Search Results (21)

The conventional supply chain management has undergone major advancements following IoT-enabled revolution. The IoT-enabled drones in particular have ignited much recent attention for package delivery in logistics. The service delivery paradigm in logistics has seen a surge in drone-assisted package deliveries and tracking. There have been a lot of recent research proposals on various aspects of last-mile delivery systems for drones in particular. Although drones have largely changed the logistics landscape, there are many concerns regarding security and privacy posed to drones due to their open and vulnerable nature. The security and privacy of involved stakeholders needs to be preserved across the whole chain of Supply Chain Management (SCM) till delivery. Many earlier studies addressed this concern, however with efficiency limitations. We propose a Physical Uncloneable Function (PUF)-based secure authentication protocol (PSL-IoD) using symmetric key operations for reliable last-mile drone delivery in SCM. PSL-IoD ensures mutual authenticity, forward secrecy, and privacy for the stakeholders. Moreover, it is protected from machine learning attacks and drone-related physical capture threats due to embedded PUF installations along with secure design of the protocol. The PSL-IoD is formally analyzed through rigorous security assessments based on the Real-or-Random (RoR) model. The PSL-IoD supports 26.71% of enhanced security traits compared to other comparative studies. The performance evaluation metrics exhibit convincing findings in terms of efficient computation and communication along with enhanced security features, making it viable for practical implementations. Full article

In recent years, interest in drone-based logistics has grown due to the increasing demand for efficient and sustainable package transportation, driven by the expansion of e-commerce and rising environmental awareness. In this study, we focus on flight scheduling for the efficient transportation of packages between logistics bases, rather than on last-mile delivery. In scenarios where the number of packages handled at each base varies, efficient transportation can be achieved by having drones visit high-demand bases more frequently. To this end, we consider a system with two types of drones: local drones that visit all bases, and express drones that visit only selected high-demand bases. We formulate this problem as a mixed integer linear programming (MILP) model that minimizes the total transportation time. This model simultaneously determines which bases should be visited frequently and computes flight schedules that enable efficient package delivery. Unlike existing transportation models that assume fixed linear routes, our model allows for flexible routing, including direct flights and loop-based paths between bases. To ensure scalability, we also propose an approximation method that significantly reduces the computational cost. As the number of logistics bases increases, the exact solution of the MILP becomes intractable. Therefore, we pre-select candidate high-demand bases based on package volume and spatial layout, thereby reducing the number of decision variables. This makes it possible to compute high-quality solutions even in large-scale environments. Through numerical experiments, we show the effectiveness of our proposed methods for the transportation of packages between logistics bases. Full article

Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global Path Planning (GPP) problem, which uses probabilistic information and the camera’s field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms’ performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions; further, in particular, the GPP problem has not been addressed previously. Full article

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. Full article

To gain a sustained competitive advantage, organizations such as UPS, Fedex, Amazon, etc., began to seek for industry 5.0 innovative autonomous delivery options for the last mile. Autonomous unmanned aerial vehicles are a promising alternative for the logistics industry. The fact that drones are propelled by green renewable energy source fits the companies’ need to become sustainable, replacing their fuel truck fleets, especially for traveling to remote rural locations to deliver small packages, but a major obstacle is the necessity for charging stations which is well documented in the literature. Therefore, the current research embarks on devising a novel yet practical piece of technology adopting the simplicity approach of direct flights to destinations. The analysis showcases the application for a network of warehouses and hospitals in Israel while controlling costs. Given the products in the case study are medical, direct flight has the potential to save lives when every moment counts. Hydrogen cell technology allows long-range flying without refueling, and it is both vibration-free which is essential for sensitive medical equipment and environmentally friendly in terms of air pollution and silence in urban areas. Importantly, hydrogen cells are lighter, with higher energy density than batteries, which makes them ideal for drone usage to reduce weight, maintain a longer life, and enable faster charging, all of which minimize downtime. Also, hydrogen sourcing is low-cost and unlimited compared to lithium-ion material which needs to be mined. The case study investigates an Israeli entrepreneurial company, Gadfin, which builds a vertical takeoff-and-landing-type of drone with folded wings that enable higher speed for the delivery of refrigerated medical cargo, blood, organs for transplant, and more to hospitals in partnership with the Israeli medical logistic conglomerate, SAREL. An analysis of shipping optimization (concerning the number and type of drone) is conducted using a mixed-integer linear programming technique based on various types of constraints such as traveling distance, parcel weight, the amount of flight controllers and daily number of flights allowed in order to not overcrowd the airspace. Importantly, the discussion assesses the ecosystem’s variety of risks and commensurate safety mechanisms for advancing a newly shaped landscape of drones in an Israeli tight airspace to establish a network of national routes for drone traffic. The conclusion of this research cautions limitations to overcome as the utilization of drones expand and offers future research avenues. Full article

This study addresses the Electric Vehicle Routing Problem with Drones (EVRPD) by implementing and comparing two variants of the Greedy Randomized Adaptive Search Procedure (GRASP). The primary objective of the EVRPD is to optimize the routing of a combined fleet of ground and aerial vehicles, with the aim of improving delivery efficiency and minimizing energy consumption, which is directly influenced by the weight of the packages. The study assumes a standardized packing system consisting of three weight classes, where deliveries are exclusively performed by drones, while ground vehicles function as mobile depots. The two employed GRASP variants vary in their methods of generating the Restricted Candidate List (RCL), with one utilizing a cardinality-based RCL and the other adopting a value-based RCL. To evaluate their performance, benchmark instances from the existing EVRPD literature are utilized, extensive computational experiments are conducted, and the obtained computational results are compared and discussed. The findings of the research highlight the considerable impact of RCL generation strategies on solution quality. Lastly, the study reports four new best-known values. Full article

As a new delivery mode, the collaborative delivery of packages using trucks and drones has been proven to reduce delivery costs and delivery time. To cope with the huge cost challenges brought by strict time constraints and ever-changing customer orders in the actual delivery process, we established a two-stage optimization model based on different demand response strategies with the goal of minimizing delivery costs. To solve this problem, we designed a simulated annealing chimp optimization algorithm with a sine–cosine operator. The performance of this algorithm is improved by designing a variable-dimensional matrix encode to generate an initial solution, incorporating a sine–cosine operator and a simulated annealing mechanism to avoid falling into a local optimum. Numerical experiments verify the effectiveness of the proposed algorithm and strategy. Finally, we analyze the impact of dynamic degree on delivery cost. The proposed model and algorithm extend the theory of the vehicle routing problem with drones and also provide a feasible solution for route planning, taking into account dynamic demands and time windows. Full article

New innovative green concepts in electrified vertical take-off and landing vehicles are currently emerging as a revolution in urban mobility going into the third dimension (vertically). The high population density of cities makes the market share highly attractive while posing an extraordinary challenge in terms of community acceptance due to the increasing and possibly noisier commuter traffic. In addition to passenger transport, package deliveries to customers by drones may enter the market. The new challenges associated with this increasing transportation need in urban, rural, and populated areas pose challenges for established companies and startups to deliver low-noise emission products. The article’s objective is to revisit the benefits and drawbacks of an affordable acoustic measurement campaign focused on early prototyping. In the very early phase of product development, available resources are often considerably limited. With this in mind, this article discusses the sound power results using the enveloping surface method in a typically available low-reflection room with a reflecting floor according to DIN EN ISO 3744:2011-02. The method is applied to a subsonic electric ducted fan (EDF) unit of a 1:2 scaled electrified vertical take-off and landing vehicle. The results show that considerable information at low costs can be gained for the early prototyping stage, despite this easy-to-use, easy-to-realize, and non-fine-tuned measurement setup. Furthermore, the limitations and improvements to a possible experimental setup are presented to discuss a potentially more ideal measurement environment. Measurements at discrete operating points and transient measurements across the total operating range were conducted to provide complete information on the EDF’s acoustic behavior. The rotor-self noise and the rotor–stator interaction were identified as primary tonal sound sources, along with the highest broadband noise sources located on the rotor. Based on engineering experience, a first acoustic improvement treatment was also quantified with a sound power level reduction of 4 dB(A). In conclusion, the presented method is a beneficial first measurement campaign to quantify the acoustic properties of an electric ducted fan unit under minimal resources in a reasonable time of several weeks when starting from scratch. Full article

The use of drones for package delivery, commonly known as drone delivery or unmanned aerial vehicle (UAV) delivery, has gained significant attention from academia and industries. Compared to traditional delivery methods, it provides greater flexibility, improved accessibility, increased speed and efficiency, enhanced safety, and even some environmental benefits. With the increasing interest in this technology, it is crucial for researchers and practitioners to understand the current state of the art in drone delivery. This paper aims to review the current literature on drone delivery and identify research trends, challenges, and future research directions. Specifically, the relevant literature is identified and selected using a systematic literature review approach. We then categorize the literature according to the characteristics and objectives of the problems and thoroughly analyze them based on mathematical formulations and solution techniques. We summarize key challenges and limitations associated with drone delivery from technological, safety, societal, and environmental aspects. Finally, potential research directions are identified. Full article

Recently, owing to the high mobility and low cost of drones, drone-based delivery systems have shown considerable potential for ensuring flexible and reliable parcel delivery. Several crucial design issues must be considered to design such systems, including route planning, payload weight consideration, distance measurement, and customer location. In this paper, we present a survey of emerging drone routing algorithms for drone-based delivery systems, emphasizing three major drone routing aspects: trajectory planning, charging, and security. We focus on practical design considerations to ensure efficient, flexible, and reliable parcel delivery. We first discuss the potential issues arising when designing such systems. Next, we present a novel taxonomy based on the above-mentioned three aspects. We extensively review each algorithm for drone routing in terms of key features and operational characteristics. Furthermore, we compare the algorithms in terms of their main idea, advantages, limitations, and performance aspects. Finally, we present open research challenges to motivate further research in this field. In particular, we focus on the major aspects that researchers and engineers need to consider in order to design effective and reliable drone routing algorithms for drone-based delivery systems. Full article

With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the laws and privacy regarding the use of drones in the urban planning context play an essential role in future smart cities. This study provides a comprehensive survey of the regulation of drone application and drone management systems, including a comparison of existing rules, management methods, and guidelines in drone operation to guarantee the safety and security of people, property, and environment. Evaluating existing regulations and laws practiced worldwide will assist in designing drone management and regulation. In Vietnam, the current rules can manage and regulate general guidelines of drone operations based on prohibited, restricted, and controlled areas within the urban context. The legislation, however, is unclear as to how it regulates smaller civilian unmanned aircraft used in the country. In the legal aspect, the potential consequences consist of the inefficiency of compensation responsibility, the violation of drone regulations, and information insecurity. Full article

Investigations about the feasibility of delivery systems with unmanned aerial vehicles (UAVs) or drones have been recently expanded, owing to the exponential demand for goods to be delivered in the recent years, which has been further increased by the COVID-19 pandemic. UAV delivery can provide new contactless delivery strategies, in addition to applications for medical items, such as blood, medicines, or vaccines. The safe delivery of goods is paramount for such applications, which is facilitated if the payload is embedded in the main drone body. In this paper, we investigate payload solutions for medium and small package delivery (up to 5 kg) with a medium-sized UAV (maximum takeoff of less than 25 kg), focusing on (i) embedded solutions (packaging hosted in the drone fuselage), (ii) compatibility with transportation of medical items, and (iii) user-oriented design (usability and safety). We evaluate the design process for possible payload solutions, from an analysis of the package design (material selection, shape definition, and product industrialization) to package integration with the drone fuselage (possible solutions and comparison of quick-release systems). We present a prototype for an industrialized package, a right prism with an octagonal section made of high-performance double-wall cardboard, and introduce a set of concepts for a quick-release system, which are compared with a set of six functional parameters (mass, realization, accessibility, locking, protection, and resistance). Further analyses are already ongoing, with the aim of integrating monitoring and control capabilities into the package design to assess the condition of the delivered goods during transportation. Full article

In this work, we focus on a drone-fleet-enabled package delivery scenario, in which multiple drones fly from a start point and cooperatively deliver packages to the ground users in the presence of a number of no-fly zones (NFZs). We first mathematically model the package delivery scenario in a rigorous manner. Then, a package value maximization problem is established to optimize the flight trajectory and package delivery under the constraints of drone load and collision as well as NFZs. The formulated problem is a highly challenging mixed-integer non-convex problem. To facilitate solving it, we transform the formulated problem into an equivalent problem with special structure by using some appropriate transformations, based on which a low-complexity algorithm with favorable performance is developed using the penalty convex–concave procedure method. Finally, numerical results demonstrate the superiority of the proposed solution. Full article

Package delivery via autonomous drones is often presumed to hold commercial and societal value when applied to urban environments. However, to realise the benefits, the challenge of safely managing high traffic densities of drones in heavily constrained urban spaces needs to be addressed. This paper applies the principles of traffic segmentation and alignment to a constrained airspace in efforts to mitigate the probability of conflict. The study proposes an en-route airspace concept in which drone flights are directly guided along a one-way street network. This one-way airspace concept uses heading-altitude rules to vertically segment cruising traffic as well as transitioning flights with respect to their travel direction. However, transition flights trigger a substantial number of merging conflicts, thus negating a large part of the benefits gained from airspace structuring. In this paper, we aim to reduce the occurrence of merging conflicts and intrusions by using a delay-based and speed-based merge-assist strategy, both well-established methods from road traffic research. We apply these merge assistance strategies to the one-way airspace design and perform simulations for three traffic densities for the experiment area of Manhattan, New York. The results indicate, at most, a 9–16% decrease in total number of intrusions with the use of merge assistance. By investigating mesoscopic features of the urban street network, the data suggest that the relatively low efficacy of the merge strategies is mainly caused by insufficient space for safe manoeuvrability and the inability for the strategies to fully respond and thus resolve conflicts on short-distance streets. Full article

Delivery drones have been attracting attention as one of the promising technologies to deliver packages. Several research studies on routing problems specifically for drone delivery scenarios have extended Vehicle Routing Problems (VRPs). Most existing VRPs are based on Traveling Salesman Problems (TSPs) for minimizing the overall distance. On the other hand, VRPs for drone delivery have been aware of energy consumption due to the consideration of battery capacity. Despite hovering motions with loading packages accounting for a large portion of energy consumption since delivery drones need to hover with several packages, little research has been conducted on drone routing problems that aim at the minimization of overall flight times. In addition, flight time is strongly influenced by windy conditions such as headwinds and tailwinds. In this paper, we propose a VRP for drone delivery in which flight time is dependent on the weight of packages in a windy environment, called Flight Speed-aware Vehicle Routing Problem with Load and Wind (FSVRPLW). In this paper, flight speed changes depending on the load and wind. Specifically, a heavier package slows down flight speeds and a lighter package speeds up flight speeds. In addition, a headwind slows down flight speeds and a tailwind speed up flight speeds. We mathematically derived the problem and developed a dynamic programming algorithm to solve the problem. In the experiments, we investigate how much impact both the weight of packages and the wind have on the flight time. The experimental results indicate that taking loads and wind into account is very effective in reducing flight times. Moreover, the results of comparing the effects of load and wind indicate that flight time largely depends on the weight of packages. Full article