Fleet Of Unmanned Aerial Vehicles Research Articles

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.

Communicating Multi-UAV System for Cooperative SLAM-based Exploration

In the aerial robotic community, a growing interest for Multi-Robot Systems (MRS) appeared in the last years. This is thanks to i) the technological advances, such as better onboard processing capabilities and higher communication performances, and ii) the promising results of MRS deployment, such as increased area coverage in minimum time. The development of highly efficient and affordable fleet of Unmanned Aerial Vehicles (UAVs) and Micro Aerial Vehicles (MAVs) of small size has paved the way to new large-scale applications, that demand such System of Systems (SoS) features in areas like security, disaster surveillance, inundation monitoring, search and rescue, infrastructure inspection, and so on. Such applications require the robots to identify their environment and localize themselves. These fundamental tasks can be ensured by the exploration mission. In this context, this thesis addresses the cooperative exploration of an unknown environment sensed by a team of UAVs with embedded vision. We propose a multi-robot framework where the key problem is to cooperatively choose specific regions of the environment to be simultaneously explored and mapped by each robot in an optimized manner in order to reduce exploration time and, consequently, energy consumption. Each UAV is able to performSimultaneous Localization And Mapping (SLAM) with a visual sensor as the main input sensor. To explore the unknown regions, the targets – selected from the computed frontier points lying between free and unknown areas – are assigned to robots by considering a trade-off between fast exploration and getting detailed grid maps. For the sake of decision making, UAVs usually exchange a copy of their local map; however, the novelty in this work is to exchange map frontier points instead, which allow to save communication bandwidth. One of the most challenging points in MRS is the inter-robot communication. We study this part in both topological and typological aspects. We also propose some strategies to cope with communication drop-out or failure. Validations based on extensive simulations and testbeds are presented.

Nonlinear control for collision-free navigation of UAV fleet

This paper presents the development of a cooperation scheme among unmanned aerial vehicle (UAV). Sliding mode control technique is used to guarantee that the set of robots can follow a reference trajectory and, in addition, will guarantee collision-free navigation of these autonomous vehicles. The analytical approach of the control strategy is shown, and the necessary conditions to guarantee the stability and governability of the multi-robot system are derived. The strategy and the derived conditions are tested using simulations, to demonstrate their effectiveness for collision-free navigation of an UAV fleet.

UAVS FLIGHT ROUTES OPTIMIZATION IN CHANGING WEATHER CONDITIONS – CONSTRAINT PROGRAMMING APPROACH

The problem of delivering goods in a distribution network is considered in which a fleet of Unmanned Aerial Vehicles (UAV) carries out transport operations. The changing weather conditions in which the transport operations take place and the UAVs energy capacity levels influenced by the weather conditions are taken into account as factors that affect the determination of a collision-free route. The goods must be delivered to the customers in a given time window. Establishing the routes are the focus of this study. Solutions maximizing the level of customer satisfaction are focused and the computational experiments presented in the study show the impact of weather conditions on route determination.

ВИЗНАЧЕННЯ ОПТИМАЛЬНОГО МАРШРУТУ ОБЛЬОТУ ЗАДАНИХ ТОЧОК ТЕРИТОРІЇ ПОТЕНЦІЙНО НЕБЕЗПЕЧНОГО ОБ’ЄКТУ ФЛОТОМ БПЛА

Motivation. One of the tasks of nuclear power plants and other potentially dangerous objects monitoring employing unmanned aerial vehicles (UAV) is flying over specified points of the territory of such objects. Finding the optimum routes often involves different options for the traveling salesman problem solving. However, if there are multiple starting positions, there is a need to solve the traveling salesman problem for each variant of the UAV start (for each variant of the start-end point of the route). The subject matter of the paper is the process of minimizing the flight time of visiting the specified points of the potentially dangerous object territory, taking into account the locations and models of the UAV fleet. The tasks to be solved are: to develop an algorithm for determining the optimal route for flight over of the given points of the potentially dangerous object territory for the fleet, each UAV of which is at its separate starting position; to show the possibility of using the proposed algorithm to minimize the flight time for visiting all of the appointed control posts of the automated radiation situation monitoring system for Zaporizhzhia nuclear power plant. The methods used are: graph theory, mathematical optimization models, methods for solving the traveling salesman problem. The following results were obtained. The faceted classification of the traveling salesman problem for UAV flight routing is offered. The steps of the algorithm for determining the optimal route of flight over of the specified points of the potentially dangerous object territory by the UAV fleet are described. The problem of determining the fastest flight over of 11 control posts of the automated radiation monitoring system for Zaporizhzhia nuclear power plant is solved for two cases: 1) UAV "Leleka-100" are at all starting positions, 2) UAV "Leleka-100" is at the first starting position, various modifications of the model "R-100" are at the rest starting position. Changes in the optimal route when changing UAV models and speeds are shown. Conclusions. The results obtained should be used to justify the composition of the UAV fleet, simulate its application, evaluate its target effectiveness, as well as to create algorithmic support and software for ground control station operators’ work places. Further research should focus on developing models that take into account the possibility of refueling UAVs or recharging their batteries at stationary or moving posts while being on a route.

МОДЕЛІ НАДІЙНОСТІ УГРУПОВАНЬ ФЛОТІВ БПЛА З КОВЗНИМ РЕЗЕРВУВАННЯМ ДЛЯ МОНІТОРИНГУ ПОТЕНЦІЙНО НЕБЕЗПЕЧНИХ ОБ’ЄКТІВ

Over the past years, unmanned aerial vehicles have been used to solve the problems of pre-and post-accident monitoring of nuclear power plants and other potentially dangerous objects. The use of a fleet (fleet grouping) of the unmanned aerial vehicle (UAV) for monitoring missions is due to the special conditions of the aggressive environment, which causes the failures of certain UAVs, and therefore needs to ensure a high level of reliability of such a fleet (the group of fleets). The most effective way to solve this problem is to use k-out-of-n redundancy. The subject of the study is a UAV fleet group with k-out-of-n redundancy. In order to take into account the reliability of the control station for various variants of the organization of UAV fleet groups, it is advisable to formulate the following tasks: to analyze the different structures of UAV fleet groups taking into account the scheme of activation of redundant UAVs; to develop and investigate models of reliability of UAV fleet groups with a centralized, decentralized and partially decentralized schemes of activation of redundant UAVs with the possibility of control station redundancy; to formulate recommendations for choosing a scheme for activation of redundant UAVs. Research results: the structure of the UAV fleet group with two-level k-out-of-n redundancy (at fleet levels and their groups using the reserve fleet) and different variants of organization of control stations were proposed. The centralized, decentralized and partially decentralized structures of activation of the redundant UAVs for a fleet group with reserve (reserve fleets) are investigated, namely: the reliability block diagrams of these systems are constructed; analytical expressions for calculating the probability of failure-free operation of the UAV fleet group based on each of these schemes are obtained; the following dependencies are obtained and investigated: probability of failure-free operation of a UAV fleet group with different probabilities of UAV failure-free operation from the number of the main fleets in case of use of reserve fleet with three UAVs; the probability of UAV fleet group failure-free operation with different schemes of activation of redundant UAVs from the number of main fleets in the case of using a reserve fleet with three UAVs. The development and research of reliability models have made it possible to formulate tasks regarding the choice of schemes for activation of redundant UAVs and the corresponding recommendations for the organization of groups. Further research is appropriate to focus on developing software to support decision-making on choosing the options for structures and taking into account possible schemes to get areas of responsibility by UAVs.

A Game Theory Based Efficient Computation Offloading in an UAV Network

Recently, solutions based on mobile edge computing paradigm have been widely discussed in academia and industry. This paradigm offers solutions to address limitations, in terms of battery lifetime and processing power, of mobile and constrained devices. Despite the ever-increasing capabilities of these devices, resource requirements of applications can often transcend what is available within a single device. Offloading intensive computation tasks to a distant server can help applications reach their desired performances. In this work, we tackle the problem of offloading heavy computation tasks of unmanned aerial vehicles (UAVs) while achieving the best possible tradeoff between energy consumption, time delay, and computation cost. We focus on a scenario of a fleet of small UAVs performing an exploration mission. During their mission, these constrained devices have to carry-out highly intensive computation tasks such as pattern recognition and video preprocessing. We formulate the problem using a non-cooperative theoretical game with N players and three pure strategies. We provide a comprehensive proof for the existence of a Nash equilibrium and implement accordingly a distributed algorithm that converges to such an equilibrium. Extensive simulations are performed in order to provide thorough results and assess the performances of the approach compared to three other models. Results show that our algorithm outperforms all the three approaches. Our approach achieved in average about 19%, 58%, and 55% better results compared to local computing, offloading to the edge server, and offloading to base station, respectively.

Design and Implementation of Distributed Path Planning Algorithm for a Fleet of UAVs

This paper presents the development of a controller for a fleet of unmanned aerial vehicles based on a distributed path planning strategy under a multiagent systems framework. The issue, treated as an online optimization problem, is solved using a particle swarm optimization (PSO) algorithm. The proposal was validated in experiments, considering different scenarios like fixed and mobile targets, external disturbances, and the loss of an agent. The proposed PSO is implemented independently in each vehicle in order to determine, by minimizing a cost function, the best paths that ensure the fleet formation control, target tracking, and collision avoidance.

Multiagent UAV Routing: A Game Theory Analysis With Tight Price of Anarchy Bounds

We study the multiagent unmanned aerial vehicle (UAV) routing problem where a set of UAVs needs to collect information via surveillance of an area of operation. Each UAV is autonomous and does not rely on a reliable communication medium to coordinate with other UAVs. We formulate the problem as a game where UAVs are players and their strategies are the different routes they can take. Our model also incorporates the useful concept of information fusion. This results in a new variant of weighted congestion-type games. We show that the price of anarchy (PoA) of the game is at most 2, irrespective of the number of UAVs and their sensor capabilities. This also validates the empirical results of earlier works. Furthermore, we identify classes of games for the existence of a pure Nash equilibrium. To the best of our knowledge, these are the first such theoretical results in the related literature. Finally, we conduct experimental studies using randomly generated instances with several multiagent UAV routing policies. Our insights are that PoA increases with the congestion level when the same number of UAVs search a smaller area or more UAVs search the same area, and on an average, our proposed policies are less than 10% worse than the centralized optimal for the problem scenarios attempted. Note to Practitioners —UAVs are becoming increasingly popular for information collection tasks in defense and civilian applications alike. When the collection area is large, it is not unusual that a fleet of UAVs is deployed. Routing of a fleet can be performed in a centralized or decentralized manner. Decentralized routing might be the only possibility when centralized situational awareness is not possible due to bandwidth limitations and centralized optimal routes for each UAV in the fleet are too complex to compute. Autonomous solutions have several other advantages, let alone simplicity. For managers of UAV systems, our work provides the first theoretical characterization of how bad could decentralized routing be. Under various scenarios of information fusion, specifically weak and strong, and the attribution of information collected to each UAV of a team, we prove that the fleet will collect at least 50% of the best-centralized solution. Empirically, we show that, in fact, the performance of the fleet is much better and generally not worse than 10% of the best-centralized solution. Hopefully, our routing strategies provide valuable guidance to the practicing engineer or manager of a UAV fleet.

Sampling-Based Tour Generation of Arbitrarily Oriented Dubins Sensor Platforms

This paper describes a formulation and develops a novel procedure for a fleet of unmanned aerial vehicles (UAVs) from the perspective of remotely executable tasks. In a complex mission environment, the characteristics of vehicles can be different in terms of sensing capability, range, direction, or the motion constraints. The purpose of this paper is to find a set of paths that minimizes the sum of costs while every task region is visited exactly once under certain reasonable assumptions. The heterogeneous multi-UAV path planning problem is formulated as a generalized heterogeneous multiple depot asymmetric traveling salesman problem (GHMDATSP), which is a variant of the traveling salesman problem. The proposed transformation procedure changes an instance of the GHMDATSP into a format of an asymmetric traveling salesman problem (ATSP) to obtain tours for which the total cost of a fleet of vehicles is minimized. The instance of the ATSP is solved using the Lin–Kernighan–Helsgaun heuristic, and the result is inversely transformed to the GHMDATSP-formatted instance to obtain a set of tours. An additional local optimization-based path refinement process helps obtain a high-quality solution. Numerical experiments investigate and confirm the validity and applicability of the proposed procedure.

Minimization of the waiting time to start performing a radiation monitoring mission via a fleet of unmanned aerial vehicles in the new zone of responsibility

The analysis of possible applications of unmanned aerial vehicles for per-forming radiation monitoring missions was carried out. This paper proposes an approach to minimization of the waiting time to start performing a radiation mon-itoring mission via a fleet of unmanned aerial vehicles (hereafter UAVs) in the new zone of responsibility. The approach includes the following steps: distribu-tion of UAVs in the zones of responsibility and setting the time for performing a mission for each UAV; determination of the starting position location where UAVs battery can be replaced; defining the new zone of responsibility and time for performing a mission in the zone; construction of an algorithm for calculating parameters necessary for solving the optimization problem; formulation of the optimization problem and constraints. Parameters to be calculated: the UAV flight time from the current mission zone to: a) starting position; b) a new zone (with making the intermediate landing and without making one); a flight time from the new zone to the starting position; the UAV battery life required for per-forming the mission in the new zone; the waiting time to start performing the mis-sion in the new zone and the current battery life for the UAV at the start of its mission in the new zone for the following cases: a) the UAV reaches the new zone with making the intermediate landing, b) the UAV reaches the new zone without making one. An example of the proposed approach implementation for a fleet, con-sisting of five Ukrainian electric UAVs “Furiia” and performing missions in five zones of responsibility (one UAV performs a mission in one zone) within the Za-porizhzhia NPP thirty-kilometre radius zone is given. Six possible new zones are considered and the waiting time to start performing a mission in each of these zones is given. It has been get that UAV 5, UAV 4 and UAV 2 can be assigned to three zones, two zones and one zone, respectively. UAV 1 and UAV 2 can not be used in any new zone. Note that the UAV 5 can be used once without making the intermediate landing at the starting position for its battery replacement and twice with making the intermediate landing. Further studies should take into account the following cases: the fleet consists of various UAVs, two or more UAVs should be assigned to the new zone.

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.

A Fleet of MEC UAVs to Extend a 5G Network Slice for Video Monitoring with Low-Latency Constraints

In the last decade, video surveillance systems have become more and more popular. Thanks to a decrease in price of video camera devices and the diffusion of cheap small unmanned aerial vehicles (UAVs), video monitoring is today adopted in a wide range of application cases, from road traffic control to precision agriculture. This leads to capture a great amount of visual material to be monitored and screened for event detection. However, information that is gathered from a platform of video monitoring UAVs may produce high-volume data, whose processing is unfeasible to be done locally by the same UAVs that perform monitoring. Moreover, because of the limited bandwidth of wireless links connecting UAVs to computing infrastructures that are installed on ground, offloading these data to edge clouds renders these platforms infeasible for video analysis applications with low-latency requirements. The target of this paper is to extend a 5G network slice for video monitoring with a Flying Ad-hoc NETwork (FANET) constituted by UAVs with multi-access edge computing (MEC) facilities (MEC UAVs), flying very close to the layer of UAVs monitoring the area of interest. A policy for mutual help among MEC UAVS is defined in order to increase the performance of the whole aerial MEC platform, so further reducing end-to-end latency between sources and actuators, and increasing system reliability. A use case is considered for a numerical analysis of the proposed platform.

Future UAV-Based ITS: A Comprehensive Scheduling Framework

Multi-rotor unmanned aerial vehicles (UAVs) have been recently recognized as one of the top emerging technologies to be utilized in various smart city domains such as intelligent transportation systems (ITS). They represent an innovative mean to complement existing technologies to surveil transportation network, control traffic, and monitor incidents. The UAVs usually operate for time-limited missions due to their limited battery capacity. Hence, they need to frequently return to their docking stations to recharge their batteries, which handicaps their mission coverage and performance. When designing a UAV-based ITS infrastructure, it is crucial to leverage the UAV fleet effectively. In this paper, a generic management framework of UAVs for ITS applications is developed. The problem of docking/charging station placement is first investigated to find optimized locations for a given number of stations to be installed by the ITS operator. To this end, two fundamental criteria are taken into account: i) the flying time required by the UAV to reach the mission/incident location and ii) the risk of battery failure during the UAV operation. The two algorithms, namely a penalized weighted k-means algorithm and the particle swarm optimization algorithm, are designed for this purpose. Once the docking stations are placed, a UAV scheduling program is formulated to optimally cover the pre-known missions while minimizing the total energy consumption of the fleet and respecting a coverage efficiency targeted by the ITS operator. The proposed proactive scheduling approach employs multiple UAVs in sequential and parallel ways to cover spatially and temporally distributed events in the geographical area of interest over a long period.

Integrated maintenance and mission planning using remaining useful life information

ABSTRACTThe modern world requires high reliability and availability with minimum ownership cost for complex industrial systems (high-value assets). Maintenance and mission planning are two major interrelated tasks affecting availability and ownership cost. Both tasks play critical roles in cost savings and effective utilization of the assets, and cannot be performed without taking each other into consideration. Maintenance schedule may make an asset unavailable or too risky to use for a mission. Mission type and duration affect the health of the system, which affects the maintenance schedule. This article presents a mathematical formulation for integrated maintenance and mission planning for a fleet of high-value assets, using their current and forecast health information. An illustrative example for a fleet of unmanned aerial vehicles is demonstrated and evolutionary-based solutions are presented.

An unmanned aerial vehicle optimal selection methodology for object tracking

In this article, an optimal method of unmanned aerial vehicle selection for following a moving ground target is presented. The most suitable unmanned aerial vehicle from an aircraft operating fleet is selected for tracking task. The aircraft choice is done taking regarding the unmanned aerial vehicle distribution in the mission zone, individual performance of fleet platforms, and maneuverability of a ground platform. The unmanned aerial vehicle fleet members’ flying qualities are various, so an optimization process is used to assign an aircraft to follow the ground target. A target trajectory prediction is embedded in tracking algorithm. The simulation results demonstrated the effectiveness of algorithms developed, and in-flight demonstration proved the possibility of realization of computed trajectories by the unmanned aerial vehicle.