kumar et al. (2023) conduct a literature review on the use of blockchain in underwater applications, with a focus on architectures. The authors highlight the security and privacy challenges for UUVs and discuss the obstacles to providing safe and optimal resource allocation in underwater communication networks.
4.4 Application overview
Figure 7 illustrates the robots found in the literature, as discussed in Section 2, which can be categorized as industrial and service robots. The industrial category comprises robotic arms and robotic grippers. In the service robot category, the literature encompasses a diverse range of technologies, including UAVs, AMRs, AGVs, autonomous robots, cobots, Intelligent Autonomous Vehicles (IAVs), UUVs, AVs, exoskeletons, Mobile Robot Manipulators (MRMs), Unmanned Surface Vehicles (USVs), and wearable robots. It is important to emphasize that robots identified as autonomous robots, cobots, AVs, and wearable robots are not specific types of robots but rather general categories. For instance, in the case of AV, it could be a broader category that includes UAVs. However, Misra et al. (2022) do not specify the types of AVs mentioned. Nevertheless, this review includes these categories to encompass all mentions of robots in the studies.
This review does not include robots identified in the literature whose applications extend beyond the supply chain. This is exemplified by Shen et al. (2021), which explores robots deployed across various fields during the pandemic, such as diagnosis and screening, disinfection, surgery, telehealth, social care, logistics and manufacturing, and other applications (agriculture, security, construction, and other hospital applications). In the cited example, only agricultural, logistics, and manufacturing robots are included in the systematization, as the activities identified in the text are recognized as part of the supply chain.
Studies like Rathee et al. (2022)which primarily focus on developing a blockchain-based mechanism to establish a secure communication environment for drones and UAVs in a smart city, do not strictly tie robot applications to the supply chain. However, they are included because this literature review explores studies that address the integration of blockchain and robots, whether in the context of the supply chain or not.
In summary, the most frequently encountered robot types in the review are UAVs, AMRs, AGVs, robotic grippers, and robotic arms. UAVs account for approximately 23% of the robots found, followed by AMRs at 21%. AGVs represent 16%, while robotic grippers and robotic arms account for 13% and 5%, respectively.
Table 3 illustrates the robots categorized by the industry area of each study included in this review. The area with the most robots is “Transportation and logistics.” Within this group, UAVs, AMRs, and AGVs are the most studied robots in the literature, with 7, 8, and 8 studies, respectively. The “General” industry area has the second-highest number of robots included in studies. Still, for each type of robot, there is a lower number of robots; the maximum value is three studies. Consequently, the literature does not provide a conclusion or make any remarks on this area. Further explanation will be provided in Section 5.
As previously established, “Transportation and Logistics” includes the Delivery, Logistics, and Warehouse subareas. Table 4 shows the types of robots for each subarea. We noted that UAV robots were more prevalent in delivery, while AGV robots were more commonly found in logistics and warehouses.
Table 5 provides a detailed overview of robot integration with blockchain identified in this SLR. It presents the study title, a concise description of its objective, the type of robot involved, a brief outline of its function, and the corresponding industry application. As shown in Table 5, the primary areas of integration between robotics and blockchain are transportation and logistics, manufacturing, and water management.
4.5 Blockchain integration overview
This section presents studies that focus on integrating blockchain technology with robotics. Of the 37 studies included in this SLR, seven (representing 16%) specifically address this subject (see Figure 8).
In Rathee et al. (2022), the authors propose a blockchain-based mechanism for secure communication among UAVs in a smart city, classifying these devices as either legitimate or tampered. To ensure communication security, the proposed model incorporates three trust schemes: behavior-based, local trust, and blockchain. The behavior-based scheme evaluates the device’s behavior, local trust assesses the surrounding environment, and the blockchain is integrated with both schemes to enhance communication. Each block generated by every device undergoes validation by pre-selected miners before being added to the network. The block contains data such as device ID, tracking number, information to be sent, and time nonce. The information in the block can be a smart contract. The authors assess the effectiveness of the results in terms of throughput, accuracy, latency, block time, size limit, hash rate, fees, and costs.
In Strobel et al. (2020), the authors aim to establish consensus in collective decision-making within a swarm of robots, addressing tasks such as path selection, spatial aggregation, and collective sensing, even in the presence of malfunctioning robots, referred to as Byzantine robots. The approach involves using smart contracts to store snippets of programming code. Each network node (or robot) operates a virtual machine that executes these code snippets. The authors advocate using smart contracts to establish a robust infrastructure for implementing greater control in robot swarms. In this framework, information from each robot is securely stored, aggregated, and processed through a smart contract. This ensures that information or control commands are based on a consensus within the swarm. The authors assess the effectiveness of the results in terms of absolute error, consensus time, harm, and the estimated percentage of white tiles.
Fernández-Caramés & Fraga-Lamas (2019) Conduct a literature review on blockchain-based Industry 4.0 applications, emphasizing the advantages and challenges of utilizing smart contracts to develop cyber-secure industrial applications. They assert that blockchain enables autonomous industrial robots and vehicles to engage with other entities through smart contracts. The study provides reviews of blockchain applications in UAVs for coordinating air routes and in AVs for ride-sharing services.
In Dutta et al. (2021), the authors conduct a literature review on the current state and challenges of multi-robot information gathering in precision agriculture. They note that one of the most significant security challenges involves attacks on the integrity of collected information, as there is no universal standard for computer systems supporting precision agriculture security. The authors propose that blockchain-based consensus protocols could serve as a solution to this challenge, as they can mitigate the impact of integrity attacks while maintaining efficient information collection in the presence of adversarial influence. However, they acknowledge the drawback of high energy consumption in existing blockchain implementations, emphasizing the need to strike a balance between security and resource consumption. The study suggests employing engineering constructions such as Merkle trees to address this issue, utilizing a generic model for energy optimization in algorithms.
Kumar et al. (2023) review the application of blockchain in underwater contexts, examining architecture, challenges, and solutions. The benefits of integrating blockchain into UUV applications include decentralization, enhanced network security, improved data traceability, transparency, data privacy, integrity, and cost reduction. Challenges include evaluating the cost-benefit of power usage, addressing concerns about throughput, data concurrency, network connections, and massive data processing, as well as maintaining transparency and communication privacy within the UUV network. Blockchain in UUV applications can prevent collisions, distribute service loads evenly, authenticate data and entities, and facilitate collaboration and synchronization. It is also a tool to enhance the security of UUV networks. The authors also outline the limitations of those studies.
In Xianjia et al. (2021), the authors conduct a literature review on the role of Federative Learning in enhancing the autonomy and intelligence of robotic systems. They note that blockchain has been utilized in robot swarms to handle byzantine agents, share computational and communication resources, and address privacy-critical applications. Studies on the applications of blockchain-enhanced FL primarily focus on establishing a trustworthy network for AVs.
In Huo et al. (2022), the authors review the applications of blockchain in the IIoT. They highlight that data in an industrial environment often originates from connected devices, including industrial sensors, swarm robots, and vehicles. Consequently, challenges arise in ensuring secure and fair data, fair models, and robust cyber infrastructures. Blockchain is identified as a solution to meet demands for trust, security, and other requirements, such as ensuring data authenticity across devices, sharing computing resources with AI and IIoT, and enhancing decentralized, collaborative AI model analysis in IIoT.
In summary, as shown in Figure 9, there are five literature reviews on blockchain technology related to underwater applications or precision agriculture, and only two case studies are available. The first application is presented in Rathee et al. (2022), where the type of access regulation is a consortium, that is, each device is authorized to enter the network, the miners are preselected, the type of incentive is non-tokenized, and the operation mode is logic-oriented, as the miners must execute a logic to classify each block as secure or insecure. The second application is presented in Strobel et al. (2020), where a customized Ethereum network is used. The access type is private since there are no pre-selected miners. It is an open robot swarm in which robots are free to join and leave the network at any time. For example, a hardware failure is still permitted because the robots' network is authorized. The incentive is tokenized; miners (robots) are rewarded every time they solve a block in the Ethereum network. Moreover, the operation mode is logic-oriented: a smart contract executes every time a block is mined. Therefore, the typical characteristics of the two applications reviewed are that both present a logic-oriented mode of operation via smart contracts and are permissioned blockchains, in which network nodes are known and pre-authorized to join.
5 Findings
As seen in Section 4.4, Application Overview, “Transportation and logistics” is where most studies in this review have been classified, with 18 studies (Figure 5). It contains the highest number of robots found in the literature, totaling 32 robots (Table 3), grouped as shown in Figure 10. AMR, AGV, and UAV are the robots most frequently mentioned in this area. Given that this area and these robots are the most relevant and offer an opportunity to systematize their descriptions, applications, and approaches, this section and Section 5 will focus on them.
Some common aspects emerge from the findings on integrating blockchain and robots. A prevalent feature is the use of smart contracts, which are snippets of code that resemble transactions generated by each robot. Additionally, a Proof-of-Work mechanism is utilized to achieve consensus. The primary application focuses on ensuring secure communication across a fleet of robots to facilitate optimal operation. The literature finds that the robots studied for integration with blockchain are UAVs, AMRs, UUVs, and USVs.
According to Fernández-Caramés & Fraga-Lamas (2019), one of the fundamental principles of Industry 4.0 is the comprehensive collection of data from various elements across the value chain. This process must be swift and efficient to serve its purpose effectively within a factory. Systems responsible for data collection are intricately designed to acquire, store, process, and exchange information with devices located in factories, supplied by vendors, or owned by clients. Robots, such as UAVs, can monitor inventory and collect data (AlRushood et al., 2023), and by using UAVs and AGVs in data collection for precision agriculture (Dutta et al., 2021). This data needs to be securely stored, which requires integrating with blockchain technology. Nevertheless, blockchain integration with a multi-robot information collection system is still in development (Dutta et al., 2021).
5.1 Barriers
As in the previous section, the identification of barriers to implementing robots in the supply chain will be limited to studies in the “Transportation and Logistics” domain. In this review, barriers are categorized following the classification proposed by Ludwig et al. (2023) for the implementation of Voice User Interfaces (VUIs) in industrial environments, which includes three main groups: knowledge-related, economic, and technological barriers. Additionally, two further categories are introduced to address the specific nature of the barriers identified in this review: environmental and regulatory barriers.
Knowledge constraints are limits in understanding that result from either insufficient research or a lack of expertise in the workforce. Technological constraints involve technical barriers in developing robotic device hardware or software, or in the infrastructure conditions required for implementation. Environmental constraints pertain to the physical surroundings that influence the difficulty and safety of navigating a specific area, such as weather conditions or the layout of a workshop. Regulatory barriers encompass legislative or normative constraints that are enforced. Economic barriers stem from the high costs of implementing a robotic solution that enables the robot to function effectively. Table 6 presents all barriers identified in the literature for the “Transportation and logistics” sector, along with their classification into five groups.
In the case of Huang et al. (2022), Santos et al. (2021), Song et al. (2023), Gelareh et al. (2013), Stavrou et al. (2018), Rennie et al. (2016) and Rathee et al. (2022) The limitations or constraints identified in the study are specific to the simulation's implementation; therefore, they cannot be considered general barriers to robot implementation. Additionally, the study does not describe any general barriers.
As shown in Table 6, most barriers are concentrated in the technology category, comprising 18 (40% of the total). Two recurrent barriers in this domain are the limited travel range (by air or land) and collision avoidance capabilities during journeys, particularly for UAVs and AMRs. One contributing factor to the restricted travel range is battery life, as many UAVs use small batteries that typically last less than 30 minutes. These two barriers necessitate that robot operators be in proximity. For UAVs, regulations specify visibility ranges for operators, such as Visual Line-Of-Sight (VLOS) (Saunders et al., 2024), requiring the UAV to remain within the operator’s visible range to enhance collision avoidance capabilities. This helps mitigate potential harm to individuals or property during emergency landings or device falls. In the case of UAVs, there is also a limitation on carrying heavy or large-sized cargo. Additional technological barriers include constraints in digital infrastructure and internet access across various industries, due to geographical restrictions, which pose risks of system shutdowns and dependence on spare parts for robot components. Barriers are also identified concerning the technical capacity of the workforce to standardize multiple robots operating in the same environment and address failures in the early stages of robot implementation.
Regarding the group of regulatory barriers, the most notable ones include normative restrictions that define visibility ranges between the operator and UAV, the prohibition of UAV flights in congested areas, airports, government or military zones, and the restriction of UAV flights over private property, which may be considered trespassing. Additionally, UAVs may use images of the surroundings to guide their flight or establish landing zones, and handling this data is sensitive, especially when it involves private property. Other constraints encompass the minimum altitude requirements for certain flight zones and societal perceptions regarding the use of these devices.
Within the knowledge barriers group, two primary obstacles emerge. The first focuses on the limited literature on the risks associated with UAV use, including studies on UAV energy consumption models, sensor challenges, and blockchain applications for routing, tracking, safety, and data privacy for UAVs and AMRs. The second barrier involves the workforce's and society's acceptance of UAVs, coupled with the limited availability of trained personnel for UAV and general robot use.
Lastly, the environmental and economic barriers come into play. These include challenging conditions for UAV flights, such as rain or wind, risks of AMR theft, and financial limitations for robot implementation. This involves establishing depots as a strategy for the short flights or land travel of UAVs and AMRs.
Table 7 shows the barriers found in the SLR for blockchain implementation in robotics.
As shown in Table 7, the predominant obstacles hindering blockchain integration in robotics are primarily clustered within the technological category. This accounts for 19 barriers, constituting 83% of the total impediments. Noteworthy recurring themes within this domain include constraints on robots’ storage capacity for storing all transactions, which become particularly challenging as the network expands. Additionally, barriers arise from delays in robots’ response times, attributable to the intricacies of the consensus mechanism, as well as increased power consumption required for blockchain performance, which affects robot battery life. Furthermore, limitations in robots’ computing power hinder their ability to execute blockchain mechanisms effectively. Other barriers identified in the literature encompass network connectivity issues and concerns regarding transparency and communication privacy.
6 Conclusion
This study aims to identify the current state of blockchain integration with robotics and the barriers associated with integrating and implementing robots in the supply chain through an SLR. Using this approach, we answer the research question through an SLR, as summarized in Tables 5, 6, and 7. We provide a comprehensive mapping of robotics integration with blockchain, identify the barriers to robot implementation in the supply chain, and highlight the challenges associated with integrating robots and blockchain, respectively.
To contextualize the barriers to robot implementation in supply chains, we mapped the areas where robots have been applied. We found that the “Transportation and logistics” domain is the most mature area in the literature concerning robot implementations or potential uses in the supply chain. In contrast, areas such as “General,” “Agriculture,” “Manufacturing,” “Construction,” “Food and Beverage,” “Retail,” and “Water” have limited literature, necessitating greater efforts to study robot use in both the literature and existing real-world implementations.
Consequently, the “Transportation and Logistics” domain was selected to examine the robots most prominently implemented in supply chain contexts. Within this domain, the literature highlights AGVs, commonly used in indoor environments without public traffic, such as moving on rails to reorganize inventory within a warehouse or transferring materials from one station to another in a factory. AMRs and UAVs are used to deliver packages, including medical products, food, and other goods, in outdoor environments with public traffic, both on land and in the air. Additionally, AMRs are being explored as an alternative to AGVs in indoor environments, although research is ongoing to achieve reliability and safety comparable to that of conventional AGVs (Hažík et al., 2022).
Other robots, such as robotic grippers, robotic arms, IAV, UUV, MRM, USV, and exoskeletons, have limited literature on supply chains, making them more difficult to study in both the literature and in existing real-world implementations. As for the categories of Autonomous robots, Cobots, AVs, and Wearable robots, they are general categories rather than specific types. They have been included to illustrate their mention frequency in studies, which is insignificant and refers to specific robots, even though the studies do not provide detailed specifications.
Based on this analysis, we identified the “Transportation and Logistics” domain as the most mature area in the literature, with AGVs, AMRs, and UAVs featured most prominently. Concerning the barriers to implementing these robotic systems, our analysis indicates that the primary challenges lie within the technological domain. We found that the main barriers to implementing robots in the supply chain are primarily technological. These barriers are limited travel range (by air or land) and collision-avoidance capabilities during journeys, particularly for UAVs and AMRs.
Regarding the obstacles in implementing blockchain with robots, it has been identified that the most significant challenges include limitations in robots’ storage capacity to manage all transactions, delays in robots’ response times due to the execution of the consensus mechanism, increased power consumption impacting robot battery life, and constraints in robots’ computing power for the effective execution of blockchain mechanisms.
From a managerial perspective, the findings of this research provide valuable guidance for supply chain managers and practitioners in assessing the feasibility of implementing robotics—either independently or in combination with blockchain—while proactively mitigating the barriers identified in Tables 6 and 7. For instance, the primary barriers to robot implementation, such as limited travel range and insufficient collision avoidance capabilities, should be systematically evaluated through pilot projects before committing to large-scale investments. Similarly, the technological challenges associated with blockchain-enabled robotics—such as storing all transactions, delays caused by consensus mechanisms, and the resulting impact on power consumption—require companies to establish dedicated IT teams with the necessary expertise to design, test, and maintain solutions tailored to their specific operational requirements.
Additionally, companies should invest in pilot projects to test feasibility and scalability, form cross-functional teams with expertise in IT, operations, and security, and develop clear internal policies for data governance and cybersecurity in environments with bots and blockchain. This becomes particularly relevant given that literature does not yet provide a dominant model for selecting and implementing Industry 4.0 technologies; instead, requirements tend to vary across organizations, although factors such as technological maturity and economic feasibility consistently emerge as key considerations (Silva et al., 2022).
Notably, only seven articles in this SLR (37) delve into integrating blockchain with robots. This constitutes a relatively small dataset, indicating that this research area is still in its early stages of development. The limited number of studies indicates that insufficient research has been conducted in the area analyzed to date.
As future research directions concerning the integration of robots into the supply chain, it is essential to emphasize the need for empirical case studies in real-world environments that encompass both UAVs and AMRs. Such studies would enable the validation of existing theoretical models, refinement of energy consumption estimates, and a deeper understanding of the challenges involved in implementing energy solutions across both urban and industrial contexts. In the specific case of AMRs, the relevance of complex scenario simulations should also be highlighted, as they can explore conditions of greater operational variability and optimize their coordination with other elements of the logistics chain.
Regarding the integration of robots and blockchain, there is a clear need to develop resource optimization models to minimize energy consumption associated with P2P communication, consensus algorithms, and blockchain data structures. These advancements are crucial for enabling the use of such technologies in battery-constrained devices, ensuring both energy efficiency and adequate system performance.
Another vital research avenue concerns the factors influencing social acceptance and public perception of UAVs. Investigations in this area may support the development of regulatory and design strategies that address privacy and security concerns, thereby facilitating the adoption of UAV-based solutions, including in sensitive locations such as historic city centers. In this context, fostering interdisciplinary collaborations—particularly with regulatory bodies—becomes essential to inform the design of policies such as no-fly zones and ensure that technological development aligns with societal and legal expectations.
Finally, the importance of training programs and technical skill development must be underscored. Prior research highlights that factors such as the availability of workers with ICT skills, the continuous search for information, partnerships with research and technological development institutions, among others, are key factors that need to be strengthened for the adoption of Industry 4.0 technologies, such as robotics (Baio & Carrer, 2022). Furthermore, the active participation of workers in the decision-making and implementation process is essential, as it fosters continuous improvement and helps address the challenges associated with the adoption of new technologies (Muniz et al., 2023).
Despite this, the present study has some limitations. It does not explicitly address how these human-capability requirements can be developed to manage the complexity of integrating such technologies, particularly in overcoming knowledge-related barriers. This gap represents a relevant avenue for future research. In line with this, prior studies emphasize the need to complement a technology-driven perspective with a people-oriented approach. For instance, Ribeiro et al. (2022) highlight the importance of continuous learning, adaptation, and the identification and sharing of critical knowledge, while also underscoring the role of communication, organizational culture, and trust in fostering worker engagement and facilitating innovation..
It is crucial to highlight that this study represents the first comprehensive attempt to identify the integration of robots with blockchain technology, the barriers for implementing robots in the “transportation and logistics” sector, and the implementation of blockchain in robots. The taxonomy of barriers can serve as a comprehensive checklist for companies planning to implement robotics and/or blockchain, helping them anticipate and mitigate challenges from the strategic planning stage. This analysis takes a broad scope, considering the topic in general rather than focusing solely on specific case studies or types of robots. Consequently, there is no opportunity to reference results from similar studies. This suggests a potential avenue for further, more detailed investigations.
Acknowledgements
The authors thank Espaço da Escrita – Pró-Reitoria de Pesquisa – Universidade Estadual de Campinas – for the language services provided.
Statement on Data Availability
The authors declare that they have not used data that require availability in a database.
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Financial support:
None.
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How to cite:
Lujan Alaya, T., Azevedo, A. T., Ignácio, P. S. A., & Morini, C. (2026). Robotics and blockchain in supply chain management: cutting-edge developments and identified barriers. Gestão & Produção, 33, e5124. https://doi.org/10.1590/1806-9649-2026v33e5124
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Editor-in-Chief
Pedro Munari
