Fleet Of Unmanned Aerial Vehicles Research Articles

A two-time-level model for mission and flight planning of an inhomogeneous fleet of unmanned aerial vehicles

We consider the mission and flight planning problem for an inhomogeneous fleet of unmanned aerial vehicles (UAVs). Therein, the mission planning problem of assigning targets to a fleet of UAVs and the flight planning problem of finding optimal flight trajectories between a given set of waypoints are combined into one model and solved simultaneously. Thus, trajectories of an inhomogeneous fleet of UAVs have to be specified such that the sum of waypoint-related scores is maximized, considering technical and environmental constraints. Several aspects of an existing basic model are expanded to achieve a more detailed solution. A two-level time grid approach is presented to smooth the computed trajectories. The three-dimensional mission area can contain convex-shaped restricted airspaces and convex subareas where wind affects the flight trajectories. Furthermore, the flight dynamics are related to the mass change, due to fuel consumption, and the operating range of every UAV is altitude-dependent. A class of benchmark instances for collision avoidance is adapted and expanded to fit our model and we prove an upper bound on its objective value. Finally, the presented features and results are tested and discussed on several test instances using GUROBI as a state-of-the-art numerical solver.

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Unmanned aerial vehicles (UAVs) have the potential to make a significant impact in a range of scenarios where it is too risky or too costly to rely on human labour. Fleets of autonomous UAVs, which complete tasks collaboratively while managing their basic flight and related tasks independently, present further opportunities along with research and regulatory challenges. Improvements in UAV construction and components, along with developments in embedded computing hardware, communication mechanisms and sensors which may be mounted on-board a UAV, are nearing the point where commercial deployment of fleets of autonomous UAVs will be technically possible. To fulfil this potential, UAVs will need to operate safely and reliably in complex and potentially dynamically changing environments with path-planning, obstacle sensing and collision avoidance paramount. This survey presents an original environment complexity classification and critically analyses the current state of the art in relation to UAV path-planning approaches. Moreover, it highlights the existing challenges in environment complexity modelling and representation, as well as path-planning approaches, and outlines open research questions together with future directions.

Energy-Efficient Information Placement and Delivery Using UAVs

This article focuses on minimizing the energy consumption of a fleet of unmanned aerial vehicles (UAVs) disseminating information to a set of Internet of Things devices. In the considered scenario, each device wants to download a subset of files from a library of files. Considering the storage capacity of the UAVs, a framework is provided that minimizes energy consumption by optimally selecting the contributing UAVs, placing files, and planning the trajectory of each contributing UAV. In this framework, a combinatorial optimization problem is formulated, which is hard to solve directly for a practical number of devices, files, and/or UAVs. In order to tackle this challenge, we develop three solution approaches, namely, a multichromosome genetic algorithm (GA), a hybrid genetic-ant colony algorithm, and a GA with heuristic file placement. Results show that the proposed solution approaches minimize the total energy consumption and provide near-optimal solutions. Results also illustrate that the proposed framework optimizes the number of UAVs participating in the information delivery mission.

Determining Reliable Solutions for the Team Orienteering Problem with Probabilistic Delays

In the team orienteering problem, a fixed fleet of vehicles departs from an origin depot towards a destination, and each vehicle has to visit nodes along its route in order to collect rewards. Typically, the maximum distance that each vehicle can cover is limited. Alternatively, there is a threshold for the maximum time a vehicle can employ before reaching its destination. Due to this driving range constraint, not all potential nodes offering rewards can be visited. Hence, the typical goal is to maximize the total reward collected without exceeding the vehicle’s capacity. The TOP can be used to model operations related to fleets of unmanned aerial vehicles, road electric vehicles with limited driving range, or ride-sharing operations in which the vehicle has to reach its destination on or before a certain deadline. However, in some realistic scenarios, travel times are better modeled as random variables, which introduce additional challenges into the problem. This paper analyzes a stochastic version of the team orienteering problem in which random delays are considered. Being a stochastic environment, we are interested in generating solutions with a high expected reward that, at the same time, are highly reliable (i.e., offer a high probability of not suffering any route delay larger than a user-defined threshold). In order to tackle this stochastic optimization problem, which contains a probabilistic constraint on the random delays, we propose an extended simheuristic algorithm that also employs concepts from reliability analysis.

AMon: A domain-specific language and framework for adaptive monitoring of Cyber–Physical Systems

Cyber–Physical Systems (CPS) are increasingly used in safety–critical scenarios where ensuring their correct behavior at runtime becomes a crucial task. Therefore, the behavior of the CPS needs to be monitored at runtime so that violations of requirements can be detected. With the inception of edge devices that facilitate runtime analysis at the edge and the increasingly diverse environments that CPS operate in, flexible monitoring approaches are needed that consider the data that needs to be monitored and the analyses performed on that data. In this paper, we propose AMon, a flexible adaptive monitoring framework that supports the specification and validation of monitoring adaptation rules, using a domain-specific language. Based on these rules, AMon automatically generates code for direct deployment onto devices. We evaluated AMon by applying it to TurtleBot Robots and a fleet of Unmanned Aerial Vehicles. Furthermore, we conducted a user study assessing the understandability and ease of use of our language. Results show that creating multiple adaptation rules with our DSL is feasible with minimal effort, and that adaptive monitoring can reduce the amount of runtime data transmitted from the edge device according to the current state of the system and its monitoring needs.

UAV Fleet as a Dependable Service for Smart Cities: Model-Based Assessment and Application

The paper suggests a model-based approach to assessment and choice of parameters of unmanned aerial vehicle (UAV) fleets applied as one of the main services for Smart Cities and recommendations to assure their dependability. The principles of building and modeling a UAV Fleet as a Dependable Service (UAVFaaDS) for Smart Cities are formulated. Dependability issues for UAVFaaDS including a taxonomy of UAVF failures caused by equipment faults and attacks on assets were specified. The main results cover methodology, classification of UAVFaaDS models as models of queuing systems, and a set of queueing theory-based models for assessment of UAVFaaDS performance, and availability allowing for analysis and choice of fleet parameters. The efficiency of UAVFaaDS is assessed by the probability of successful delivery of services. The proposed modeling base and algorithms provide a choice of appropriate models for analysis and synthesis of UAVFaaDS, grounding of parameters of UAV fleets considering operation modes, and maintenance policy. The application of the developed models and algorithms during the synthesis of UAVFaaDS allows choosing the appropriate parameters of the fleet and ensuring the dependability of services, as well as service of orders with a probability of 0.9–0.99 depending on the requirements. Two cases of UAVFaaDS application for delivery of medicines in normal and emergence modes, models’ development, and recommendations for their utilization are discussed.

Dynamic Path Planning for Unmanned Aerial Vehicles Under Deadline and Sector Capacity Constraints

The US NationalAirspace System is currentlyoperating at a level close to its maximum potential. The limitation comes from the workload demand on the air traffic controllers. Currently, the air traffic flow management is based on the flight path requests by the airline operators, whereas the minimum separation assurance between flights is handled strategically by air traffic control personnel. In this paper, we propose a scalable framework that allows path planning for a large number of unmanned aerial vehicles (UAVs) taking into account the deadline and weather constraints. Our proposed solution has a polynomial-time computational complexity that is also verified by measuringthe runtime for typical workloads. We further demonstrate that the proposed framework is able to route 80% of the workloads while not exceeding the sector capacity constraints, even under dynamic weather conditions. Due to low computational complexity, our framework is suitable for a fleet of UAVs where decentralizing the routing process limits the workload demand on the airtraffic personnel.

The Rise of UAV Fleet Technologies for Emergency Wireless Communications in Harsh Environments

For unforeseen emergencies, such as natural disasters and pandemic events, it is highly demanded to cope with the explosive growth of mobile data traffic in extremely critical environments. An unmanned aerial vehicle (UAV) fleet is an effective way to facilitate the emergency wireless communication network (EcoNet). In this article, a multi-tier heterogeneous UAV network (MuHun), with different UAV fleets at different altitudes, is proposed to flexibly serve various emergencies. We refresh the key performance indicators of full coverage, network capacity, low latency, and energy efficiency in harsh environments. Then we present the special challenges regarding the shadowing-dominated complex channel model, energy supply limited short endurance, various communication mechanisms' coexistence, and a communication island for underground users in UAV-based EcoNet, followed by the MuHun-based EcoNet architecture and its advantages. Furthermore, some potential solutions such as the new hybrid-channel adapted resource allocation, reconfigurable intelligent surface assisted UAV communications, competitive heterogenous networks, and magnetic induction based air-to-ground/underground communications are discussed to effectively achieve full coverage, high capacity, high energy efficiency, and diverse qualities of services for EcoNets in harsh environments.

Systemic Performance Analysis on Zoning for Unmanned Aerial Vehicle-Based Service Delivery

A zoning approach that divides an area of interest into multiple sub-areas can be a systemic and strategic solution to safely deploy a fleet of unmanned aerial vehicles (UAVs) for package delivery services. Following the zoning approach, a UAV can be assigned to one of the sub-areas, taking sole ownership and responsibility of the sub-area. As a result, the need for collision avoidance between units and the complexity of relevant operational activities can be minimized, ensuring both safe and reliable execution of the tasks. Given that the zoning approach involves the demand-server allocation decision, the service quality to customers can also be improved by performing the zoning properly. To illuminate the benefits of the zoning approach to UAV operations from a systemic perspective, this study applies clustering techniques to derive zoning solutions under different scenarios and examines the performance of the solutions using a simulation model. The simulation results demonstrate that the zoning approach can improve the safety of UAV operations, as well as the quality of service to demands.

Heuristic-driven strategy for boosting aerial photography with multi-UAV-aided Internet-of-Things platforms

Unmanned Aerial Vehicles (UAVs) are gaining much attractiveness due to the emerging 5G, Internet of things applications and the advances in artificial intelligence. Their application fields encompass both civil and military domains. UAVs can ubiquitously supply many Internet-of-things-driven services; as such, they can be configured into airborne networks to provide flexible aerial views, which is essential for photography and videography-based applications like 3D mapping and real-time monitoring. However, many research challenges need to be tackled to facilitate the deployment of such promising applications. This paper addresses a non-convex NP-hard problem of deploying a fleet of UAVs equipped with rotating gimbal-mounted cameras over large-scaled terrains. The problem is heuristically solved in two phases. Firstly, we introduce a fast parallel multi-verse swarm optimization algorithm. A hybrid multi-objective heuristic coalescing two relevant concepts of stochastic optimization: (1) Improved Multi-Objective Particle Swarm Optimization; (2) Improved Multi-Objective Multi-verse Optimization. This heuristic holds several algorithmic tweaks, such as Pareto-based population splitting, Taguchi-based parameters tuning, adaptive mutation, and is used to derive the most near-optimal hovering coordinates of UAVs under two customized objective functions. Secondly, we adopt an efficient gimbal-based rotation and synchronization strategy allowing the UAVs to rotate their cameras resourcefully in four cardinal directions to boost the aerial photographing coverage with few maneuvers. The claimed performance is rigorously endorsed with intensive simulations and comparative analyses (e.g., analysis of variances, Wilcoxon test, performance index) against twenty multi-objective bio-inspired heuristics. The results show that our approach has the upper hand in terms of efficiency and accuracy.

Real-time wildfire monitoring with a fleet of UAVs

This paper introduces a wildfire monitoring system based on a fleet of Unmanned Aerial Vehicles (UAVs) to provide firefighters with precise and up-to-date information about a propagating wildfire, so that they can devise efficient suppression actions. We present an approach to plan trajectories for a fleet of fixed-wing UAVs to observe a wildfire evolving over time by tailoring the Variable Neighborhood Search metaheuristic to the problem characteristics. Realistic models of the terrain, of the fire propagation process, and of the UAVs are exploited, together with a model of the wind, to predict wildfire spread and plan accordingly the UAVs motions. Algorithms and models are integrated within a software architecture allowing tests with real and simulated UAVs flying over synthetic wildfires. Results of a mixed-reality test campaign show the ability of the proposed system to effectively map wildfire propagation.

Bipartite Cooperative Coevolution for Energy-Aware Coverage Path Planning of UAVs

The coverage path planning of unmanned aerial vehicles (UAVs) is a complex optimization problem in practice, especially for those involving multiple target areas. It is challenging to comprehensively plan the inter-area visiting order and the intra-area coverage paths simultaneously. Due to the battery limitation, usually the task can hardly be finished by a single UAV, and instead a fleet of UAVs are required. In this article, we first formulate an energy-aware multi-UAV multi-area coverage path planning (EM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CPP) model, in order to characterize the practical path planning requirements of UAVs in complex conditions. Subsequently, to accomplish the optimization task, we propose a bipartite cooperative coevolution (BiCC) algorithm that coevolves an inter-area path planning and an intra-area path planning components to obtain good solutions. The basic operators in BiCC, such as the initialization and the reproduction operators, are tailored for the task of EM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CPP. Besides, we also develop a fast heuristic algorithm for EM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CPP, which is able to produce approximate solutions in a short time. Simulations on real-world datasets validate the good performance of the proposed methods.

A Realistic Model to Support Rescue Operations After an Earthquake via UAVs

In this paper, we consider the problem of completely flying over an area just hit by an earthquake with a fleet of Unmanned Aerial Vehicles (UAVs) to opportunely direct rescue teams. The cooperation between UAVs ensures that the search for possible survivors can be faster and more effective than the solutions currently implemented by civil protection. To study this scenario, we introduce the Cover by Multitrips with Priorities (CMP) problem, which tries to keep into account all the main real-life issues connected to the flight and coordination of the UAVs. We conduct a theoretical study to estimate the best number of UAVs and additional batteries, to give indications to the organization that leads the rescue teams to be able to guarantee rapid and effective rescue. Finally, based on some theoretical considerations, we propose some heuristics that tackle the problem of flying over the whole area with a fleet of UAVs in the shortest possible time. Simulations show that they work efficiently in both the proposed scenarios and provide better performance than previous solutions once they are arranged to work in our scenarios. The main advantages of our approach w.r.t. the current drone-based solutions used by the civil defense are that UAVs do not need drivers so the time of all available rescue workers can be invested in doing something else. In our model, we take into account that some sites (<i>e.g.</i> buildings with a high fire risk or schools and hospitals) have a higher priority and must be inspected first, and the possibility that UAVs can make a decision based on what they detect. Finally, our approach allows UAVs to collaborate so that the same sites will be flown over exactly once in order to speed up the rescue mission.

3D Map Reconstruction of an Orchard using an Angle-Aware Covering Control Strategy

In the last years, unmanned aerial vehicles are becoming a reality in the context of precision agriculture, mainly for monitoring, patrolling and remote sensing tasks, but also for 3D map reconstruction. In this paper, we present an innovative approach where a fleet of unmanned aerial vehicles is exploited to perform remote sensing tasks over an apple orchard for reconstructing a 3D map of the field, formulating the covering control problem to combine the position of a monitoring target and the viewing angle. Moreover, the objective function of the controller is defined by an importance index, which has been computed from a multi-spectral map of the field, obtained by a preliminary flight, using a semantic interpretation scheme based on a convolutional neural network. This objective function is then updated according to the history of the past coverage states, thus allowing the drones to take situation-adaptive actions. The effectiveness of the proposed covering control strategy has been validated through simulations on a Robot Operating System.

Comparison of exact and approximate approaches to UAVs mission contingency planning in dynamic environments.

This paper presents a novel approach to the joint proactive and reactive planning of deliveries by an unmanned aerial vehicle (UAV) fleet. We develop a receding horizon-based approach to contingency planning for the UAV fleet's mission. We considered the delivery of goods to spatially dispersed customers, over an assumed time horizon. In order to take into account forecasted weather changes that affect the energy consumption of UAVs and limit their range, we propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. These rules are used in the course of designing the contingency plans related to the need to implement an emergency return of the UAV to the base or handling of ad hoc ordered deliveries. Due to the nonlinearity of the environment's characteristics, both constraint programming and genetic algorithm paradigms have been implemented. Because of the NP-difficult nature of the considered planning problem, conditions have been developed that allow for the acceleration of calculations. The multiple computer experiments carried out allow for comparison representatives of the approximate and exact methods so as to judge which approach is faster for which size of the selected instance of the UAV mission planning problem.

Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.

Robust Observer-based Leader-Following Consensus for Multi-agent Systems

This paper presents a robust observer-based leader-following consensus control for multi-agent systems subject to external disturbances. The stability and robustness to external disturbances are addressed through the Lyapunov approach and an optimal H criterion. It is shown that the design conditions for a correct convergence of the estimated states and consensus control can be expressed in a set of linear matrix inequalities whose solution allows computing the observer and controller gain matrices. In order to show the effectiveness of the proposed strategy, numerical examples are carried out to solve the formation control problem of a fleet of unmanned aerial vehicles (UAVs) under the effect of wind turbulence.

Event-triggered leader-following formation control for multi-agent systems under communication faults: Application to a fleet of unmanned aerial vehicles

The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems (MASs) under communication faults. All the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the agents. Linear matrix inequalities (LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization error. Based on the closed-loop system, an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy consumption. The proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles (UAVs) under communication faults. A comparison between a state-of-the-art technique and the proposed technique has been provided, demonstrating the performance improvement brought by the proposed approach.

A distributed antenna orientation solution for optimizing communications in a fleet of UAVs

In this article, we describe how a fleet of unmanned aerial vehicles (UAVs) can optimize communication performances by having its members independently change their orientations. This distributed solution, based on a hill-climbing approach, relies on information available locally at each node, namely the reception power of the received frames. The solution is evaluated using the ns–3 network simulator, whose source code is modified to be able to deal with non-isotropic antennas in the context of Wi-Fi networks, as well as simulate angular movements. As isotropic antennas are only theoretical objects, this step is mandatory in order to increase the realism of network simulations. The results, obtained using realistic antenna models, highlight that controlled mobility, in particular controlled orientation needs to be considered in order for UAV networks to provide better performances.

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.