Drone Delivery Research Articles

Optimization and Application Analysis of the UAV "Last Mile" Mobile Terminal Distribution Station in Rural Area under the Background of E-Commerce

With the booming demands of global consumers, the grand total of international e-commercial procurement in 2024 will be expected to exceed 6.3 trillion USD. Under the pressure of huge orders and shipping lag, figuring out a cost-effective last mile delivery achieving real door-to-door service, especially in rural areas, has become one of the focuses in the international e-commercial market. To better solve this problem, this paper starts with the analysis of international e-commercial status and the feasibility of UAV mobile terminal station launch, taking good account of drone delivery route setting, time schedule, and costs spent to optimize the mobile station moving. Based on a series of experiments to iterate the Unmanned Aerial Vehicle (UAV) mobile terminal station delivery mode, it will provide some insights to the market about how the last mile delivery will develop in the next decade. This paper will focus on applying the hybrid ant colony algorithm to improve truck-drone collaborative last mile delivery route settings and figure out the best delivery route combo for cost saving.

Advancements in automated external and wearable cardiac defibrillators.

Survival statistics for out-of-hospital cardiac arrests remain unsatisfactory. Prompt defibrillation of shockable rhythms, such as ventricular fibrillation and pulseless ventricular tachycardia, is crucial for improving survival. Automated external defibrillators (AEDs) and wearable cardiac defibrillators (WCDs) seek to improve the survival rates following out-of-hospital cardiac arrests. We aim to review the indications, utility, advancements, and limitations of AEDs and WCDs, as well as their role in contemporary and future clinical practice. Recent advancements in these technologies, such as smartphone applications and drone delivery of AEDs and less inappropriate shocks and decreased size of WCDs, have increased their ubiquity and efficacy. However, implementation of this technology remains limited due to lack of resources and suboptimal patient adherence. Out of hospital cardiac arrests continue to pose a significant public health challenge. Advancements in AEDs and WCDs aim to facilitate prompt defibrillation of shockable rhythms with the goal of improving survival rates. However, they remain underutilized due to limited resources and suboptimal patient adherence. As these technologies continue to evolve to become smaller, lighter and more affordable, their utilization and accessibility are expected to improve.

Dynamic collaborative truck-drone delivery with en-route synchronization and random requests

Coordinated truck and drone delivery is gaining popularity in logistics as it can greatly reduce operation costs. However, existing studies on related operations management problems typically ignore the following important features: (i) the random appearance of requests, which require operators to dynamically respond to the requests; and (ii) the decisions of optimal launch and retrieval locations for trucks and drones instead of fixed to customer locations, which can significantly impact the overall time costs. To tackle these challenges, this study investigates the dynamic collaborative truck-drone routing problem with randomly arriving requests and synchronization on routes. We model the problem as a Markov Decision Process (MDP) and solve the MDP via a reinforcement learning (RL) approach. The proposed RL approach determines: (i) whether each request should be serviced upon arrival, (ii) which truck or drone should be assigned for the request, and (iii) the optimal en-route take-off and landing positions for paired trucks and drones. We further employ a framework of decentralized learning and centralized dispatching in RL to increase performance. Numerical experiments are conducted to assess the proposed solution approach on instances generated based on both the Solomon dataset and real-world operational data of a logistics operator in Singapore over several benchmark algorithms under various battery endurance levels of drones and distinct transportation scenarios including node-based dynamic collaborative truck-drone routing problem, dynamic non-collaborative truck and drone routing problem, and dynamic vehicle routing problem. The results show that our RL solution outperforms the benchmark algorithm in total profit by an average of 28.03 %, and our en-route takeoff and landing scenario outperforms the benchmark scenarios in total profit by an average of 8.43 % in multi-day instances. Additionally, compared to the traditional node-based landing scenario, employing our en-route takeoff and landing strategy can save 0.9 h/(drone*day) of waiting time on average.

Evaluating Reduced-Order Urban Wind Models for Simulating Flight Dynamics of Advanced Aerial Mobility Aircraft

Advanced Aerial Mobility (AAM) platforms are poised to begin high-density operations in urban areas nationwide. This new category of aviation platforms spans a broad range of sizes, from small package delivery drones to passenger-carrying vehicles. Unlike traditional aircraft, AAM vehicles operate within the urban boundary layer, where large structures, such as buildings, interrupt the flow. This study examines the response of a package delivery drone, a general aviation aircraft, and a passenger-carrying urban air mobility aircraft through an urban wind field generated using Large Eddy Simulations (LES). Since it is burdensome to simulate flight dynamics in real-time using the full-order solution, reduced-order wind models are created. Comparing trajectories for each aircraft platform using full-order or reduced-order solutions reveals little difference; reduced-order wind representations appear sufficient to replicate trajectories as long as the spatiotemporal wind field is represented. However, examining control usage statistics and time histories creates a stark difference between the wind fields, especially for the lower wing-loading package delivery drone where control saturation was encountered. The control saturation occurrences were inconsistent across the full-order and reduced-order winds, advising caution when using reduced-order models for lightly wing-loaded aircraft. The results presented demonstrate the effectiveness of using a simulation environment to evaluate reduced-order models by directly comparing their trajectories and control activity metrics with the full-order model. This evaluation provides designers valuable insights for making informed decisions for disturbance rejection systems. Additionally, the results indicate that using Reynolds-averaged Navier–Stokes (RANS) solutions to represent urban wind fields is inappropriate. It was observed that the mean wind field trajectories fall outside the 95% confidence intervals, a finding consistent with the authors’ previous research.

Urban Air Logistics with Unmanned Aerial Vehicles (UAVs): Double-Chromosome Genetic Task Scheduling with Safe Route Planning

In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy.

Consumer support for restrictive policies on unhealthy food and beverage delivery via drones.

Drone delivery services are set to increase unhealthy food and alcohol accessibility. The aim of this study was to evaluate public receptiveness to various options for regulating drone food and beverage deliveries and to identify sociodemographic differences in receptiveness. In total, 1079 adults were surveyed to assess total support and differences in support between population subgroups (e.g.age, sex, location, existing habits) for nine potential drone policies covering curfews, quotas, and product bans. Support was measured on five-point agreement scales, with mean individual policy support (M) and grand mean support calculated for all assessed policies (grand M). There was moderate support for all assessed policies (grand M=3.5), ranging from M=3.2 (drone delivery quotas for shopping centres and dwellings) to M=3.7 (night curfews, airspace quotas). Factors associated with policy support were older age, metropolitan residenceand using grocery delivery services. Public support exists for policies designed to restrict drone food and beverage deliveries. Drone food and beverage delivery policies will likely be supported by the public and could assist in controlling the accessibility of such products for the benefit of population dietary health.

Optimizing Drone Delivery Paths from Shared Bases: A Location-Routing Problem with Realistic Energy Constraints

Recently, Amazon patented fulfillment centers for drones on a large scale in densely populated areas. A network of such shared centers can be used for landing and launching drones as an alternative to the traditional private bases in near future. This paper studies how a user of such a shared delivery infrastructure can optimally determine the required bases among the existing bases to perform her delivery tasks. To this end, a location-routing problem is studied where the problem determines the drone launching centers and their routes to deliver parcels. A realistic energy function is used that incorporates the effect of load weight for calculating energy consumption in all flight phases including take-off, level flight, hovering, and landing. Although the energy consumption is nonlinear, the problem is formulated as a mixed-integer linear programming model and strengthened by valid inequalities and a pre-processing algorithm that enables us to solve instances with up to 100 customers using off-the-shelf optimization solvers. Moreover, a heuristic method is presented to solve instances with up to 200 customers. Results indicate the importance of incorporating the load-dependent energy formula in contrast to using fixed flight duration constraints for drones. The value of allowing multiple visits on each trip versus only simple back-and-forth trips is also assessed. The advantage of using an integrated location-routing approach is shown over the sequential approach in which the locations of bases are decided first and the routes are constructed next. Moreover, to manage future demand uncertainty, the model is extended for the case that a number of demand scenarios are given.

Drones reduce the time to defibrillation in a highly visited non-urban area: A randomized simulation-based trial

IntroductionOut-of-hospital cardiac arrest (OHCA) has a high global incidence and mortality rate, with early defibrillation significantly improving survival. Our aim was to assess the feasibility of autonomous drone delivery of automated external defibrillators (AED) in a non-urban area with physical barriers and compare the time to defibrillate (TTD) with bystander retrieval from a public access defibrillator (PAD) point and helicopter emergency medical services (HEMS) physician performed defibrillation. MethodsThis randomized simulation-based trial with a cross-over design included bystanders performing AED retrievals either delivered by automated drone flight or on foot from a PAD point, and simulated HEMS interventions. The primary outcome was the time to defibrillation, with secondary outcomes comparing workload, perceived physical effort, and ease of use. ResultsThirty-six simulations were performed. Drone-delivered AED intervention had a significantly shorter TTD [2.2 (95 % CI 2.0–2.3) min] compared to PAD retrieval [12.4 (95 % CI 10.4–14.4) min] and HEMS [18.2 (95 % CI 17.1–19.2) min].The self-reported physical effort on a visual analogue scale for drone-delivered AED was significantly lower versus PAD [2.5 (1 – 22) mm vs. 81 (65–99) mm, p = 0.02]. The overall mean workload measured by NASA-TLX was also significantly lower for drone delivery compared to PAD [4.3 (1.2–11.7) vs. 11.9 (5.5–14.5), p = 0.018]. ConclusionThe use of drones for automated AED delivery in a non-urban area with physical barriers is feasible and leads to a shorter time to defibrillation. Drone-delivered AEDs also involve a lower workload and perceived physical effort than AED retrieval on foot.

Drone delivery problem with multi-flight level: Machine learning based solution approach

This study provides a new perspective on the drone delivery problems (DDP) by conceptualizing the vertical space in multiple flight levels. The main advantage of drone delivery is efficiency in utilizing free three-dimension aerial space, enabling numerous travels at multiple flight levels. However, the operational efficiency tradeoff exists according to the flight level, particularly in metropolitan cities with countless skyscrapers. Operation on the upper level requires less detour on horizontal movement, but it needs more time on the vertical movement of drones to reach the upper level. This study introduces a novel DDP by dividing the vertical airspace into multiple flight levels, thereby providing an opportunity to increase overall delivery efficiency based on realistic constraints faced by cities. We formulate this problem into a mathematical model and suggest a new supervised machine learning approach called SPML (Sequential Prediction Machine Learning). The SPML has three phases. In the first phase, customers are sequenced by priority. The second phase uses a supervised machine learning model trained by the data collected from solving the mixed-integer linear programming (MILP) model to assign customers to the depot. The third phase is distributing jobs to drones by using dynamic programming.

An improve crested porcupine algorithm for UAV delivery path planning in challenging environments

With the rapid advancement of drone technology and the growing applications in the field of drone engineering, the demand for precise and efficient path planning in complex and dynamic environments has become increasingly important. Traditional algorithms struggle with complex terrain, obstacles, and weather changes, often falling into local optima. This study introduces an Improved Crown Porcupine Optimizer (ICPO) for drone path planning, which enables drones to better avoid obstacles, optimize flight paths, and reduce energy consumption. Inspired by porcupines' defense mechanisms, a visuo-auditory synergy perspective is adopted, improving early convergence by balancing visual and auditory defenses. The study also employs a good point set population initialization strategy to enhance diversity and eliminates the traditional population reduction mechanism. To avoid local optima in later stages, a novel periodic retreat strategy inspired by porcupines' precise defenses is introduced for better position updates. Analysis on the IEEE CEC2022 test set shows that ICPO almost reaches the optimal value, demonstrating robustness and stability. In complex mountainous terrain, ICPO achieved optimal values of 778.1775 and 954.0118; in urban terrain, 366.2789 and 910.1682 and ranked first among the compared algorithms, proving its effectiveness and reliability in drone delivery path planning. Looking ahead, the ICPO will provide greater efficiency and safety for drone path planning in navigating complex environments.

Effective influence of Community Pharmacists in Public Health Using Blockchain Technology to Secure Patient Data and Enhance Healthcare Delivery

The advent of healthcare delivery drones, a novel unmanned aerial vehicle (UAV), has opened the door to a more comprehensive approach to developing smart health services. Internet of Things (IoT)-based healthcare delivery via UAVs has not been fully realized because of a lack of augmentation-aware concepts. Despite community pharmacists' strong clinical expertise and ease of patient access, they cannot provide comprehensive clinical services due to a lack of access to patient data. Blockchain could be a solution if it's concerned about the reliability and security of drone deliveries over untrusted open channels. Hence, this study proposes the Internet of Things and Blockchain Technology-assisted Drone Healthcare Delivery System (IoT-BT-DHDS) for enhancing access to healthcare information in the community pharmacy environment. Ensuring the security of drone operations is crucial to protect users from any breaches that might result in physical and financial loss. This research suggests an oT-BT-DHDS that authenticates and registers the involved entities, such as objects (healthcare supplies), warehouses (healthcare centres), and drones, to address these security concerns and make the delivery progression visible. This research achieves its goals by analyzing several parameters that impact the time and number of transactions required for authentication on the Ethereum platform, which is used to build smart contracts and blockchain. The suggested strategy is shown to be effective by results acquired from a simulated environment compared to alternative models based on an access control ratio of 98.9%, data management ratio of 97.4% and resource utilization ratio of 96.4% to other existing models.

415.3: Revolutionizing organ transport: The advent of drone delivery systems for organs.

415.3: Revolutionizing organ transport: The advent of drone delivery systems for organs.

텍스트마이닝을 활용한 도심 드론 물류 서비스 수용성에 대한 연구: 유튜브 댓글을 활용하여

In modern society, logistics systems need new innovations due to urban traffic congestion and environmental issues. Drones, or unmanned aerial vehicles, are gaining attention for improving urban logistics efficiency. However, drone delivery faces issues such as costs, regulations, and noise. This study analyzes consumer perceptions of drone delivery services using text mining technology. A total of 16,695 comments from 587 YouTube videos containing the keyword ‘drone delivery’ were analyzed through frequency analysis, topic modeling, and sentiment analysis. The results provided insights into consumer opinions, attitudes, concerns, and preferences regarding drone logistics. Major issues identified include potential accidents, noise, and high costs. Proposed solutions include enhancing safety, developing noise reduction technologies, reducing costs, and achieving social consensus. This study offers insights for improving drone delivery services and developing strategies that reflect consumer demands for future implementations.

Consumer Study - Consumers Perspective Towards Drone Food Delivery Service

Drone food delivery has a large possibility of being welcomed by businesses as it provides benefits that traditional delivery methods dont have. Such as reduced reliance on manpower and sunk cost, and improved customer satisfaction. Our study aims to measure the adoption and attitudes of consumers toward drone food delivery technology in order to help us assess the feasibility and effectiveness of the service. The study found that privacy has a huge negative impact on consumer attitudes towards drone delivery. However, the Green image had a significant positive effect. People in rural areas pay more attention to the performance of drones than those in cites. At the same time, the research result can be used to analyse the future of the drone food delivery industry and commercial plans prepared for companies.

Collaborative truck–drone–motorcycle delivery of emergency supplies for mountain wildfire suppression

This paper studies the transportation of emergency supplies for mountain wildfire suppression by trucks, drones, and motorcycles. The primary objective is to minimize the time of emergency supplies delivery. By examining the structure of mountainous road networks, the involvement of motorcycles and unmanned aerial vehicles (drones) in collaborative delivery operations, the risk associated with fire spread, and the optimal location for supplies distribution, this problem has been formulated as a collaborative truck–drone–motorcycle location-routing robust optimization model. The aim of introducing a robust optimization model is to ensure the feasibility of the routes under uncertain demand conditions. A box uncertainty set is employed to describe the uncertainty of emergent demands. An improved adaptive large neighborhood search (IALNS) algorithm is developed to solve this problem. A delete operator, a collaborative truck–drone delivery strategy, and a location strategy are introduced to handle various constraints involved in the problem, search the optimal location of the supplies distribution point, and compute vehicle routings. Our method has been compared with several existing algorithms, such as genetic algorithm, simulated annealing algorithm, tabu search algorithm, and whale optimization algorithm. Their average gap with IALNS are respectively 50.64%, 21.20%, 32.59%, and 19.13%. The experimental results demonstrate the efficacy and efficiency of the proposed model and algorithm.

Drone delivery and consumer behavior in last mile logistics: an empirical study in South Korea

Despite the great potential that drone technology can bring to last-mile logistics, consumers’ intent to use it appears to be lower than expected. Therefore, this study investigated the factors influencing consumers’ intentions to use drone delivery. A theoretical model based on value–belief–norm theory was developed and tested with structural equation modeling based on data collected from 458 Korean consumers. The results suggest that the environmental protection, economic, and altruistic values of drone delivery positively impact consumers’ perceptions of responsibility. In addition, the ascription of responsibility positively influences consumers’ personal norms. Finally, personal norms are found to increase consumers’ intentions to use drone delivery, and the impact is positively moderated by moral obligation. This study employs the extended value–belief–norm (VBN) framework and makes a valuable contribution to the existing body of literature. Furthermore, this study employs the VBN model to ascertain the ecological advantages associated with environmentally sustainable unmanned aerial vehicles (UAVs). Additionally, it provides valuable practical implications for professionals and policymakers in the field.

Game of (delivery) drones: A serious game exploring transport futures involving logistics drones with stakeholders

BackgroundThe use of uncrewed aerial vehicles (UAVs, also known as drones) in logistics is evolving. However, there have been few opportunities for people to explore and understand the implications. Existing studies focus on acceptance, yet it is unclear what people are being asked to accept. Using a board game approach, this study has sought to develop ways to involve non-experts in a more informed debate about logistics drones in their local area. MethodA qualitative approach was adopted by developing a location-based board game to help a general audience explore the use of delivery drones and capture their views. Participants explore operational parameters, including ground risk (the probability of a drone hitting a person and injuring them if it fails in flight) and energy use while playing the game, and are prompted to respond to questions embedded within the game. Three game sessions with a total of 15 participants were completed. ResultsParticipants were able to explore and test complex scenarios involving different drone routings and levels of ground risk and energy use whilst building shared knowledge and evoking social learning during gameplay. Participants exchanged views in a relaxed environment and began to explore the implications of the possible future use of delivery drones. Questions embedded within the game allowed people to share their concerns about health and safety, regulation, and where they thought drones should fly. Participants were aware of their knowledge limitations, and it was evident that several misconceptions about delivery drones are emerging. ConclusionThe board game has proved useful in involving people and capturing an in-depth understanding of their views. It is an engaging approach to involve stakeholders in the planning process, creating an artefact that can be adapted to other locations and used by other researchers and practitioners.

Personalization and Customer Experience in E-Commerce

Innovation is crucial for maintaining a competitive edge and delivering exceptional shopping experiences in the ever-evolving realm of e-commerce. Businesses continuously seek creative approaches to enhance the e-commerce landscape, incorporating technologies like immersive experiences, seamless payment solutions, Artificial Intelligence (AI), and personalized services. This study investigates the emerging technologies and innovations within e- commerce, particularly focusing on the application and impact of AI. Utilizing conceptual research methodology and secondary data from various scholarly articles, journals, and online sources, the study concludes that recent innovations such as AI-driven personalization, Augmented Reality (AR) and Virtual Reality (VR), chatbots, blockchain for transaction security and supply chain management, social commerce, drone delivery, and e-wallet solutions have significantly advanced the e- commerce sector. Noteworthy AI applications include platforms like Google AI Cloud Platform, IBM Watson, BigML, and Infosys Nia, which are also leveraged for product pricing and distribution strategies. Additionally, the adoption of diverse payment methods such as e- wallets and UPI contributes to enhancing user satisfaction in e-commerce transactions.

Ranking the benefits of drone-based last-mile delivery due to adoption of its enablers

PurposeStrategic integration of enablers and the realization of drone delivery benefits emerge as essential strategies for business organizations to enhance operational efficiency and stay competitive in last-mile logistics. This paper aims to explore the benefits of drone-based last-mile delivery in the Indian logistic sector by providing a framework for ranking drone delivery benefits (DDBs) due to the adoption of its enablers.Design/methodology/approachThis study proposes a novel hybrid framework applied in the Indian logistic sector by integrating a sentence boundary extraction algorithm for extracting benefits from literature, a spherical fuzzy analytical hierarchy process (SF-AHP) for evaluating primary enablers, unsupervised fuzzy C-means clustering (FCM) for clustering benefits and a spherical combined compromised solution (SF-CoCoSo) for ranking benefits with respect to primary enablers.FindingsThe results reveal that technological and infrastructure enablers (TIE), government and legislation enablers (GLE) and operational and service quality enablers (OSE) are the most significant enablers for drone implementation in logistics. Top-ranked benefits increase the efficiency of last-mile delivery (DDB10), foster supply chain management and logistic sustainability (DDB16) and increase delivery access to rural area and vulnerable people (DDB17).Practical implicationsThis research assists scholars, entrepreneurs and policymakers in the sustainable deployment of drone delivery in the logistics sector. This study facilitates the use of drones in delivery services and provides a foundation for all stakeholders in logistics.Originality/valueThe assessments involve considering judgment from a highly knowledgeable and experienced group in India, characterized by a large volume of inputs and a high level of expertise.

Combinations of First Responder and Drone Delivery to Achieve 5-Minute AED Deployment in OHCA

BackgroundDefibrillation in the critical first minutes of out-of-hospital cardiac arrest (OHCA) can significantly improve survival. However, timely access to automated external defibrillators (AEDs) remains a barrier. ObjectivesThe authors estimated the impact of a statewide program for drone-delivered AEDs in North Carolina integrated into emergency medical service and first responder (FR) response for OHCA. MethodsUsing Cardiac Arrest Registry to Enhance Survival registry data, we included 28,292 OHCA patients ≥18 years of age between 1 January 2013 and 31 December 2019 in 48 North Carolina counties. We estimated the improvement in response times (time from 9-1-1 call to AED arrival) achieved by 2 sequential interventions: 1) AEDs for all FRs; and 2) optimized placement of drones to maximize 5-minute AED arrival within each county. Interventions were evaluated with logistic regression models to estimate changes in initial shockable rhythm and survival. ResultsHistorical county-level median response times were 8.0 minutes (IQR: 7.0-9.0 minutes) with 16.5% of OHCAs having AED arrival times of <5 minutes (IQR: 11.2%-24.3%). Providing all FRs with AEDs improved median response to 7.0 minutes (IQR: 6.2-7.8 minutes) and increased OHCAs with <5-minute AED arrival to 22.3% (IQR: 16.4%-30.9%). Further incorporating optimized drone networks (326 drones across all 48 counties) improved median response to 4.8 minutes (IQR: 4.3-5.2 minutes) and OHCAs with <5-minute AED arrival to 56.3% (IQR: 46.9%-64.2%). Survival rates were estimated to increase by 34% for witnessed OHCAs with estimated drone arrival <5 minutes and ahead of FR and emergency medical service. ConclusionsDeployment of AEDs by FRs and optimized drone delivery can improve AED arrival times which may lead to improved clinical outcomes. Implementation studies are needed.