Unmanned aerial vehicle and autonomous delivery robot station for last-mile delivery services

Innovation in logistics industry is now implicit, and the use of unmanned aerial vehicles is at the center of logistics innovation. To effectively utilize unmanned aerial vehicles for last-mile delivery, collaborative delivery using ground vehicles and unmanned aerial vehicles has recently been investigated. However, previous studies neglected proper management of batteries, assuming their constant replacement whenever unmanned aerial vehicles land, and hence numerous batteries are required for unmanned aerial vehicles. Given this research gap, we present a new routing model for collaborative delivery wherein an unmanned aerial vehicle uses a ground vehicle as a power source. A mathematical formulation is presented, and noticing the complexity, a heuristic algorithm is developed. We present a case study to verify the operational efficiency of the model. We test the performance of the heuristic and draw insights based on geographical locations of customers via computational experiments.

In the logistics industry, unmanned aerial vehicles (UAVs) are mostly used for last-mile delivery, in combination with other types of vehicles. There is currently no operator in Taiwan that is completely reliant upon the usage of UAVs for cargo delivery services. Therefore, this study proposes a routing and scheduling model for UAVs by utilizing the network flow technique and mathematical programming methods. All advance requests must be satisfied, and the related operating constraints ensured in the model. The model aims to minimize the total operating cost. To effectively solve large problems that may occur in practice, this study develops a relax-and-fix heuristic. Numerical tests are conducted to preliminarily examine whether the model, coupled with the heuristic algorithm, could be applied in practice. The test results indicate that the proposed model and solution algorithm are effective and thus could be useful for UAV operators to perform delivery routing and scheduling.

Industrial robots originated in mid-Twentieth Century factories where they increased the efficiency of manufacturing. Their implementation was an extension of earlier industrial automation such as the introduction of Henry Ford’s mechanized assembly line in 1913. In Ford’s assembly line, a rope-and-pully system advanced each vehicle from one worker to the next allowing each worker to remain stationary. Half a century later, in 1961, the first robotic arm, created by Unimate, was introduced to auto manufacturing, which further increased efficiency. More recently, following advancements in artificial intelligence and sensor technology, industrial robots have acquired greater autonomy and transformed the logistics and delivery industries. Like Ford’s assembly line, and Unimate’s robotic arm, Amazon’s fulfillment center robots, originally designed by Kiva Robotics, reduced the daily steps workers must take. Instead of walking through aisles to stock warehouse shelves or retrieve products for distribution, workers remain stationary, and the robots bring the products to them. Today, with even greater autonomy than their predecessors, robots are migrating out of factories, warehouses, and fulfillment centers and into neighborhood streets, sidewalks, and skies. The technological advancements that allowed robots to automate private industrial spaces, such as machine learning and sophisticated sensors, now enable autonomous delivery robots (ADVs) to travel independently in the outside world and deliver packages, meals, groceries, and other retail purchases to people’s homes. This article focuses on the evolution of ADVs used for “last-mile delivery,” the final step of the delivery process that ends at the customer’s door. It breaks ADVs down into four different categories: unmanned aerial vehicles (UAVs or “drones”); self-driving cars; autonomous delivery pods; and sidewalk delivery robots, which are sometimes called personal delivery robots (PDRs). The article describes the risks and benefits of deploying ADVs for last-mile delivery and analyzes the laws and federal agencies that regulate them. Last mile delivery is generally thought to be “the most expensive and time-consuming part of the shipping process” because it is the most personalized and unpredictable. Industry estimates suggest that last-mile delivery can account for up to 53 percent of total shipping costs. ADV manufacturers claim they can reduce delivery time, increase efficiency, cut costs, improve the consumer experience, decrease traffic congestion, reduce carbon emissions, assist seniors and people with disabilities who may have decreased mobility, and democratize access to logistics and delivery resources for small businesses allowing them to compete with large corporations. Critics claim ADVs may negatively impact public health by encouraging inactivity, obstructing roads and sidewalks and impairing the mobility of seniors and people with disabilities, and endangering public safety due to their potential to collide with people who are not agile enough to get out of the way. ADVs may also reduce the need for human delivery workers, cause noise pollution, violate people’s privacy, and represent the increasing privatization of public spaces such as sidewalks. Though all ADVs will be discussed, my focus is primarily on sidewalk delivery robots because they are the newest and fastest growing segment of the ADV industry, and they face the fewest legal and regulatory hurdles. Particular attention will be paid to the differences between the laws that regulate sidewalk delivery robots and the laws that govern other types of ADVs. The article concludes by drawing lessons from the regulation of UAVs and self-driving cars to propose legislation to regulate sidewalk delivery robots that will increase their safety and utility while limiting the privatization of public spaces.

Abstract. Unmanned Aerial Vehicles (UAVs) are increasingly utilized for package delivery due to their efficiency and automation capabilities. UAVs can execute autonomous flight missions using Global Positioning System (GPS)-based navigation. However, challenges arise in the final stage of delivery, known as the last-mile delivery problem. The limitations of GPS-based navigation, the absence of recipient authentication, and shifting drop-off points create reliability and safety concerns. External factors such as varied environmental topography further contribute to delivery inaccuracies, highlighting the need for a more precise approach. Purpose: Many studies have explored UAV navigation and delivery systems, but challenges in last-mile delivery remain unresolved. This research introduces an improved UAV delivery system using computer vision (CV) and image-based visual servoing (IBVS) with QR Codes as location markers. The aim is to enhance UAV navigation accuracy and recipient verification, ensuring more reliable package deliveries. Methods/Study design/approach: The study implements a CV-based navigation system where QR Codes serve as landing markers for UAVs. Image processing is conducted using a companion computer linked to the UAV's flight control system. The IBVS method enables UAVs to adjust their position in real-time, minimizing GPS errors. Recipient verification is performed through QR Code scanning before releasing the package. The system is tested through computer simulations and real flight experiments to assess accuracy and effectiveness. Result/Findings: Experimental results demonstrate that UAVs equipped with the IBVS method can successfully complete package delivery missions with improved accuracy. GPS errors are corrected by aligning the UAV's position with QR Code markers, and recipient authentication is verified before package release. Real-flight tests confirm that this approach significantly enhances UAV delivery reliability compared to conventional GPS-based navigation. Novelty/Originality/Value: This research presents a novel integration of computer vision and UAV navigation for addressing last-mile delivery challenges. By leveraging IBVS and QR Code-based authentication, UAVs can perform fully autonomous, precise, and secure package deliveries. This method offers a viable solution to improve UAV-based logistics, reducing delivery errors and enhancing operational safety.

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

Time-dependent hydrogen fuel cell vehicle routing problem with drones and variable drone speeds

With the rapid development of e-commerce, logistics and distribution systems face the dual pressures of efficiency improvement and cost control. Unmanned Aerial Vehicle (UAV) delivery, featuring flexibility, high efficiency, and low carbon emissions, has become an effective means to solve the “last-mile” problem. However, the widespread no-fly zones in urban environments (e.g., airports, government agencies, and high-voltage power lines) severely limit the application scope of UAVs and increase the complexity of path planning. Against this backdrop, the vehicle-assisted UAV collaborative delivery model has emerged: through the division of labor and collaboration between ground vehicles and UAVs, it not only expands the service radius of UAVs but also overcomes the constraints of no-fly zones, achieving dual improvements in delivery efficiency and service quality.This study focuses on the optimization of vehicle-assisted UAV delivery paths under no-fly zone constraints, aiming to construct a multi-objective optimization model that balances delivery costs, carbon emissions, and customer satisfaction, and to design an efficient solution algorithm for providing scientific decision support to logistics enterprises. First, the paper systematically sorts out the classification and definition of no-fly zones as well as their impact mechanisms on UAV path planning, and elaborates on the theoretical basis of vehicle-UAV collaborative delivery, including the constituent elements of the problem, methods for quantifying customer satisfaction, and the application framework of heuristic algorithms. On this basis, a mixed-integer programming model is built with the objectives of minimizing total cost, minimizing carbon emissions, and maximizing customer satisfaction. Given that this model falls into the category of NP-hard problems, we have designed a four-stage heuristic solution. First, an improved K-means algorithm (IKM) is used to cluster customer points under the constraint of the UAV’s maximum flight radius, so as to determine vehicle parking points. Second, a multi-objective genetic algorithm is applied to plan UAV delivery routes for customers in open areas. Next, the multi-objective genetic algorithm is continued to design initial routes for vehicles between parking points. Finally, the multi-objective genetic algorithm is utilized again to plan delivery routes for customers in no-fly zones, ultimately forming a complete collaborative “vehicle-UAV” delivery scheme.To verify the effectiveness of the model and algorithm, simulation experiments are conducted using two sets of cases: 30 customer points in a local area of Harbin and the large-scale R201 case from the Solomon dataset. The results show that compared with traditional vehicle-only or UAV-only delivery models, the vehicle-UAV collaborative delivery model exhibits significant advantages in total cost, carbon emissions, and customer satisfaction; the model maintains good robustness in stability tests under different no-fly zone settings; and parameter sensitivity analysis further reveals the impact of key parameters (e.g., UAV load capacity, endurance, and vehicle load capacity) on delivery performance, providing practical references for logistics enterprises in equipment selection and operation strategy formulation.

Unmanned aerial vehicles (UAVs) are expected to make groundbreaking changes in the logistics industry. Leading logistics companies have been developing and testing their usage of UAVs recently as an environmentally friendly and cost-effective option. In this paper, we investigate how much the UAV delivery service is environmentally friendly compared to the traditional ground vehicle (GV) delivery service. Since there are fuel (battery) and loadable weight restrictions in the UAV delivery, multi-hopping of UAV is necessary, which may cause a large consumption of electrical energy. We present a two-phase approach. In Phase I, a new vehicle routing model to obtain optimal delivery schedules for both UAV-alone and GV-alone delivery systems is proposed, which considers each system’s restrictions, such as the max loadable weight and fuel replenishment. In Phase II, CO2 emissions are computed as a sustainability measure based on the travelling distance of the optimal route obtained from Phase I, along with various GV travel-speeds. A case study finds that the UAV-alone delivery system is much more CO2 efficient in all ranges of the GV speeds investigated.

Advancing the marketing‐operations interface in omnichannel retail

Optimal scheduling and quantitative analysis for multi-flying warehouse scheduling problem: Amazon airborne fulfillment center

Using multiple unmanned aerial vehicles (UAVs) to perform some tasks cooperatively has received growing attention in recent years. Task assignment is a difficult problem in mission planning. Multiple tasks assignment problem for cooperating homogeneous UAVs is considered as a traditional combinatorial optimization problem. This paper addresses the problem of assigning multiple tasks to cooperative homogeneous UAVs, minimizing the total cost and balancing the cost of each UAV. We propose a centralized task assignment scheme which is based on minimum spanning tree. This scheme involves two phases. In the first phase, we use the Kruskal algorithm and the breadth first search algorithm to assign all tasks to UAVs and get a proper initial task assignment solution. The second phase involves the Pareto optimization improvement in the solution generated from the first phase. For a single UAV, we use the dynamic programming algorithm to calculate the total cost of completing all assigned tasks. The performance of the proposed scheme is compared to that of heuristic simulated annealing algorithm. The simulation results show that the proposed scheme can solve the homogeneous multi-UAV cooperative task assignment problem effectively.

<div class="section abstract"><div class="htmlview paragraph">With the growing diversification of modern urban transportation options, such as delivery robots, patrol robots, service robots, E-bikes, and E-scooters, sidewalks have gained newfound importance as critical features of High-Definition (HD) Maps. Since these emerging modes of transportation are designed to operate on sidewalks to ensure public safety, there is an urgent need for efficient and optimal sidewalk routing plans for autonomous driving systems. This paper proposed a sidewalk route planning method using a cost-based <i>A*</i> algorithm and a mini-max-based objective function for optimal routes. The proposed cost-based <i>A*</i> route planning algorithm can generate different routes based on the costs of different terrains (sidewalks and crosswalks), and the objective function can produce an efficient route for different routing scenarios or preferences while considering both travelling distance and safety levels. This paper’s work is meant to fill the gap in efficient route planning for sidewalks on aerial/HD maps.</div></div>

Unmanned Aerial Vehicles (UAVs) are being increasingly implemented in parcel delivery applications. The scientific progress in this field is progressing exponentially. However, there is a notable gap in synthesizing recent research progress in UAV applications for last-mile delivery. This review study addresses this gap and conducts an in-depth review of UAV research for last-mile delivery across seven domains: environmental performance, economic impacts, social impacts, policy and regulations, routing and scheduling, charging infrastructure, and energy consumption. The review indicates that UAVs promise to reduce last-mile delivery emissions by 71% and costs by 96.5% compared to truck delivery. Saturated knowledge analysis is conducted across the seven domains to identify potential research gaps. Additionally, this review identifies key knowledge gaps, including variability in environmental and cost data, limitations associated with 2D modelling, and a lack of experimental validation. Future research interventions aimed at advancing UAV adoption in last-mile delivery applications are discussed.

The implementation of the autonomous unmanned aerial mobility is a game changer for the on-demand delivery service in the crowded urban setting. In this study, the first of its kind commercial unmanned aerial vehicle (UAV) urban delivery program in China is targeted. Different from the traditional ground pickup and delivery services, the aerial mode considers not only the time window constraints, but also the spatial conflicts incurred during the take-off and landing operations of UAVs. To obtain the optimal flying routes of the focused problem, a mixed integer programming model is formulated. Due to its inherent complexity, the optimal schedule cannot be attained within acceptable time via the off-the-shelf solvers. To help speed up the solving process, a branch-and-cut based exact algorithm is proposed, together with a series of customized valid inequalities. To further accelerate, a greedy insertion heuristic is designed to secure high-quality initial solutions. In the numerical section, it is observed that the algorithm proposed in this paper can help solve the real-life on-demand UAV delivery problem to near optimum (within 5% optimality gap) within reasonable computation time (in 5 minutes). <i>Note to Practitioners</i>&#x2014;With the increase of labor cost, the distribution cost increases very rapidly. In the meantime, the employment of automated vehicles for logistics reshapes the landscape of the urban last-mile delivery. As an efficient courier carrier, the unmanned aerial vehicle (UAV) is trending the autonomous delivery endeavour. When integrating UAVs into the urban delivery program, practitioners need to pay special attention to the scheduling of UAVs at the operational level in addition to the hardware of the UAVs. To help solve the UAV dispatch problem, we propose an online scheduling scheme, considering the spatial conflict constraints in the actual UAV operations. And an exact algorithm is designed to accelerate the solving process. Numerical experiments demonstrate that the proposed algorithm can achieve near optimal dispatch plan with 5% optimality gap in 5 minutes. Furthermore, it is discovered that the demand pooling is an essential decision to make for UAV-based delivery. Longer pooling time can increase the UAV efficiency with more realized demand information, but too much pooling could lead to prolonged customer waiting and a low service level.

Unmanned aerial vehicles (UAVs) are playing an important role in serving ground users as base stations (BSs) during temporary events, or after terrestrial BSs fail. UAV deployment is a fundamental and vital problem which directly affects the coverage capacity and user experience. Especially, while deploying a UAV backbone network, the cost and the quality of service (QoS) of the ground users as well as the connectivity among UAVs need to be considered jointly. In this paper, the deployment problem is modeled as minimizing the amount of deployed UAVs while guaranteeing the coverage capacity and connectivity among UAVs. Further, a novel UAV deployment scheme is proposed where a heuristic algorithm is adopted to delete locations from the initially defined candidate UAV locations iteratively, and UAVs are deployed at the remaining locations finally. The simulation results show that our UAV deployment scheme can achieve minimum deployed UAVs while ensuring QoS requirements and a robust network among UAVs.