Fleet Of Unmanned Aerial Vehicles Research Articles

Key wireless communication technologies to support traffic management systems of unmanned aerial vehicles for civil application (review of foreign literature)

Not less than one hundred thousand Unmanned Aerial Vehicles (UAVs) are expected to perform flights simultaneously in Russia by 2035. The UAV fleet capacity triggers the development of the systems for informational support, operating control and management of UAV flights (Unmanned Aircraft System Traffic Management (UTM) systems) similar to that one already operating in manned aviation. The challenges arising in the sphere of civil aviation cannot be solved without wireless communication. The goals of this article are as follows: 1) familiarization of communication experts with the latest scientific developments of unmanned aerial technologies 2) description of the telecommunication-related problems of extensive systems of UAV control encountered by development engineers. In this article a schematic architecture and main functions of UTM systems are described as well as the examples of their implementation. Special emphasis is put on enhancing flight safety by means of a rational choice of communication technologies to manage conflicts (Conflict Management) known as "collision avoidance". The article analyzes the application of a wide range of wireless technologies ranging from Wi-Fi and Automatic Dependent Surveillance Broadcast (ADS-B) to 5G cellular networks as well as cell-free networks contributing to the development of 6G communication networks. As a result of the analysis, a list of promising research trends at the intersection of the fields of wireless communication and UAVs for civil application is made.

Federated Learning and Autonomous UAVs for Hazardous Zone Detection and AQI Prediction in IoT Environment

Air pollution monitoring, finding the hazardous zone, and future air quality predictions have recently become a significant issue for many researchers. With the adverse effect of low air quality on human health, it has become necessary for predicting the air quality index (AQI) accurately and on time. The unmanned aerial vehicle (UAV) can collect air quality data with high spatial and temporal resolutions. Using a fleet of UAVs could be considered a good option. In the proposed work, we implement a distributed federated learning (FL) algorithm within a UAV swarm that collects air quality data using built-in sensors. A scheme for finding the area with the highest AQI value is proposed using swarm intelligence. The collected data are then fed to a CNN-LSTM model to predict the AQI. The trained local model is sent to the central server, and the server aggregates the received models from UAVs in the swarm. A global model is created and is transmitted to the UAV swarm again in the next iteration. The proposed architecture is compared with other time-series models. The results show that the proposed model predicts AQI daily with a minimal error rate on a real-time data set from Delhi.

Exploiting a fleet of UAVs for monitoring and data acquisition of a distributed sensor network

This study proposes an efficient data collection strategy exploiting a team of unmanned aerial vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture, and renewable energy applications. The study develops a collaborative mission planning system that enables a team of UAVs to conduct and complete the mission of sensors’ data collection collaboratively while considering existing constrains of the UAV payload and battery capacity. The proposed mission planner system employs the differential evolution optimization algorithm enabling UAVs to maximize the number of visited sensor nodes given the priority of the sensors and avoiding redundant collection of sensors’ data. The proposed mission planner is evaluated through extensive simulation and comparative analysis. The simulation results confirm the effectiveness and fidelity of the proposed mission planner to be used for the distributed sensor network monitoring and data collection.

A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs

This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of unmanned aerial vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions between vehicles and other obstacles. This specific task is suitable for surveillance applications, where the uniform distribution of the fleet in the map permits them to reach any position on the map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron has a local cost and has a local connection only with neighbor neurons. The cost of each neuron influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We introduce several controls and precautions to minimize the risk of collisions and optimize coverage planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed approach to manage and coordinate the fleet providing the full coverage of the map in every tested scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map.

Coordinated Path-Following Control of Fixed-Wing Unmanned Aerial Vehicles

This article investigates the problem of coordinated path following for fixed-wing unmanned aerial vehicles (UAVs) with speed constraints in the two-dimensional plane. The objective is to steer a fleet of UAVs along the path(s) while achieving the desired sequenced inter-UAV arc distance. In contrast to the previous coordinated path-following studies, we are able through our proposed hybrid control law to deal with the forward speed and the angular speed constraints of fixed-wing UAVs. More specifically, the hybrid control law makes all the UAVs work at two different levels: 1) those UAVs whose path-following errors are within an invariant set (i.e., the designed coordination set) work at the coordination level and 2) the other UAVs work at the single-agent level. At the coordination level, we prove that even with speed constraints, the proposed control law can make sure the path-following errors reduce to zero, while the inter-UAV arc distances converge to the desired value. At the single-agent level, analysis for the path-following error entering the coordination set is provided. We develop a hardware-in-the-loop simulation testbed of the multi-UAV system by using actual autopilots and the X-Plane simulator. The effectiveness of the proposed approach is corroborated with both numerical simulation and the testbed.

Robust Formation Control Based on Leader-Following Consensus in Multi-Agent Systems With Faults in the Information Exchange: Application in a Fleet of Unmanned Aerial Vehicles

In this paper, a robust leader-following consensus protocol for multi-agent systems (MASs) subject to faults in the information exchange and disturbance is presented. The problem under consideration is to guarantee the convergence of the agent trajectories to a leader agent when all the agent followers are under faults in the information exchange as smooth time-varying delays and disturbances. The main contribution in this paper is the design of a robust leader-following control through the Lyapunov approach and an optimal \mathscr H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> criterion such that all agents follow a virtual leader agent despite faults in the information exchange and disturbances. Linear matrix inequalities (LMIs)-based conditions are obtained whose solution allows computing the robust controller gain. In order to show the effectiveness of the proposed approach, numerical examples are carried out comparing a state-of-the-art approach and the proposed strategy in a fleet of unmanned aerial vehicles (UAVs) subject to wind turbulence which are shown to achieve the formation control.

A multi-agent semi-cooperative unmanned air traffic management model with separation assurance

This paper presents an air traffic management framework to enable multiple fleets of unmanned aerial vehicles to traverse dense, omni-directional air traffic safely and efficiently. The main challenge addressed here is separation assurance in the absence of full coordination and communication. In this framework, each fleet is independently managed by a routing agent, which progressively plans the non-overlapping move-ahead corridors for vehicles in the fleet by solving a nonlinear optimization model. The model is artfully designed so that agents of different fleets need not engage in complicated multilateral communications or make guesses about external vehicles’ flight intents to maintain effective inter-vehicle separation. For a complex routing problem, the framework is able to support centralized fleet routing, decentralized vehicle self-routing, and any other agent-vehicle configuration in between, allowing for customized trade-off between response time and traffic efficiency. Innovative algorithmic enhancements for solving the agent’s nonconvex routing problem are prescribed with detailed annotation. The effectiveness and noteworthy properties of the framework are demonstrated by several simulation experiments.

Minimizing the Longest Tour Time Among a Fleet of UAVs for Disaster Area Surveillance

In this paper, we study the employment of multiple Unmanned Aerial Vehicles (UAVs) to monitor Points of Interests (PoIs) in a disaster area, e.g., collapsed buildings after an earthquake, where the UAVs can take photos and videos for the people trapped at PoIs, because such valuable information is imperative to make rescue decisions. Unlike most existing studies that ignored the monitoring time of PoIs and simply minimized the longest flying distance among the UAVs, we observe that it takes time to monitor the PoIs. Then, it is possible that the flying distance of a UAV in its flying tour may not be too long, the tour however contains many densely-located PoIs. Therefore, it will take a very long time for the UAV to monitor the PoIs in its tour. In this paper, we first formulate a problem of finding flying tours for <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> given UAVs to collaboratively monitor PoIs in a disaster area, such that the maximum spent time of the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> UAVs among their tours is minimized, where the spent time of a UAV in its tour consists of the flying time and the PoI monitoring time. We then propose a novel <inline-formula><tex-math notation="LaTeX">$5\frac{1}{3}$</tex-math></inline-formula> -approximation algorithm for the problem, improving the best approximation ratio 6 so far for the problem of minimizing the longest flying distance among the UAVs. In addition, we extend the proposed algorithm to the case that each UAV may not be able to monitor all PoIs assigned to it, due to its limited maximum flying time (e.g., 30 minutes), and the UAV must return to its depot to replace its battery. We finally evaluate the performance of the proposed algorithms via simulation environments, and experimental results show that the proposed algorithms are very promising. Especially, the maximum spent times of the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> UAVs in their tours by the proposed algorithms are up to 30 percent shorter than those by existing algorithms. In addition, the empirical approximation ratios of the proposed algorithms are no more than 2.4, which are much smaller than their theoretical approximation ratios that are at least <inline-formula><tex-math notation="LaTeX">$5\frac{1}{3}$</tex-math></inline-formula> .

Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants

This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection.

The multiple flying sidekicks traveling salesman problem with variable drone speeds

This research considers unmanned aerial vehicles (UAVs) that may travel at varying speeds in a last-mile delivery system involving a single truck and a fleet of UAVs. In existing truck-and-UAV delivery models that assume constant or unlimited UAV endurance, the natural conclusion is that operating UAVs at higher speeds will either decrease, or have no adverse effect on, total delivery times. However, in reality, UAV power consumption is a nonlinear function of both speed and parcel weight; flying at high speeds can dramatically reduce flight ranges, thus limiting the effectiveness of these logistics systems. This paper addresses the tradeoffs between speed and range in a new variant of the combined truck/UAV delivery problem in which UAV speeds are treated as decision variables. A three-phased algorithm is provided that dynamically adjusts UAV speeds to achieve superior performance, with the goal of minimizing the total delivery time (or makespan). Results indicate that significant time savings can be achieved by operating UAVs at variable speeds. Furthermore, under certain conditions, optimizing UAV speeds also results in shorter truck travel distances, reduced UAV energy consumption per trip, and less UAV loitering while waiting to rendezvous with the truck.

Fly Slower, Deliver Faster: the Multiple Flying Sidekicks Traveling Salesman Problem with Variable Drone Speeds

This research considers unmanned aerial vehicles (UAVs) that may travel at varying speeds in a last-mile delivery system involving a single truck and a fleet of UAVs. In existing truck-and-UAV delivery models that assume constant or unlimited UAV endurance, the natural conclusion is that operating UAVs at higher speeds will either decrease, or have no adverse effect on, total delivery times. However, in reality, UAV power consumption is a nonlinear function of both speed and parcel weight; flying at high speeds can dramatically reduce flight ranges, thus limiting the effectiveness of these logistics systems. This paper addresses the tradeoffs between speed and range in a new variant of the combined truck/UAV delivery problem in which UAV speeds are treated as decision variables. A three-phased algorithm is provided that dynamically adjusts UAV speeds to achieve superior performance, with the goal of minimizing the total delivery time (or makespan). Results indicate that significant time savings can be achieved by operating UAVs at variable speeds. Furthermore, under certain conditions, optimizing UAV speeds also results in shorter truck travel distances, reduced UAV energy consumption per trip, and less UAV loitering while waiting to rendezvous with the truck.

Mission Planning for Multiple UAVs in a Wind Field with Flight Time Constraints

The use of unmanned aerial vehicles (UAVs) has become common in a wide range of applications due to their agility and maneuverability. UAV mission planning is a challenging task, especially when dealing with multiple UAVs. In this paper, we consider a typical mission planning problem in which a fleet of UAVs must visit a set of fixed waypoints. Each waypoint has an associated reward that represents its relative importance. The problem consists in assigning a UAV to each waypoint, considering that each UAV has a flight time constraint. Also, we propose a model to solve the mission planning problem for multiple UAVs in the presence of a wind field. The goal is to maximize the total reward collected and minimize total flight time. Numerical experiments are carried out to illustrate the application of the proposed model in different instances of the multiple UAV mission planning problem. The results show that, when wind field is not taken into account, the error in the estimated total flight time required for the UAVs to complete their missions is about 13%. Also, if UAVs have flight time constraints, neglecting the wind field may cause the loss of some UAVs. The solutions provided by the proposed model allow the UAVs to complete their missions and return to the base safely.

A priori assessment of a smart-navigated unmanned aerial vehicle disaster cargo fleet

The United Nations Office for the Coordination of Humanitarian Affairs has asserted that risks in deployment of unmanned aerial vehicles (UAVs) within disaster response must be reduced by careful development of best-practice standards before implementing such systems. With recent humanitarian field tests of cargo UAVs as indication that implementation may soon become reality, a priori assessment of a smart-navigated (autonomous) UAV disaster cargo fleet via simulation modeling and analysis is vital to the best-practice development process. Logistical problems with ground transport of relief supplies in Puerto Rico after Hurricane Maria (2017) pose a compelling use scenario for UAV disaster cargo delivery. In this context, we introduce a General Purpose Assessment Model (GPAM) that can estimate the potential effectiveness of a cargo UAV fleet for any given response region. We evaluate this model using the following standards: (i) realistic specifications; (ii) stable output for various realistic specifications; and (iii) support of humanitarian goals. To this end, we discuss data from humanitarian cargo delivery field tests and feedback from practitioners, perform sensitivity analyses, and demonstrate the advantage of using humanitarian rather than geographic distance in making fleet delivery assignments. We conclude with several major challenges faced by those who wish to implement smart-navigated UAV cargo fleets in disaster response, and the need for further GPAM development. This paper proposes the GPAM as a useful simulation tool to encourage and guide steps toward humanitarian use of UAVs for cargo delivery. The model’s flexibility can allow organizations to quickly and effectively determine how best to respond to disasters.

Biologically Based Control of a Fleet of Unmanned Aerial Vehicles Facing Multiple Threats

This paper addresses a set of multiagent, unmanned, aerial vehicles (UAVs) in a mission within a threat-prone environment. Each UAV is considered as a nonholonomic, nonlinear model moving in a two dimensional space. The system is composed of a fleet of UAVs, competing UAVs and a target. The approach proposed in this paper is based on a biological model representing collective behavior in predator-prey systems. The fleet of UAV’s (prey) is moving cohesively towards a target and they can come under attack from competing UAV’s (predators) which are also expected to avoid a certain area around the target, treating it as an obstacle that they have to avoid. Prey and predators are expected to obey certain social behaviors within their respective species that include coherent motion, separation to avoid collision, and alignment. The prey are also connected through an adaptable network. During attacks, this networked population of prey could be divided into several small groups that are still connected and naturally observe the same social behavior. To identify these groups and their members, the density-based algorithm DBSCAN is used. The aim of this work is to use this biologically inspired model along with a robust feedback linearization controller to achieve both target pursuance and effective evasion from two predators. Simulation results demonstrate the different aspects and features provided in the proposed approach.

A Quadral-Fuzzy Control Approach to Flight Formation by a Fleet of Unmanned Aerial Vehicles

This paper addresses the control of a fleet of unmanned aerial systems (UAVs), termed as drones, for flight formation problems. Getting drones to fly in formation is a relevant problem to be solved when cooperative cargo transportation is desired. A general approach for this problem considers the coordination of a fleet of UAVs, by fusing all information coming from several individual sensors posed on each UAVs. However, this approach induces a high cost as every UAV should have its advanced perception system. As an alternative, this paper proposes the use of a single perception system by a fleet composed of several elementary drones (workers) with primitive low-cost sensors and a leader drone carrying a 3D perception source. We propose a Quadral-Fuzzy approach to ensure that all drones fly in formation and will not collide with each other or with environment obstacles. We also develop a new way to compute potential fields based on possibility fuzzy (fuzziness) measure with the focus of avoiding collisions between the drones. The proposed approach encompasses four high-coupled intelligent controllers that respectively control the leader and worker drones’ motion and implement obstacle and collision avoidance procedures. Simulation results using a fleet of four aerial drones are presented, showing the potential for solving usual problems to flights in formation, such as dodging obstacles, avoiding collisions between the drones, among others.

Set-theoretical model of a monitoring system for a potentially dangerous object using a multi-purpose fleet of unmanned aerial vehicles

Set-theoretical model of a monitoring system for a potentially dangerous object using a multi-purpose fleet of unmanned aerial vehicles

Modeling and Analysis of Data Harvesting Architecture Based on Unmanned Aerial Vehicles

This paper explores an emerging wireless Internet-of-things (IoT) architecture based on unmanned aerial vehicles (UAVs). We consider a network where a fleet of UAVs at a fixed altitude flies on planned trajectories and IoT devices on the ground are scheduled to transmit their data to the UAVs when the latter are nearby. In such a system, the UAVs’ motion triggers the uplink transmissions of the IoT devices. As a result, network performance is determined by the geometric and dynamic characteristics of the system. We propose a joint stationary model for UAVs and IoT devices and then evaluate the interference, the coverage probability, and the data rate of the typical UAV. To assess the harvesting capability of the proposed architecture, we derive a formula for the amount of data uploaded from each IoT device to a UAV. We also establish a linear relationship between the UAV coverage and the harvesting capability of the network, which provides insights into the design of the proposed harvesting scheme. In addition, we use our analytical results to numerically show that there exists a trade-off between the uploaded data and the size of the IoT scheduling window. Specifically, for a given UAV and IoT geometry, there exists an optimal scheduling window that maximizes the harvesting capability of the proposed network.

The multiple flying sidekicks traveling salesman problem: Parcel delivery with multiple drones

This paper considers a last-mile delivery system in which a delivery truck operates in coordination with a fleet of unmanned aerial vehicles (UAVs, or drones). Deploying UAVs from the truck enables customers located further from the depot to receive drone-based deliveries. The problem is first formulated as a mixed integer linear program (MILP). However, owing to the computational complexity of this problem, only trivially-sized problems may be solved directly via the MILP. Thus, a heuristic solution approach that consists of solving a sequence of three subproblems is proposed. Extensive numerical testing demonstrates that this approach effectively solves problems of practical size within reasonable runtimes. Additional analysis quantifies the potential time savings associated with employing multiple UAVs. The analysis also reveals that additional UAVs may have diminishing marginal returns. An analysis of five different endurance models demonstrates the effects of these models on UAV assignments. The model and heuristic also support anticipated future systems that feature automation for UAV launch and retrieval.

Optimal hub selection for rapid medical deliveries using unmanned aerial vehicles

Unmanned Aerial Vehicles (UAVs) are being increasingly deployed in humanitarian response operations. Beyond regulations, vehicle range and integration with the humanitarian supply chain inhibit their deployment. To address these issues, we present a novel bi-stage operational planning approach that consists of a trajectory optimisation algorithm (that considers multiple flight stages), and a hub selection-routing algorithm that incorporates a new battery management heuristic. We apply the algorithm to a hypothetical response mission in Taiwan after the Chi-Chi earthquake of 1999 considering mission duration and distribution fairness. Our analysis indicates that UAV fleets can be used to provide rapid relief to populations of 20,000 individuals in under 24 h. Additionally, the proposed methodology achieves significant reductions in mission duration and battery stock requirements with respect to conservative energy estimations and other heuristics.

Planetary Exploration With Robot Teams: Implementing Higher Autonomy With Swarm Intelligence

Since the beginning of space exploration, Mars and the moon have been examined via orbiters, landers, and rovers. More than 40 missions have targeted Mars, and over 100 have been sent to the moon. Space agencies continue to focus on developing novel strategies and technologies for probing celestial bodies. Multirobot systems are particularly promising for planetary exploration, as they are more robust to individual failure and have the potential to examine larger areas; however, there are limits to how many robots an operator can control individually. We recently took part in the European Space Agency's (ESA's) interdisciplinary equipment test campaign (PANGAEA-X) at a lunar/Mars analog site in Lanzarote, Spain. We used a heterogeneous fleet of unmanned aerial vehicles (UAVs)-a swarm-to study the interplay of systems operations and human factors. Human operators directed the swarm via ad hoc networks and data-sharing protocols to explore unknown areas under two control modes: in one, the operator instructed each robot separately; in the other, the operator provided general guidance to the swarm, which self-organized via a combination of distributed decision making and consensus building. We assessed cognitive load via pupillometry for each condition and perceived task demand and intuitiveness via selfreport. Our results show that implementing higher autonomy with swarm intelligence can reduce workload, freeing the operator for other tasks such as overseeing strategy and communication. Future work will further leverage advances in swarm intelligence for exploration missions.