Unmanned Aerial Vehicle Route Research Articles

Integrated Host- and Content-Centric Routing for Efficient and Scalable Networking of UAV Swarm

Efficient and scalable networking is a key enabler of the Unmanned Aerial Vehicle (UAV) swarms, wherein multiple UAVs cooperatively execute complicated tasks. Despite the intermittent connections due to the UAV mobility, stable paths may exist temporally in periods like formation keeping, which is rarely considered or utilized in existing UAV routing designs. In this paper, we propose an integrated host- and content-centric routing (IHCR) mechanism to harness the advantages of both routing mechanisms. Specifically, the routing information of stable paths is reused in a host-centric manner to reduce the flooding for path exploring. In addition, the route failure detection and re-routing are content-centric to adjust to the topology dynamics. The challenges lie in the inherent contradiction between host-centric and content-centric routing mechanisms (e.g., naming spaces) and the tradeoff between path reusing and re-routing. To overcome these challenges, we appropriately incorporate node names into content names and then fully exploit reusable paths via time-based route failure detection and delayed forwarding. Packet-level simulation results show that IHCR increases the packet delivery ratio by 60.1%, and enlarges the achievable network scale by 4.2 times compared to state-of-the-art routing mechanisms.

OPTIMIZING UNMANNED AERIAL VEHICLE BASED FOOD DELIVERY THROUGH VEHICLE ROUTING PROBLEM: A COMPARATIVE ANALYSIS OF THREE DELIVERY SYSTEMS.

In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.

Mathematical Optimization in Innovation Productivity: A Framework and A Case Study on UAV Border Patrolling in Türkiye

Purpose: In this paper, the potential of mathematical optimization (MO) in enhancing innovation productivity is explored. Innovation is a process that converts new ideas and methods into products and services, MO can contribute to innovation management by improving productivity across all stages, from pre-innovation to post-innovation. This paper establishes a connection between MO and innovation productivity while demonstrating an application for a post-innovation phase problem of unmanned aerial vehicles (UAVs). Methodology: A framework for incorporating MO into the design problems of innovation processes is developed. Additionally, a MO model is developed for a case study concerning UAV border patrolling in Türkiye. Findings: Computational experiments demonstrate MO's effectiveness in optimizing UAV routes and strategies, enhancing operational efficiency, and innovation productivity. Optimal recommendations and trade-offs among different mission considerations are obtained in 18 minutes on average (with a median of 5 seconds) over 210 runs. Originality: A link is established between MO and innovation productivity. An operations research problem is introduced for UAV operations in border patrolling in Türkiye. The codebase and data are openly provided for readers to apply the model in their research.

Multigene and Improved Anti-Collision RRT* Algorithms for Unmanned Aerial Vehicle Task Allocation and Route Planning in an Urban Air Mobility Scenario.

Compared to terrestrial transportation systems, the expansion of urban traffic into airspace can not only mitigate traffic congestion, but also foster establish eco-friendly transportation networks. Additionally, unmanned aerial vehicle (UAV) task allocation and trajectory planning are essential research topics for an Urban Air Mobility (UAM) scenario. However, heterogeneous tasks, temporary flight restriction zones, physical buildings, and environment prerequisites put forward challenges for the research. In this paper, multigene and improved anti-collision RRT* (IAC-RRT*) algorithms are proposed to address the challenge of task allocation and path planning problems in UAM scenarios by tailoring the chance of crossover and mutation. It is proved that multigene and IAC-RRT* algorithms can effectively minimize energy consumption and tasks' completion duration of UAVs. Simulation results demonstrate that the strategy of this work surpasses traditional optimization algorithms, i.e., RRT algorithm and gene algorithm, in terms of numerical stability and convergence speed.

IDENTIFYING THE POTENTIAL OF UNMANNED AERIAL VEHICLE ROUTING FOR BLOOD DISTRIBUTION IN EMERGENCY REQUESTS

This study is focusing on identifying the potential of Unmanned Aerial Vehicle (UAV) routing for blood distribution in emergency requests in Sri Lanka compared to existing transportation modes. Capacitated Unmanned Aerial Vehicle Routing Problem was used as the methodology to find the optimal distribution plan between blood banks directing emergency requests. The developed UAV routing model was tested for different instances to compare the results. Finally, the proposed distribution process via UAVs was compared with the current distribution process for the objective function set up in the model and other Key Performance Indicators (KPIs) including energy consumption savings and operational cost savings. The average percentage of distribution time re-duction, energy consumption cost reduction, and operational cost per day reduction utilizing UAVs were determined to be 58.57%, 96.35%, and 61.20%, respectively, for the instances tested using the model highlighting the potential of UAVs. Therefore, the deficiencies in Sri Lanka's present blood delivery system can be addressed using UAVs' potential for time, cost, and energy savings. The ability to save time through the deployment of UAVs to the fleet during emergency situations plays a crucial role in preventing the loss of human lives.

A PAD-Based Unmanned Aerial Vehichle Route Planning Scheme for Remote Sensing in Huge Regions.

Unmanned aerial vehicles (UAVs) have been employed extensively for remote-sensing missions. However, due to their energy limitations, UAVs have a restricted flight operating time and spatial coverage, which makes remote sensing over huge regions that are out of UAV flight endurance and range challenging. PAD is an autonomous wireless charging station that might significantly increase the flying time of UAVs by recharging them in the air. In this work, we introduce PADs to simplify UAV-based remote sensing over a huge region, and then we explore the UAV route planning problem once PADs have been predeployed throughout a huge remote sensing region. A route planning scheme, named PAD-based remote sensing (PBRS), is proposed to solve the problem. The PBRS scheme first plans the UAV's round-trip routes based on the location of the PADs and divides the whole target region into multiple PAD-based subregions. Between adjacent subregions, the UAV flight subroute is planned by determining piggyback points to minimize the total time for remote sensing. We demonstrate the effectiveness of the proposed scheme by conducting several sets of simulation experiments based on the digital orthophoto model of Hutou Village in Beibei District, Chongqing, China. The results show that the PBRS scheme can achieve excellent performance in three metrics of remote sensing duration, the number of trips to charging stations, and the data-storage rate in UAV remote-sensing missions over huge regions with predeployed PADs through effective planning of UAVs.

A Simulation Framework of Unmanned Aerial Vehicles Route Planning Design and Validation for Landslide Monitoring

Unmanned aerial vehicles (UAVs) have emerged as a highly efficient means of monitoring landslide-prone regions, given the growing concern for urban safety and the increasing occurrence of landslides. Designing optimal UAV flight routes is crucial for effective landslide monitoring. However, in real-world scenarios, the testing and validating of flight path planning algorithms incur high cost and safety concerns, making overall flight operations challenging. Therefore, this paper proposes the use of the Unreal Engine simulation framework to design UAV flight path planning specifically for landslide monitoring. It aims to validate the authenticity of the simulated flight paths and the correctness of the algorithms. Under the proposed simulation framework, we then test a novel flight path planning algorithm. The simulation results demonstrate that the model reconstruction obtained using the novel flight path algorithm exhibits more detailed textures, with a 3D model simulation accuracy ranging from 10 to 14 cm. Among them, the RMSE value of the novel flight route algorithm falls within the range of 10 to 11 cm, exhibiting a 2 to 3 cm improvement in accuracy compared to the traditional flight path algorithm. Additionally, it effectively reduces the flight duration by 9.3% under the same flight path compared to conventional methods. The results confirm that the simulation framework developed in this paper meets the requirements for landslide damage monitoring and validates the feasibility and correctness of the UAV flight path planning algorithm.

ANALYSIS OF EXTERIOR ORIENTATION PARAMETERS ON MONOPLOTTING PROCEDURE FOR AVOIDING OBSTACLES IN UAV REAL-TIME ROUTING

Abstract. Unmanned Aerial Vehicles (UAVs) are adopted in different applications, such as mapping, logistics, and surveillance. For some applications, it is important to identify people or objects to be tracked in the image and define their coordinates in the object space. It can be done by using a photogrammetric process called Monoplotting. Moreover, by applying the Propagation of Variance and Covariance, the standard deviation from planimetric coordinates can be defined as an Exterior Orientation Parameter (EOP) function. This work evaluates the planimetric accuracy achieved using points defined in UAV images and the impact of EOPs precision. Two UAV images, the Digitals Surface Model (DSM) from the corresponding areas, the EOPs (X0, Y0, Z0, ω, φ, κ), and the platform/sensors characteristics were used for the experiments. Results showed that planimetric precision is low when using UAVs with a low-cost positioning system, especially if the points of interest in the image are far from the nadir. For UAVs with a high-precision positioning system, Inertial Measurement Unit (IMU) system is the one with a higher influence on the accuracy of planimetric coordinates, especially for oblique photographs (essential for UAV routing when avoiding obstacles). The impact of the IMU system precision increases in oblique photographs, this alerts to attention for UAV flight real-time routing guided by obstacles mapping using images.

Parcel consolidation approach and routing algorithm for last-mile delivery by unmanned aerial vehicles

The last-mile delivery by unmanned aerial vehicles (UAVs), emerging from the online grocery retailing industry, has attracted much attention and interest from the scientific and industrial communities. In reality, the online retailing platform receives grocery orders and promises ultrafast delivery service to the customers via a two-echelon logistics and distribution network. The uncertain arrivals of the orders have a nontrivial negative effect on the performance of the whole delivery network, which has not been well studied in the literature. This paper investigates the parcel consolidation policy and the UAVs routes for the last-mile delivery from a transshipment site in a distribution network to the final customers. First, this study proposes a nonmyopia consolidation policy considering the upcoming grocery parcels with random arrival times to minimize the total delivery cost including the possible penalty cost due to the delivery delay to the customer. To make the problem tractable in the real-time computational setting, an approximation method based on Bayesian estimation is proposed to reduce a large number of random arrival scenarios of upcoming parcels to an expected scenario. The problem is then approached with a deterministic model under the expected scenario. Subsequently, a discrete particle swarm optimization (DPSO) algorithm is developed to solve the model. In the algorithm, a novel decoding method is designed to evaluate the particles with respect to the constraints of the load and battery capacities of a UAV and a neighborhood search based on reinforcement learning is developed to improve the quality of the particles in the searching process. The experimental results based on a case study validate the performance of the proposed parcel consolidation approach and the UAV routing algorithm. Some findings are given based on the variations of benchmarks with different distributions of customer locations. The approaches and insights in this paper could be used as a reference for last-mile delivery by UAVs.

Hybrid ant colony and intelligent water drop algorithm for route planning of unmanned aerial vehicles

Route planning is a crucial element in unmanned aerial vehicle (UAV) systems, particularly in autonomous UAV technology. In the past decades, various algorithms have been proposed for UAV route planning. However, they still have defects, such as stagnation and slow search rates. In this study, a novel hybrid algorithm which integrates ant colony optimization (ACO) and intelligent water drop (IWD) is proposed for UAV route planning. First, the advantages of the IWD and ACO algorithms are combined in an iterative strategy, to ensure mutual cooperation via exchange of information for route optimization. Initially, the water drops optimize the soil and pheromones within the environment simultaneously to generate good approximate solutions and an initial pheromone distribution for the ant colony. Based thereupon, the ant colony roams the solution space to further optimize the routes, thus combining the exploration potential and exploitability of two types of agents. Additionally, a novel node selection strategy is proposed to guide the agents’ route planning along a reasonable direction. Compared with state-of-the-art algorithms, the convergence accuracy, success rate, and stability of the proposed algorithm exhibited significant improvements of approximately 8.25%, 4.20%, and 66.20%, respectively.

An Operational Capacity Assessment Method for an Urban Low-Altitude Unmanned Aerial Vehicle Logistics Route Network

The Federal Aviation Administration introduced the concept of urban air mobility (UAM), a new three-dimensional transport system that operates with a fusion of manned/unmanned aerial vehicles on an urban or intercity scale. The rapid development of UAM has brought innovation and dynamism to many industries, especially in the field of logistics. Various types of unmanned aerial vehicles (UAVs) for use in transport logistics are being designed and produced. UAV logistics refers to the use of UAVs, usually carrying goods and parcels, to achieve route planning, identify risk perception, facilitate parcel delivery, and carry out other functions. This research provides a method for assessing the operational capacity of a UAV logistics route network. The concept of “logistics UAV route network operation capacity” is defined, and a bi-objective optimization model for assessing the route network’s operating capacity is developed. The first objective is to maximize the number of UAV logistics delivery plans that can be executed in a fixed operation time. The second objective is to minimize the total operational impedance value in a fixed operation time. To solve the bi-objective optimization model, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is utilized. A UAV logistics route network with 62 nodes is developed to assess the rationale and validity of the proposed concept. The experiments show that with an increase in operation time, the route network’s optimal operational capacity gradually increases, the convergence speed of the algorithm slows down, and the optimization magnitude gradually reduces. Two key parameters—operational safety interval and flight speed—are further analyzed in the experiments. According to the experiments, as the safety interval increases, the route network’s average operational capacity steadily diminishes, as does its sensitivity to the safety interval. The average operational capacity steadily rose with the rise in flight speed, especially when the UAV logistics flight speed was between 10 m/s and 10.5 m/s. In that range, the operational capacity of the route network was substantially impacted by the flight speed.

Adaptive large neighborhood search algorithm for the Unmanned aerial vehicle routing problem with recharging

The applications of Unmanned aerial vehicles (UAVs) in both civilian and military fields are drawing increasing attention recently. This paper investigates a new routing problem of small UAVs for information collection with time windows, where UAVs can be recharged at platforms (e.g. ground vehicles or stations) distributed in a given area. Different from the previous works on UAV routing, the UAVs are allowed to partially recharge their batteries according to the requirement in the following route. A mixed integer nonlinear programming model is developed to formulate the problem, where the overall time for completing all targets’ observation and the number of UAVs are minimized. An improved adaptive large neighborhood search (ALNS) algorithm with simulated annealing strategies is designed, and a recharging platform insertion heuristic is developed to determine the recharging strategy and construct feasible solutions. To verify the efficiency of the proposed algorithms, a set of new benchmark instances are designed based on the well-known Solomon data set and solved. The computational results are compared with those obtained by the ant colony optimization and variable neighborhood search, which shows that ALNS performs significantly better and stable. Furthermore, experimental analysis indicates that important advantages can be obtained through introducing the recharging strategy for small UAVs.

Decentralized, Privacy-Preserving Routing of Cellular-Connected Unmanned Aerial Vehicles for Joint Goods Delivery and Sensing

Unmanned aerial vehicles (UAVs) have been extensively applied to goods delivery and in-situ sensing. It becomes increasingly probable that multiple UAVs are delivering goods and carrying out sensing tasks at the same time. The destinations of the UAVs are usually required to jointly design their trajectories and sensing selections, leading to privacy concerns for the UAVs. This paper presents a new game-theoretic routing framework for joint goods delivery and sensing of multiple cellular-connected UAVs, where the UAVs minimize their energy consumption and connectivity outage, maximize their sensing reward, and ensure timely goods delivery and trajectory privacy by optimizing their trajectories and sensing task selections in a decentralized manner. The key idea is that we unify routing and sensing in a single task selection process, which is further transformed into routing on a task-time graph. Another important aspect is that we design a non-cooperative potential game for the routing on the task-time graph. A distributed strategy is developed, where each UAV only reports its sensing task selections and withholds its destination information and its best response produced by the Bellman-Ford algorithm. By this means, the destination and trajectory privacy of the UAVs are protected. Simulations show that the new game-theoretic approach can ensure timely delivery and achieve close-to-optimal solutions with significantly lower complexity compared to a centralized brute-force approach.

A Reliable Delivery Logistics System Based on the Collaboration of UAVs and Vehicles

In recent years, land–air collaborative logistics delivery is a promising distribution method. It combines the flexibility of unmanned aerial vehicles (UAVs) with the high payload capacity of vehicles, expanding the service range of UAVs while reducing carbon emissions. However, most existing research has focused on path planning and resource allocation for either UAVs or vehicles alone. Therefore, to address the shortcomings of the current research, this paper proposes an intelligent land–air collaboration delivery algorithm for trajectory optimization and resource scheduling. Subsequently, this paper develops a land–air collaboration reliable delivery logistics distribution system, showcasing the driving routes of vehicles and UAVs. Meanwhile, the mode of UAV–vehicle collaboration not only saves operating costs compared to traditional logistics delivery but also achieves energy conservation and emission reduction goals. During the specific design and implementation process of this platform, blockchain technology is integrated into the logistics delivery service to ensure data security and prevent tampering, making the system more efficient and reliable. Finally, through testing and verification of the system’s functionalities, its completeness is demonstrated.

A bi-level planning approach of logistics unmanned aerial vehicle route network

The rapid growth of e-commerce in urban areas has put incremental pressure on delivery. Unmanned Aerial Vehicle (UAV) has great potential in the logistics field with the aggravation of ground congestion. This study proposes a logistics UAV route network planning approach based on the bi-level program, which from the perspectives of network managers and users. The upper model focuses on the macro network configuration, while the lower model focuses on the UAV traffic flow distribution. The two-layer Simulated Annealing (SA) is presented to deal with the iterative process of network topology and traffic flow balance allocation. A case study is carried out using a region from Nanjing, to introduce the application of this bi-level approach. The results suggest that there is a positive correlation between the network overall length and the flow entropy, a negative correlation between the network overall length and the nonlinear coefficient. When the maximum transit time of UAV is 3, the comprehensive performance of network operation is optimal. This study provides a new way for logistics UAV network planning in urban areas, which is significant to regulate the operation of large-scale UAVs.

Autonomous aerial robotics for package delivery: A technical review

AbstractSmall unmanned aerial vehicles (UAVs) have gained significant interest in the last decade. More specifically these vehicles have the capacity to impact package delivery logistics in a disruptive way. This paper reviews research problems and state‐of‐the‐art solutions that facilitate package delivery. Different aerial manipulators and grippers are listed along with control techniques to address stability issues. Landing on a platform is next discussed which encompasses static and dynamic platforms. Landing on a dynamic platform presents further challenges. This includes delayed control responses and poor precision of the relative motion between the platform and the aerial vehicle. Subsequently, risks such as weather conditions, state estimation, and collision avoidance to ensure safe transit is considered. Finally, delivery UAV routing is investigated which categorizes the topic into two areas: drone operations and drone–truck collaborative operations. Additionally, we compare the solutions against design, environmental, and legal constraints.

Optimal routing and scheduling of unmanned aerial vehicles for delivery services

ABSTRACT 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.

A novel waypoint guidance and adaptive evolution strategy for unmanned aerial vehicle 3D route planning

In the field of unmanned aerial vehicles (UAVs) mission planning, autonomous route planning in complex 3D environments represents a crucial aspect. To enhance the utilization of valuable information and increase search efficiency when solving path planning problems in 3D environments, a novel UAV route planning algorithm named WG-GWO is proposed with a waypoint guidance and an adaptive evolution strategy. Initially, we introduce a novel waypoint extraction method based on cubic B-spline to harness crucial information from individuals eliminated during the evolutionary process. Subsequently, an adaptive evolutionary direction selection strategy is introduced, which utilizes historical information and a differential operator to improve the search ability. The WG-GWO also incorporates Le´vy flight to prevent premature convergence to local minimums. Simulation results validate the effectiveness of the proposed algorithm, demonstrating that WG-GWO outperforms several existing algorithms in generating feasible routes and achieving faster convergence speeds across various scenarios. These results suggest that WG-GWO holds potential for practical engineering applications.

Multi-UAV computing enabling efficient clustering-based IoT for energy reduction and data transmission

Internet of Things (IoT) technologies increasingly integrate unmanned aerial vehicles (UAVs). IoT devices that are becoming more networked produce massive data. The process and memory of this enormous volume of data at local nodes, particularly when utilizing artificial intelligence (AI) algorithms to collect and utilize useful information, have been declared vital issues. In this paper, we introduce UAV computing to solve greater energy consumption, delay difficulties using task offload and clustered approaches, and make cloud computing operations accessible to IoT devices. First, we present a clustering technique to group IoT devices for data transmission. After that, we apply the Q-learning approach to accomplish task offloading and allocate the difficult tasks to UAVs that are not yet fully loaded. The sensor readings from the CHs are then collected using UAV path planning. Furthermore, We use a convolutional neural network (CNN) to achieve UAV route planning. In terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets, the effectiveness of the current study is finally compared with the existing techniques using UAVs. The results showed that the suggested strategy outperformed the current approaches in terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets. Additionally, the proposed technique consumed less energy due to CNN-based route planning and dynamic positioning, which reduced UAV transmits power. Overall, the study concluded that the suggested approach is effective for improving energy-efficient and responsive data transmission in crises.

Research on route planning for solar UAV based on the intelligent optimization algorithm.

The solar unmanned aerial vehicle (UAV) route planning needs to comprehensively consider the conversion efficiency of solar cells under the influence of solar ground reflection radiation and sky scattering radiation. On the one hand, it is necessary to consider the cost of radar threat, mileage energy consumption, mountain impact and other costs. On the other hand, it is also necessary to consider the influence of high threat, mountain shadow occlusion cost, and cloud shading cost on solar photovoltaic conversion efficiency. The above problem was solved through using the ant colony intelligent optimization algorithm. By constructing ant colony paths rationally, models of mountain impact cost, high threat, mountain shadow shelter cost, and cloud shading cost were established. The constraints such as the maximum action distance, solar irradiation angle and effective action distance of various costs were introduced into the cost model and exploration factor calculation, and the comprehensive optimization problem of solar UAV route was solved. Finally, the simulation results show that the algorithm path structure is reasonable; the target node can be found independently; the convergence speed can meet the requirements of route planning; the generated route cost is small; the algorithm is reasonable and effective.