Unmanned Aerial Vehicle Route Research Articles

Optimal design of artificial bee colony based UAV routing (ABCUR) algorithm for healthcare applications

PurposeTo avoid this situation, the authors proposed an optimal artificial bee colony algorithm-based Unmanned Aerial Vehicle (UAV) routing algorithm for efficient data communication between doctors and patients. This proposed method worked in three stages.Design/methodology/approachIn recent decades, wireless body area networks have played an important role in health care applications. It facilitates the transmission of the patients' health data analysis report to the appropriate doctors.FindingsIn the first phase, biological sensors are connected to the human body via a controller node and collected data is transmitted via Bluetooth to the Personal Device Assistant (PDA). In the second phase, collected data will be transmitted via the Internet of things using an artificial bee colony algorithm. The second aids in determining the best route. In the third phase, unmanned aerial vehicles will use the best path to send collected data to doctors, caregivers, ambulances and cloud storage servers.Originality/valueThe simulation results show that the network's performance is superior when compared to existing approaches. The proposed algorithm achieves a high throughput, a lower delay, a higher link rate and a higher delivery rate.

Nuclear Accident Emergency Response System: Radiation Field Estimation and Evacuation

In this paper, a nuclear accident emergency response system based on unmanned aerial vehicles (UAVs) and bus collaboration is designed for radiation field estimation and evacuation. When a nuclear accident occurs, the radiation field is estimated firstly using the measurements acquired by UAVs. Based on the Cramer–Rao Lower Bound (CRLB), the coordinate optimization combined with UAV routing is formulated as a nonconvex mixed integer nonlinear programming (MINLP) problem to maximize the estimation accuracy. Further, a two-stage solution procedure based on genetic algorithm (GA) is proposed to solve the above problem. Then, taking the predicted radiation field as input, personnel in the emergency planning zone (EPZ) are evacuated to shelters by buses. The evacuation routing problem for minimizing both the total radiation exposure and evacuation time is formulated as a mixed integer linear programming (MILP) problem, which is solvable with efficient commercial solvers, such as Gurobi and CPLEX. The simulation results indicate that the system can provide effective help for emergency management under the nuclear accident scenarios.

Real-Time UAV Routing Strategy for Monitoring and Inspection for Postdisaster Restoration of Distribution Networks

After a natural disaster, a quick inspection of all damaged components is crucial to recover the functionality of distribution networks. Unmanned aerial vehicles (UAVs) can perform inspection tasks, particularly for damages that are difficult to access for human repair crews. Additionally, UAVs can monitor the transmission lines to find potential dangers and early-stage damages, and to monitor the road infrastructure to provide real-time information about traffic conditions so that repair crews can select the best ways to reach damages. Besides, due to unpredictable events during restoration, the UAV routing strategy (UAVRS) needs to be updated in real time. Thus, the proposed UAVRS in this article determines the optimal routes for the UAVs allocated to inspect damages as well as the optimal routes for the UAVs to monitor transmission lines and roads in real time for distribution networks. To tackle the multi-time-scale characteristic of the proposed UAVRS, a two-layer decision-making architecture is proposed. A bilevel programming problem is solved in the first layer for the large-time-scale problem, and a mixed-integer linear programming problem is solved for the small-time-scale problem in the second layer. A case study based on the distribution network in Zaltbommel and its neighbor areas, in The Netherlands, illustrates the effectiveness of our real-time method compared to the offline methods. Furthermore, different solvers are studied and compared in view of the real-time requirement.

An Improved Particle Swarm Optimization Algorithm for Unmanned Aerial Vehicle Route Planning

A global path planning method based on improved particle swarm optimization (PSO) algorithm was proposed to find a high quality flight trajectory in three-dimensional complex environment under multiple threats for UAV. The improved path planning algorithm combines the standard PSO with A* method to compensate for the slow convergence rate of PSO. The objective function with multiple constraints of A* method is used to evaluate the quality of the waypoint, and the objective function of PSO algorithm is designed to evaluate the quality of the candidate path. To verify the effectiveness of the improved algorithm, the improved PSO algorithm is used to compare with basic PSO algorithms. The experiment in complex environment shows it has stronger search ability, convergence ability due to the improvement of population diversity and convergence speed.

Optimizing UAV traffic monitoring routes during rush hours considering spatiotemporal variation of monitoring demand

Dynamic changes in traffic conditions cause spatiotemporal variation in traffic monitoring demand. It is, therefore, necessary to conduct efficient road monitoring to identify dynamic abnormal situations, especially in peak traffic periods. Recently, unmanned aerial vehicles (UAVs) have become an attractive solution to this problem. However, UAV monitoring routes suffer from time limitations during peak traffic hours. To optimize UAV monitoring routes during rush hours, we develop a route planning method incorporating spatiotemporal variations in monitoring demand, in which we introduce a team orienteering arc routing problem with time-varying profits (TOARP-TP) and construct a corresponding mathematical model. The TOARP-TP is an extension of an already existing routing problem, team orienteering arc routing problem (TOARP). An iterated local search (ILS)-based algorithm is designed to solve the large instances of this problem. To verify the proposed method, we conduct sets of numerical experiments with the Sioux Falls road network in South Dakota, US, and a case study is applied using Wuhan, Hubei, PRC. The results demonstrate the efficiency and practicality of our method in optimizing UAV traffic monitoring routes during rush hours. Furthermore, we discuss a strategy for scenario determination and method selection in UAV route planning.

Group Mobility Model for Complex Multimission Cooperation of UAV Swarm

In the unmanned aerial vehicle (UAV) swarm combat system, multiple UAVs’ collaborative operations can solve the bottleneck of the limited capability of a single UAV when they carry out complicated missions in complex combat scenarios. As one of the critical technologies of UAV collaborative operation, the mobility model is the basic infrastructure that plays an important role for UAV networking, routing, and task scheduling, especially in high dynamic and real-time scenarios. Focused on real-time guarantee and complex mission cooperative execution, a multilevel reference node mobility model based on the reference node strategy, namely, the ML-RNGM model, is proposed. In this model, the task decomposition and task correlation of UAV cluster execution are realized by using the multilayer task scheduling model. Based on the gravity model of spatial interaction and the correlation between tasks, the reference node selection algorithm is proposed to select the appropriate reference node in the process of node movement. This model can improve the real-time performance of individual tasks and the overall mission group carried out by UAVs. Meanwhile, this model can enhance the connectivity between UAVs when they are performing the same mission group. Finally, OMNeT++ is used to simulate the ML-RNGM model with three experiments, including the different number of nodes and clusters. Within the three experiments, the ML-RNGM model is compared with the random class mobility model, the reference class mobility model, and the associated class mobility model for the network connectivity rate, the average end-to-end delay, and the overhead caused by algorithms. The experimental results show that the ML-RNGM model achieves an obvious improvement in network connectivity and real-time performance for missions and tasks.

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

The use of drones, formally known as unmanned aerial vehicles (UAVs), has significantly increased across a variety of applications over the past few years. This is due to the rapid advancement towards the design and production of inexpensive and dependable UAVs and the growing request for the utilization of such platforms particularly in civil applications. With their intrinsic attributes such as high mobility, rapid deployment and flexible altitude, UAVs have the potential to be utilized in many wireless system applications. On the one hand, UAVs are able to operate as flying mobile terminals within wireless/cellular networks to support a variety of missions such as goods delivery, search and rescue, precision agriculture monitoring, and remote sensing. On the other hand, UAVs can be utilized as aerial base stations to increase wireless communication coverage, reliability, and the capacity of wireless systems without additional investment in wireless systems infrastructure. The aim of this article is to review the current applications of UAVs for civil and commercial purposes. The focus of this paper is on the challenges and communication requirements associated with UAV-based communication systems. This article initially classifies UAVs in terms of various parameters, some of which can impact UAVs’ communication performance. It then provides an overview of aerial networking and investigates UAVs routing protocols specifically, which are considered as one of the challenges in UAV communication. This article later investigates the use of UAV networks in a variety of civil applications and considers many challenges and communication demands of these applications. Subsequently, different types of simulation platforms are investigated from a communication and networking viewpoint. Finally, it identifies areas of future research.

Vehicle Routing Problem When Using UAVs

Introduction. In recent years, the use of unmanned aerial vehicles (UAVs) is growing rapidly. Initially introduced for military purposes, drones and related technologies have been successfully switched to a number of new civilian applications in the last few years, such as delivery, logistics, surveillance, entertainment, and more. They also opened up new opportunities, such as working in difficult or dangerous areas. The UAV has the potential to solve the problem of air mobility, allowing to change transport and logistics in the future. Combining UAVs with traditional land vehicles can solve the last-mile delivery problem by achieving significant improvements in distribution costs and speed of vehicle delivery. One of the biggest challenges is to plan UAV routes with a number of constraints, including time, distance or energy costs, cargo weight, environmental and environmental conditions (such as wind direction or obstacles), UAV battery life, and demand requirements. users you want to visit. Thus, it revealed the need to classify different types of research and study the general characteristics of the study area. This article aims to help identify the main topics and new areas of research, as well as provides a published overview of the current state and contribution to the problem of UAV routing, as well as a general categorization of the problem of vehicle routing (VRP). The purpose of the paper is to analyze the scientific contributions to the problem of UAV routing to determine the main characteristics of these problems, as well as trends in research and recent improvements. Results. Sources are classified according to the areas of application of UAVs; methods that include exact, heuristic, metaheuristic, and mixed algorithms are mentioned. Conclusions. An overview of the work on routing problems using UAVs and the tasks they generate, trends in research and recent developments. Keywords: Unmanned aerial vehicle, routing, vehicle, optimization.

Formalization of the Problem of Optimization of Base Places and Routes of the UAV Group

Introduction. The problem of planning the mission of a set of heterogeneous unmanned aerial vehicles (UAVs)is considered, which is to survey and/or service a given set of targets in the field. A mathematical model of the problem and algorithms for its solving that is based on deterministic local search, as well as optimization by ant colonies are proposed. The efficiency of algorithms is investigated based on the results of solving problems with real objects in the field. The relative error of the results of each algorithm was obtained, which allowed to compare their efficiency. The purpose of the paper is to solve a routing problem in different ways to reduce overall mission cost and compare the efficiency. The problem statement considers multiple starting points and destinations (depots) for UAVs with determined capacity, so algorithms proposed in the paper are designed to optimize the initial placement. Each UAV has a maximum flight distance because of an energy limit, though vehicles can be recharged by visiting one of previously placed depots. The mission goal is to visit all the given targets while minimizing the overall cost, so fuel consumption over distance, depot placement, and resources needed to survey and/or service of the target by each UAV are considered as components of the final cost metric to be minimized considering a set of specific constraints. Results. To solve the given UAV routing problem, a max-min algorithm of ant systems was developed, which features step-by-step interaction of ants to form solutions, a hybrid taboo search algorithm and a deterministic local search algorithm - the decay vector method. The developed algorithms were tested both on the known travelling salesman problems, and on specially developed problems with multiple depots and additional restrictions. Conclusions. The proposed algorithms which are based on ant colony optimization are compared both in terms of accuracy and computation time. A hybrid algorithm achieved slightly better score, though computation time has increased. Keywords: routing, combinatorial optimization, UAV, local search, ant colony optimization algorithms.

Multi-UAV Coverage Path Planning Based on Hexagonal Grid Decomposition in Maritime Search and Rescue

In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.

Method of an unmanned aerial vehicle composition route in space

The article is devoted to the development of a method of composition of the route of an unmanned aerial vehicle in three-dimensional space. The main difference of the presented method is the complex consideration of the features of the environment, which reflects the possible obstacles (active or passive) and other limitations of the problem when composing the route of the unmanned aerial vehicle in three-dimensional space. This allowed to increase the safety of the task in autonomous flight conditions. The article analyzes the main approaches to the composition of unmanned aerial vehicle routes in space. The conclusion about the shortcomings of the two-dimensional representation is made. The method presents four stages of the task. This is the stage of modeling the environment that reflects possible obstacles (active or passive) and other limitations of the task. Stage of construction of an extended graph of unmanned aerial vehicle routes in space. The difference of this stage is the adaptive consideration of the spatial location of active obstacles in space. The next stage is the route search stage, which connects the starting point with the end and bypasses all obstacles and allows you to build a starting route in the form of a broken line, which is formed by a sequence of waypoints, and connects the starting point with the end, bypassing obstacles. The last is the stage of obtaining the final result, which is provided by smoothing the obtained broken line. In this part of the composition method, to solve the problem of smoothing the trajectory of the unmanned aerial vehicle in space on the selected route, the expediency of using the method of non-uniform cubic B-spline is proved. With the help of this method the task of selection and optimization of the smoothing parameter is set and solved.

A cumulative unmanned aerial vehicle routing problem approach for humanitarian coverage path planning

This paper presents a Cumulative Unmanned Aerial Vehicle Routing Problem (CUAVRP) approach to optimize Humanitarian Coverage Path Planning (HCPP). Coverage path planning consists of finding the route which covers every point of a certain area of interest. This paper considers a Search & Rescue mission, using a homogeneous fleet of Unmanned Aerial Vehicles (UAVs). In this scenario, the objective is to minimize the sum of arrival times at all points of the area of interest, thus, completing the search with minimum latency. The HCPP problem is transformed into a Vehicle Routing Problem by using an approximate cellular decomposition technique to discretize the area into a grid, where the rectangles represent the UAV sensor’s field of view. The center points of the formed rectangles, become the nodes used for a UAV routing problem. This approach uses the objective of minimizing the sum of arrival times at customers, found in the Cumulative Capacitated Vehicle Routing Problem (CCVRP), adjusted for the Search & Rescue Coverage Path Planning using UAVs. The Min-max objective is also implemented and tested. Three versions of a Parallel Weighted Greedy Randomized Adaptive Search Procedure - Variable Neighborhood Decent (GRASP-VND) algorithm is implemented to solve the Cumulative UAV Routing Problem for Humanitarian Coverage Path Planning.

A data-driven intelligent planning model for UAVs routing networks in mobile Internet of Things

Owing to constant progress of wireless communications, the Unmanned Aerial Vehicles (UAVs) routing networks (UAVs-RN) under mobile Internet of Things (MIoT) have been prevalent tools to deal with natural emergencies. But the achievement of effective responses and proper utility, still remains a challenging task. It is required to analyze multi-source data of UAVs-RN, so that optimal planning schemes under MIoT can be found. To bridge such gap, this work mainly takes three aspects of factors into consideration: rapid response, finite budget and uncertain signal fading. Accordingly, a data-driven intelligent planning model for UAVs-RN under MIoT, is put forward in this paper. Data about wildfire happened in local areas of Australia is selected to build experimental scenarios. And two kinds of UAVs, Surveillance and Situational Awareness drones and Radio Repeater drones, are considered in this study. Firstly, the source data is visualized and the internal trend is analyzed to verify true validity. Then, a multi-objective planning model is accordingly established to aggregate multi-source data. At last, a case study is deeply investigated on real-world data to assess the proposed approach and suggest feasible planning schemes.

A diversified group teaching optimization algorithm with segment-based fitness strategy for unmanned aerial vehicle route planning

The complexity and diversity of the flight environment pose great challenges to unmanned aerial vehicle route planning, which demands feasible flight strategies and efficient route planning algorithms. To address the issue, this paper constructs a 3-D flight environment model with multiple obstacles, and designs a novel diversified group teaching optimization algorithm for the generation of flight routes of unmanned aerial vehicles. In the environment model, a variety of obstacles are taken into consideration to make the flying scenarios more realistic, including mountain, cuboid, cylinder and triangular prism, and corresponding strategies are presented for unmanned aerial vehicles to safely avoid these obstacles. In the proposed algorithm, three novel teaching methods are introduced to balance the exploitation and exploration phases. Besides, a novel constrained optimization strategy is adopted, in which constraints are incrementally added to the fitness function to avoid the premature phenomenon in the initial iteration stage of algorithm. The experimental results show that compared with several state-of-the-art optimization algorithms, the proposed algorithm is significantly superior and can always generate the optimal flight route in complicated environments.

Optimal routing, aborting and hitting strategies of UAVs executing hitting the targets considering the defense range of targets

This paper is an attempt to investigate optimal routing, aborting and hitting strategies of Unmanned Aerial Vehicles (UAVs) executing hitting tasks. Several factors such as time window and a certain circular defense range of each target, the optimal bomb and fuel loads are considered in the UAVs routing plan in order to better assign targets to each UAV and determine the hitting order. It is assumed that UAVs may be attacked by random shocks when they are within the target's defense range, so each UAV can choose to abort its task according to the number of targets that the UAV has hit and the number of shocks that UAV has suffered in order to reduce the probability of UAVs being destroyed. In addition, we try to accomplish two objectives simultaneously with meeting the fuel requirements and increasing the probability of target being destroyed by optimizing the load of fuel and bomb. The tabu search algorithm is adopted in the optimization of UAV routing, aborting and hitting strategies, and numerical experiments are presented to illustrate the performance of the algorithm.

Reactive UAV Fleet’s Mission Planning in Highly Dynamic and Unpredictable Environments

Unmanned aerial vehicles (UAVs) create an interesting alternative for establishing more sustainable urban freight deliveries. The substitution of traditional trucks in the last-mile distribution by a UAV fleet can contribute to urban sustainability by reducing air pollution and increasing urban freight efficiency. This paper presents a novel approach to the joint proactive and reactive planning of deliveries by a UAV fleet. We develop a receding horizon-based approach to reactive, online planning for the UAV fleet’s mission. We considered the delivery of goods to spatially dispersed customers over an assumed time horizon. Forecasted weather changes affect the energy consumption of UAVs and limit their range. Therefore, consideration should be given to plans for follow-up tasks, previously unmet needs, and predictions of disturbances over a moving time horizon. We propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. We implement a constraint programming paradigm, which is well suited to cope with the nonlinearity of the system’s characteristics. The proposed approach to online reactive UAV routing is evaluated in several instances. The computational experiments have shown that the developed model is capable of providing feasible plans for a UAV fleet’s mission that are robust to changes in weather and customer’s orders.

Joint traffic-aware UAV placement and predictive routing for aerial networks

Aerial networks, composed of Unmanned Aerial Vehicles (UAVs) acting as Wi-Fi access points or cellular base stations, are emerging as an interesting solution to provide on-demand wireless connectivity to users, when there is no network infrastructure available, or to enhance the network capacity. This article proposes a traffic-aware topology control solution for aerial networks that holistically combines the placement of UAVs with a predictive and centralized routing protocol. The synergy created by the combination of the UAV placement and routing solutions allows the aerial network to seamlessly update its topology according to the users’ traffic demand, whilst minimizing the disruption caused by the movement of the UAVs. As a result, the Quality of Service (QoS) provided to the users is improved. The components of the proposed solution are described and evaluated in this article by means of simulation and an experimental testbed. The results show that the QoS provided to the users is significantly improved when compared to the corresponding baseline solutions.

UAV routing with genetic algorithm based matheuristic for border security missions

In recent years, Unmanned Aerial Vehicles (UAVs) are a good alternative for the problem of ensuring the security of the borders of the countries. UAVs are preferred because of their speed, ease of use, being able to observe many points at the same time, and being more cost-effective in total compared to other security tools. This study is dealt with the problem of the use of UAVs for the security of the Turkey-Syria borderline which becomes sensitive in recent years and the problem is modeled as a UAV routing problem. To solve the problem, a Genetic Algorithm Based Matheuristic (GABM) approach has been developed and 12 scenarios have been created covering the departure bases, daily patrol numbers, and ranges of UAVs. GABM finds the minimum number of UAVs to use in scenarios with the help of a GA run first and tries to find the optimal routes for these UAVs. If GABM can find an optimal route for the determined UAV number, it decreases the UAV number and tries to solve the problem again. GABM proposes a hybrid approach in which a metaheuristic with a mathematical model works together and the metaheuristic sets an upper limit for the number of UAVs in the model. In computational studies, when compared GA with GABM it is seen that GABM has obtained good results and decreased the utilized number of UAVs (up to 400%) and their flight distances (up to 85.99%) for the problem in very short CPU times (max. 122.17 s. for GA and max. 46.39 s. for GABM in addition to GA).

3D Determination of Message Collection and Delivery Locations for UAV-Enabled Disaster Recovery Networks

When a large-scale disaster happens, critical infrastructure is destroyed, and many people are displaced. Unmanned aerial vehicle (UAV)-enabled disaster recovery networks can be used to support people in disaster-hit areas. However, determining UAV routes is critical to communicate with refugees. In this work, we propose a method to determine UAV locations to collect and deliver messages for UAV-enabled disaster recovery networks. Our method involves two stages: the received signal strength sensing stage and the message collection and delivery stage. We theoretically analyzed the computational complexity in terms of the number of steps and consequently evaluated the performance of our method using experimental data obtained from rural, suburban, and urban areas.

Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

China is a country with frequent earthquake disasters. After the occurrence of earthquake disasters, the key to disaster monitoring and rescue is to quickly obtain images of postdisaster areas. Unmanned aerial vehicle (UAV) path planning is the core of multi-UAV cooperative control. With the increasing popularity of UAVs, people with more complex living environments contact with UAVs more frequently, which also poses a challenge to the overall control of UAVs, making single UAV and even multi-UAV cooperative path planning become a hot research issue in recent years. The complexity of communication between aircraft in three-dimensional flight space and multidegree of freedom navigation makes multi-UAV cooperation more challenging. According to the research results at home and abroad, this paper takes multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm as research methods. Based on virtual reality technology, by comparing the advantages and disadvantages of several algorithms, the research model of path optimization is established, and a multitarget detection method based on virtual reality technology is established. Through the analysis and improvement of multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm, the path optimization problem of UAV after an earthquake based on virtual reality technology is studied. The results show that, compared with the previous research models, the overall optimization efficiency of UAV route is improved by 15%, which is more practical.