Decentralized unmanned aerial vehicle (UAV) swarms require low-latency peer communication while remaining resilient to spoofing, replay, command injection, key compromise, and malicious membership changes. This study develops a zero-trust communication framework that separates the real-time swarm data plane from a permissioned Byzantine-fault-tolerant trust ledger. The method is grounded in an existing GPS-denied UAV software baseline implementing canonical packet hashing, HMAC-SHA256 authentication, trust epochs, timestamp and sequence freshness checks, onboard security-state transitions, command-policy gating, and firmware trust records. The proposed extension introduces per-node identities, authenticated session establishment, AEAD-protected peer traffic, and event-sparse ledger anchoring for trust-changing evidence. Formal models are derived for message acceptance, trust dynamics, Byzantine tolerance, consensus traffic, storage growth, processing overhead, and energy cost. Under a representative analytical case of 100 swarm messages/s, a 1% anchoring ratio reduces ledger event rate and modeled consensus-control traffic by 100 times compared with per-packet anchoring. Repository benchmark measurements are reported separately from blockchain projections. The analysis supports using blockchain as a decentralized trust anchor rather than as a transport for flight-critical telemetry.
The integration of real-time capacity optimization suites within legacy civil aviation computing ecosystems is highly bottlenecked by the severe structural heterogeneity of distribution data infrastructures. Telemetry and transactional feeds remain highly siloed across disparate legacy Global Distribution System (GDS) alphabetic fields, Low-Cost Carrier (LCC) direct APIs, and non-public multi-alliance loyalty program ledger inventories. This paper presents a sovereign computational architecture engineered to achieve distributed eventual consistency across these fragmented environments. The system introduces an automated Heterogeneous Data Fusion (H-Pipeline) layer that aggregates high-frequency multi-source distribution data streams into a unified, encrypted semantic vector space through specialized vector dimension coalescing protocols operating under strict TLS 1.3 mutual authentication frameworks. To intelligently parse and navigate these multi-source streams, the architecture deploys an asynchronous, cloud-native Large Language Model (LLM) orchestration middleware running entirely within serverless stateless edge containers (AWS Wavelength/Cloudflare Workers meshes). The cloud-native LLM layer is established as an asynchronous predictive semantic router, dynamically identifying macroeconomic anomalies, unexpected capacity imbalances, and transient route volatility without introducing synchronized write-back overhead or data persistence bottlenecks to critical On-Line Transaction Processing (OLTP) reservation threads. Simulation-based performance evaluation utilizing industry-standard benchmark datasets confirms single-digit millisecond failover recovery bounds, a strict 12 ms cross-border fiber pathway propagation convergence limit, and total mitigation of cross-region distributed semantic drift, establishing a robust computational foundation for next-generation asynchronous AI airline operations.
Aircraft maintenance records are critical to airworthiness and asset valuation, yet they are often fragmented across stakeholders, creating verification bottlenecks and information asymmetry that may suppress aircraft residual value. This paper proposes a blockchain-anchored decentralized application (dApp) based on a dual-layer architecture that combines InterPlanetary File System (IPFS)-based off-chain storage with on-chain anchoring of Content Identifiers (CIDs) and selected metadata. With respect to off-chain file size, the on-chain payload per record remains $\mathcal{O}(1)$, compared with $\mathcal{O}(n)$ for direct on-chain file storage. The architecture incorporates metadata and traceability controls informed by Federal Aviation Administration (FAA) electronic recordkeeping guidance. The main contribution is an economic framework that models the relationship between tamper-evident maintenance-record provenance, audit workflow duration, aircraft residual value, and operational cost. In a 7-kB experiment conducted on the BNB Smart Chain testnet, CID anchoring reduced gas consumption by 93.9\% compared with direct on-chain storage. Under explicitly stated scenario assumptions, the audit-cost model indicates potential savings of more than 90\%. These results support the technical feasibility of the prototype and illustrate its economic potential, while the estimated financial benefits remain to be validated using operational data.
A privacy-preserving compliance audit architecture for unmanned aerial vehicle (UAV) swarm operations. The central contribution is a deconfliction-to-containment reduction: rather than comparing n trajectories after the fact (a quadratic, disclosure-bound check), a planner assigns pairwise-disjoint spatial tubes before take-off and establishes their separation once, so that each vehicle subsequently attests only that its own samples stayed inside its own tube. Collision-freedom follows as a consequence (Theorem 2), and the pairwise cost is paid a single time at planning. The commitment layer (Layer 1) is implemented and evaluated as a decision-support audit pipeline that produces non-disclosing, tamper-evident audit artifacts via pre-flight Merkle commitments. It is evaluated in an emulated UAV swarm environment with systematic adversarial injection, in configurations up to 200 vehicles × 500 samples (100,000 sample statements), reporting artifact size, commit/prove/verify/disjunction times, and tamper-detection rates. We then formally identify the security boundary of the implemented layer: it provides coordinate hiding and tamper evidence, but cannot by itself make self-reported containment truthful, which we state as a security game and an impossibility result (Theorem 3). We specify the additional soundness layers (range proof, continuity, provenance and freshness, and aggregation) needed for full containment assurance, proving that composing a knowledge-sound range argument closes the gap (Theorem 4). Throughout, we separate the implemented and measured Layer 1 from the specified and proved—but not yet benchmarked—Layers 2–4, and we make no claim of full zero-knowledge geofence compliance, of swarm-scale deployment, or of deployment readiness.
The aviation industry depends on data integrity across supply chains spanning OEMs, MRO organizations, airlines, lessors, and national regulators. Centralized data management systems — still dominant in the sector — expose the ecosystem to single points of failure and provide limited traceability of millions of aircraft parts circulating annually. This paper presents a structured review of blockchain-based security architectures for aviation networks, synthesized from 14 peer-reviewed sources published between 2018 and 2025, retrieved from IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and Wiley/Hindawi. On this basis, a thirteen-step design method is proposed for integrating permissioned blockchain with distributed cloud infrastructure in aviation environments. The method is grounded in quantitative acceptance criteria — throughput ≥ 500 TPS, smart contract execution latency < 200 ms (p95), system availability 99.9% — and maps each design phase to specific security controls (integrity, access control, auditability, privacy, resilience, governance). Core mechanisms are formalized via hash-chain integrity verification, attribute-based access control functions, zero-knowledge proof verification, and a composite pre-ledger trust-scoring model. The principal finding: permissioned blockchain architectures — Hyperledger Fabric in particular — can support aviation requirements for immutable audit trails, decentralized identity management, and regulatory compliance with EASA and FAA; adoption remains constrained by organizational readiness and the unresolved GIGO problem at the ledger boundary.
Background: Airport security demands sub-second, high-throughput identity verification while increasingly stringent privacy regulation prohibits the centralized accumulation of passenger data. Existing deployments copy complete passenger profiles to every checkpoint terminal, multiplying the data breach surface at each journey touchpoint and conflicting with GDPR data minimization requirements. Methods: This paper presents BIPV (Blockchain-based Identity and Privacy Verification), a system that resolves this tension through programmable zero-knowledge proofs. BIPV anchors only cryptographic references on a Hyperledger Fabric consortium blockchain; passengers prove eligibility at checkpoints via Circom-compiled Groth16 zk-SNARKs that confirm policy compliance without disclosing any underlying personal attributes. We detail the Circom circuit design for airport policy predicates (AgeVerifier, NationalityChecker, DocumentValidator), a proof pre-computation and caching strategy that eliminates gate-lane latency, and a Hyperledger Fabric consortium governance model that anchors verification keys without recording passenger movement. Results: Our prototype achieves 0.42 s mean verification latency, 2,380 passengers per checkpoint per hour, and a 94.7% reduction in PII exposure relative to centralized baselines, evaluated across 1,000 simulated verification sessions. Security analysis confirms resistance to credential forgery, replay attacks, and consortium collusion under standard cryptographic assumptions. Conclusions: BIPV satisfies GDPR data minimization requirements, ICAO Annex 17, and IATA One ID guidelines. Beyond aviation, the BIPV model generalizes to any domain requiring high-assurance, high-throughput identity verification under privacy obligations.
The following paper presents research aimed at identifying the most critical risks and their mitigations in Urban Air Mobility (UAM) operations. This topic is one of aviation's most significant challenges in the coming decades. Having many flying vehicles in a single airspace requires an innovative approach, rule redefinition, and traffic management. Some solutions are scalable and can be adapted from general aviation. Therefore, stakeholders must address new risks and implement dedicated methods while maintaining the highest level of operational safety. Simulation research is needed to validate solutions before systems operate in real environments. The response to those challenges is the development of a simulation tool that can serve as a test benchmark. The study is divided into two sections: identifying potential risks associated with the rapidly growing UAV market and its applications in urban environments and developing a simulation tool that addresses various Urban Air Mobility challenges. A set of test cases is presented to demonstrate the tool’s functionality and capabilities for further analysis. The paper reviews the United States and European Union approaches to UAM integration, including NASA, FAA, SESAR, and EASA initiatives, and highlights differences in operational concepts and regulatory frameworks. The research identifies major categories of risks related to UAV operations, including technical failures, environmental hazards, human factors, and cybersecurity threats. Long-term challenges associated with increasing traffic density, autonomous operations, and airspace organization are also discussed. The research evaluates scalable safety solutions derived from commercial aviation and analyzes urban airspace concepts such as layers, zones, sky-lanes, and sky-corridors. The developed simulation environment, implemented for the Warsaw metropolitan area, enables modeling of large-scale UAV and VTOL operations, no-fly zones, vertiport hubs, and traffic distribution. The results demonstrate the importance of dedicated traffic structures, altitude separation, and decentralized traffic management systems in ensuring safe and efficient Urban Air Mobility operations.
Secure cross-domain UAV authentication is challenging because identity verification alone is insufficient to guarantee safe operation. In many UAV applications, it is equally critical to verify that a UAV is currently located within an authorized geographic region. Existing approaches often expose precise GPS coordinates, rely on static identifiers that enable tracking, or fail to guarantee the freshness and authenticity of location evidence. These weaknesses allow replay, location spoofing, and trajectory inference attacks, especially in multi-domain environments. To address these limitations, we propose PrivLocAuth, a zero-knowledge-based cross-domain UAV authentication protocol that enforces geofence restrictions without revealing actual locations. In PrivLocAuth, UAVs encode their current coordinates into fresh Pedersen commitments, which are attested by the home Local Domain Server (LDS) using short-lived Schnorr signatures. Based on these attested commitments, UAVs generate Bulletproof range proofs to demonstrate compliance with cross-domain server-defined geofences. This design ensures that UAVs operate within authorized airspace while preserving strong location privacy. PrivLocAuth further incorporates a lightweight elliptic curve cryptography (ECC) and Schnorr signature-based credential framework that enables unlinkable authentication across-domains, preventing session correlation and identity tracking. Formal security analysis demonstrates resistance to impersonation, replay, geofence-bypass, and linkage attacks. Experimental evaluation shows low computational latency and minimal communication overhead, confirming the protocol’s suitability for resource-constrained UAV platforms operating in dynamic cross-domain environments.
The integration of Uncrewed Aerial Vehicles (UAVs) into low-altitude airspace has led authorities to adopt distributed Uncrewed Traffic Management (UTM) architectures that ensure interoperability and safety. Blockchain has been proposed as an enabler for trustworthy coordination among UTM stakeholders. Yet, its real-time performance under aeronautical constraints remains insufficiently characterized. This paper presentes a quantitative benchmark comparing two regulation compliant distributed architectures: the federated InterUSS platform maintained by the Linux Foundation and a permissioned blockchain based on Hyperledger Fabric. Both systems were evaluated through Operational Intent Reference (OIR) registration work loads generated via Hyperledger Caliper, measuring throughput, latency, and transaction loss under loads up to 50 transactions per second. Results show that InterUSS sustained sub-second latency and stable performance up to 30 TPS. At the same time, Fabric exhibited exponential degradation with median latency exceeding 3 s and tail latencies above 15 s beyond that point. These findings demonstrate that blockchain-based architectures must be redesigned to meet aeronautical timing and scalability requirements, suggesting that hybrid models combining distributed ledgers for auditability with federated frameworks for real-time coordination are more suitable for future UTM deployments.
Digital passports for Unmanned Aerial Vehicles (UAVs) are used to create a unified system for tracking and identifying UAVs, which ensures compliance and security. A digital passport holds details like the owner's information, drone model, and activity history, thereby enabling easy tracking and identification of UAVs. However, the absence of decentralized and secure digital passport management systems for UAVs makes it challenging to ensure tamper-proof records and transparent ownership verification across borders. This paper proposes a proof of concept for a decentralized blockchain and Non-Fungible Tokens (NFTs)-based digital passport to improve the transparency, traceability, trust, and security of UAV preoperational certifications. The proposed solution secures UAVs' preoperational stages such as design, manufacturing, and distribution, integrating best practices from the Federal Aviation Administration (FAA) and the European Union Aviation Safety Agency (EASA) for standardized compliance. The proposed NFT-based digital passport consolidates certification records into a single verifiable document, providing access to compliance history. Four smart contracts are developed to enforce role-based access control, and off-chain decentralized storage using the InterPlanetary File System (IPFS) is employed to manage large UAV records. Evaluation through implementation and testing demonstrates the solution's effectiveness in managing UAV certification workflows and enforcing regulatory compliance. Security analysis shows robustness against unauthorized modifications and common vulnerabilities, while cost analysis assesses deployment viability across multiple blockchain networks. A comparison of the proposed solution with existing UAV compliance and certification frameworks shows that it effectively fills the gap by addressing preoperational certification. The smart contract code is publicly available on GitHub.
The increasing deployment of Unmanned Aerial Vehicles (UAVs) for military, commercial, and logistics applications has raised significant concerns regarding flight path privacy. Conventional UAV communication systems often expose flight path data to third parties, making them vulnerable to tracking, surveillance, and location inference attacks. Existing encryption techniques provide security but fail to ensure complete privacy, as adversaries can still infer movement patterns through metadata analysis. To address these challenges, we propose a zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge)-based privacy preserving flight path authentication and verification framework. Our approach ensures that a UAV can prove its authorisation, validate its flight path with a control centre, and comply with regulatory constraints without revealing any sensitive trajectory information. By leveraging zk-SNARKs, the UAV can generate cryptographic proofs that verify compliance with predefined flight policies while keeping the exact path and location undisclosed. This method mitigates risks associated with real-time tracking, identity exposure, and unauthorised interception, thereby enhancing UAV operational security in adversarial environments. Our proposed solution balances privacy, security, and computational efficiency, making it suitable for resource-constrained UAVs in both civilian and military applications.
Environmental factors and electronic interference often disrupt communication between UAV swarms and ground control centers, requiring UAVs to complete missions autonomously in offline conditions. However, current coordination schemes for UAV swarms heavily depend on ground control, lacking robust mechanisms for offline task allocation and coordination, which compromises efficiency and security in disconnected settings. This limitation is especially critical for complex missions, such as rescue or attack operations, underscoring the need for a solution that ensures both mission continuity and communication security. To address these challenges, this paper proposes an offline task-coordination algorithm based on blockchain smart contracts. This algorithm integrates task allocation, resource scheduling, and coordination strategies directly into smart contracts, allowing UAV swarms to autonomously make decisions and coordinate tasks while offline. Experimental simulations confirm that the proposed algorithm effectively coordinates tasks and maintains communication security in offline states, significantly enhancing the swarm’s autonomous performance in complex, dynamic scenarios.
Non-Fungible Tokens (NFTs) have emerged as a revolutionary method for managing digital assets, providing transparency and secure ownership records on a blockchain. In this paper, we present a theoretical framework for leveraging NFTs to manage UAV (Unmanned Aerial Vehicle) flight data. Our approach focuses on ensuring data integrity, ownership transfer, and secure data sharing among stakeholders. This framework utilizes cryptographic methods, smart contracts, and access control mechanisms to enable a tamper-proof and privacy-preserving management system for UAV flight data.
Sana Hafeez, Runze Cheng, Lina Mohjazi, Yao Sun · 5 authors
Unmanned Aerial Vehicles (UAVs) have significant potential for agile communication and relief coordination in post-disaster scenarios, especially when conventional ground infrastructure is compromised. However, effectively coordinating and securing swarms of heterogeneous UAVs from multiple service providers presents critical challenges related to privacy, scalability, lightweight consensus protocols, and cybersecurity resilience. This study proposes a blockchain-enabled UAV coordination framework that leverages consensus mechanisms, smart contracts, and cryptographic techniques to address these challenges. First, a consortium blockchain architecture is introduced, integrating Zero-Knowledge Proofs (ZKPs) to enable privacy-preserving, multi-agency coordination while ensuring access control and data security. Second, a hybrid Delegated Proof-of-Stake–Practical Byzantine Fault Tolerance (DPoS-PBFT) consensus protocol is developed to optimise security, efficiency, and resilience against node failures in resource-constrained UAV networks. Third, a decentralized flocking algorithm is proposed to enable adaptive and autonomous UAV cluster operations under dynamically changing connectivity conditions, ensuring seamless disaster relief functions. Comprehensive simulations show that the proposed system scales efficiently to 500 UAV nodes while maintaining high throughput and low latency, with only a 50-ms increase in latency from 10 to 500 nodes. The framework demonstrates strong cyber resilience, remaining robust under denial-of-service (DoS), spoofing, and tampering attacks. Furthermore, communication latencies remain under 10 milliseconds, with median values of approximately 2–3 ms, achieved through self-optimizing network intelligence. The results validate the proposed system as a secure, scalable, and high-performance solution for UAV-enabled disaster response, ensuring reliable emergency communication and efficient resource allocation in critical environments.
Abstract One of the main objectives of the European Commission (EC) since the 70s has been to coordinate research policies and to enhance the transnational cooperation in order to reach efficiency in terms of funding and to match with Europe’s economic ambitions. It has been addressed through a centralized approach managed by the EC and a decentralized approach through the cooperation of member states. Regarding the aviation sector, the centralized financing has been successfully implemented under the EU Research & Innovation Framework Programmes, while the decentralized approach has been less successful through the initiative Air Transport Net (AirTN) ERA-NET. The intention of this paper is to analyse the AirTN case study and its methodology to launch transnational calls, the results, and the reasons why it was not completely successful. Following the identification of these main barriers, we provide a list of suggestions that could have been implemented for a more successful outcome.
Seyed Mohammad Hashemi, Ruxandra Mihaela Botez, Georges Ghazi
This paper introduces an innovative consensus algorithm for managing Unmanned Aircraft System Traffic (UTM) through blockchain technology, a highly secure consensus protocol, to allocate airspace. A smart contract was developed on the Ethereum blockchain for allocating airspace. This technique enables the division of the swarm flight zone into smaller sectors to decrease the computational complexity of the algorithm. A decentralized voting system was established within these segmented flight zones, utilizing two primary methodologies: Proof of Work (PoW) and Proof of Stake (PoS). By employing 1000 UAS-S4s across various locations and heading angles, a swarm flight zone was generated. The efficiency of the devised decentralized consensus system was assessed based on error rate and validation time. Despite PoS displaying greater efficiency in cumulative probability for block execution, the comparative analysis indicated PoW outperformed PoS concerning the potential for conflicts among UASs.
Seyed Mohammad Hashemi, Seyed Mohammad Hashemi, Seyed Ali Hashemi, Seyed Ali Hashemi · 6 authors
This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm was designed for Aircraft Trajectory Prediction (ATP) based on an Autoencoder architecture. The Autoencoder was considered in this study due to its excellent fault-tolerant ability when the input data provided by the GPS is deficient. After conflict detection, P2P blockchain was used for securely decentralized decision-making. A meta-controller composed of this Autoencoder, and P2P blockchain performed the ATMC task very well. A comprehensive database of trajectories constructed using our UAS-S4 Ehécatl was used for algorithms validation. The accuracy of the ATP was evaluated for a variety of data failures, and the high-performance index confirmed the excellent efficiency of the autoencoder. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our in-house Smart Contract software developed on the Ethereum platform. The Sharding approach improved the P2P blockchain performance in terms of computational complexity and processing time in real-time operations. Therefore, the probability of conflicts among aircraft in a swarm environment was significantly reduced using our new methodology and algorithm.
Jamal Habibi Markani, Abdessamad Amrhar, Jean-Marc Gagné, René Landry
In the next generation modernization plan, the automatic dependent surveillance-broadcast (ADS-B) system plays a pivotal role. However, the ADS-B’s low level of security and its vulnerabilities have raised valid concerns. The main objectives of this paper are to highlight the limitations of legacy ADS-B systems and to assess the feasibility of using Format-preserving (F), Feistel-based encryption (F), with multiple implementation variances (X) (FFX) algorithms, for enhancing ADS-B’s security. The offered solution is implemented in a standard software-defined radio (SDR) ADS-B to be utilized in real-time applications. Furthermore, a new proposed blockchain scheme is used as a secured database to manage the cipher key. The metric of message entropy is used to assess an algorithm’s ability to confuse and diffuse predictable ADS-B messages; correlation and serial correlation of plain data and cipher data are deployed to evaluate the proposed method’s security level. The authors provide both MATLAB simulations and flight test outcomes to demonstrate the feasibility of this approach. Based on our security analysis, ADS-B information can be kept confidential through our scheme. The performance evaluation results reveal that the proposed scheme is achievable, compatible, and efficient for the avionics industry.
Marina Dehez-Clementi, Nicolas Larrieu, Emmanuel Lochin, Mohamed Ali Kâafar · 5 authors
The Aviation Industry has been booming for several decades and is expected to keep growing in the future. Therefore, Air Traffic Management (ATM) tools are likely to be soon overwhelmed by the demand. The Single European Sky ATM Research Program (SESAR) 2020, controlled by EUROCONTROL, intends to revisit the management of aeronautical information along its full lifecycle and across the whole European ATM system. The efficient sharing of information over large scale Cyber Physical Systems (CPSs) is a non-trivial problem that raises several challenges including data lineage, data consistency, access rights management and privacy-preservation. Part of the SESAR initiative, System-Wide Information Management (SWIM) project defines standards to enhance the security of aeronautical data shared among stakeholders. Most of its propositions include centralized or partially centralized mechanisms in order to enforce data confidentiality, and privacy. In this paper, we intend to discuss how blockchains can improve the sharing of sensitive data over the ATM system. More specifically, we use the example of flight data and describe a high-level Blockchain-based concept that mimics the decentralized nature of existing A TM system to provide a reliable, distributed storage platform for flight information.
We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness of our approach. To the best of our knowledge, this is the first work that considers blockchain-based, distributed RL for ATFM.
In recent years, there is growing interest in the ways the European aviation industry can leverage the multi-source data fusion towards augmented domain intelligence. However, privacy, legal and organisational policies together with technical limitations, hinder data sharing and, thus, its benefits. The current paper presents the ICARUS data policy and assets brokerage framework, which aims to (a) formalise the data attributes and qualities that affect how aviation data assets can be shared and handled subsequently to their acquisition, including licenses, IPR, characterisation of sensitivity and privacy risks, and (b) enable the creation of machine-processable data contracts for the aviation industry. This involves expressing contractual terms pertaining to data trading agreements into a machine-processable language and supporting the diverse interactions among stakeholders in aviation data sharing scenarios through a trusted and robust system based on the Ethereum platform.
We propose to enhance the security and transparency of aircraft maintenance records in the aviation industry through the use of blockchain technology. A physical aircraft maintenance logbook is susceptible to being lost or destroyed. A nonexistent aircraft maintenance logbook hurts the confidence in integrity and reputation of the aircraft. Furthermore, fraud can occur through forgery of FAA personnel signatures and the installation of non-official aircraft parts. The scope of this work is to develop a secure blockchain that can store aircraft service records and information in a digital distributed ledger. By keeping the maintenance logbook on a digital ledger, records can be stored indefinitely in a trusted environment with the integrity of records guaranteed. Additionally, to achieve being a distributed ledger, a consensus algorithm PoET is used to display the global state accurately to all users. The SAMR blockchain uses the Linux Foundations open sourced software “Hyperledger” to facilitate an environment that mimics a real-world implementation. The Python Programming Language was used for SAMR's implementation of the blockchain logic through creation of a permission-based blockchain for holding the maintenance records.
Nicolas Dousse, Grégoire Heitz, Felix Schill, Dario Floreano
Semi- or fully autonomous personal aerial vehicles (PAVs) are currently studied and developed by public and private organizations as a solution for traffic congestion. While optimal collision-free navigation algorithms have been proposed for autonomous robots, trajectories and accelerations for PAVs should also take into account human comfort. In this letter, we propose a reactive decentralized collision avoidance strategy that incorporates passenger physiological comfort based on the optimal reciprocal collision avoidance strategy. We study in simulation the effects of increasing PAV densities on the level of comfort, on the relative flight time and on the number of collisions per flight hour and demonstrate that our strategy reduces collision risk for platforms with limited dynamic range. Finally, we validate our strategy with a swarm of ten quadcopters flying outdoors.