cooperation of multiple aerial systems

Cooperation of Multiple Aerial Systems

Ivan Maza and Anibal Ollero

1 Synonyms (if any)

2 Definition (of entry term)

Given an specified mission, a multiple aerial system displays cooperative behaviorif, due to some underlying mechanism, there is an increase in the total utility of thesystem due to this cooperation. Hence, the cooperation of multiple aerial systemscan be defined as a joint collaborative behavior that is directed toward the executionof a common mission.

3 Overview

The cooperation of multiple aerial systems can be found in the jointly execu-tion of missions such as search and rescue, reconnaissance, surveying, detectionand monitoring in dangerous scenarios, exploration and mapping, hazardous ma-terial handling, and others. The coordination of a team of autonomous aerial sys-tems allows to accomplish missions that no individual autonomous vehicles canaccomplish on its own. Team members can exchange sensor information, collab-orate to track and identify targets, perform detection and monitoring activities[Ollero and Maza, 2007], or even actuate cooperatively in tasks such as the trans-portation and dexterous manipulation of loads.

The advantages of using multiple aerial systems when comparing to a singlepowerful one can be categorized as follows [Maza et al., 2014]:

Ivan Maza and Anibal OlleroRobotics, Vision and Control Group, Universidad de Sevilla, 41092 Seville, Spain, e-mail:[email protected],[email protected]

1

2 Ivan Maza and Anibal Ollero

• Multiple simultaneous interventions. A single autonomous aerial system islimited at any one time to sense or actuate in a single point. However, the compo-nents of a team can simultaneously collect information from multiple locationsand exploit the information derived from multiple disparate points to build mod-els that can be used to take decisions. Moreover, multiple aerial systems canapply simultaneously forces at different locations to perform actions that couldbe very difficult for a single aerial system.

• Greater efficiency. The execution time of missions such as exploration, search-ing for targets and others can be decreased when using simultaneously multipleaerial systems.

• Complementarities of team members. Having a team with multiple heteroge-neous aerial systems offers additional advantages due to the possibility of ex-ploiting their complementarities. Thus, for example, the fixed-wing airplanestypically have longer flight range and time of flight, whereas helicopters havevertical take-off and landing capability, better maneuverability and therefore canhover to obtain detailed observations of a given target.

• Reliability. The multi-aerial systems approach leads to redundant solutions of-fering greater fault tolerance and flexibility including reconfigurability in case offailures of individual vehicles.

• Technology evolution. The development of small, relatively low cost aerial sys-tems is fuelled by the progress of embedded systems together with the devel-opments on technologies for integration and miniaturization. Furthermore, theprogress on communication technologies experienced in the last decade plays animportant role in multiple aerial systems.

• Cost. A single aerial system with the performance required to execute some taskscould be an expensive solution when comparing to several low cost aerial systemsperforming the same task. Particularly this is clear in small size, light and lowcost versions, where constraints such as power consumption, weight and sizeplays an important role.

In platforms involving multiple aerial systems, the concepts of coordination andcooperation play an important role. In general, the coordination deals with the shar-ing of resources such as the aerial space, and both temporal and spatial coordinationshould be considered. The temporal coordination relies on synchronization amongthe different aerial systems and it is required in a wide spectrum of applications. Forinstance, for objects monitoring, several synchronized perceptions of the objectscould be required. In addition, spatial coordination of the aerial systems deals withthe sharing of the space among them to ensure that each aerial system will be ableto perform safely and coherently regarding the plans of the other aerial systems, andthe potential dynamic and/or static obstacles.

The cooperation of heterogeneous aerial systems requires the integration of sens-ing, control and planning in an appropriated decisional architecture. These architec-tures can be either centralized or decentralized depending of the assumptions onthe knowledge’s scope and accessibility of the individual vehicles, their computa-tional power, and the required scalability. A centralized approach will be relevantif the computational capabilities are compatible with the amount of information

Cooperation of Multiple Aerial Systems 3

to process, and the exchange of data meets both the requirements of speed (up-to-date data) and expressivity (quality of information enabling well-informed decision-taking). On the other hand, a distributed approach will be possible if the availableknowledge within each aerial vehicle is sufficient to perform “coherent” decisions,and this required amount of knowledge does not endow the distributed componentswith the inconveniences of a centralized system (in terms of computation power andcommunication bandwidth requirements). One way to ensure that a minimal globalcoherence will be satisfied within the whole system is to enable communication be-tween the aerial systems, up to a level that will warranty that the decision is globallycoherent. One of main advantages of the distributed approach relies on its superiorsuitability to deal with the scalability of the system.

It should be noticed that communication and networking also play an importantrole in the implementation of these schemes for multiple aerial systems. Single vehi-cle communication systems usually have an unshared link between the vehicle andthe control station. The natural evolution of this communication technique towardsmulti-vehicle configurations is the star shaped network configuration. While thissimple approach to vehicles intercommunication may work well with small teams,it could not be practical or cost effective as the number of vehicles grows. Thus,for example, in multi-aerial systems there are some approaches of a wireless het-erogeneous network with radio nodes mounted at fixed sites, on ground vehicles,and in aerial systems. The routing techniques allow any two nodes to communicateeither directly or through an arbitrary number of other nodes which act as relays.When autonomous teams of aerial systems should operate in remote regions withlittle/no infrastructure, using a mesh of ground stations to support communicationbetween the mobile nodes is not possible. Then, networks could be formed in an ad-hoc fashion and the information exchanges occur only via the wireless networkingequipment carried by the individual aerial systems.

4 Key Research Findings

Research related to the cooperation of multiple aerial systems can be classified basedon the coupling between the vehicles [Maza et al., 2014] (see Fig. 1):

1. Physical coupling. In this case, the aerial systems are connected by physicallinks and then their motions are constrained by forces that depend on the motionof other aerial systems. The lifting and transportation of loads by several aerialsystems lies in this category and will be addressed in Sect. 4.1. The main problemis the motion coordinated control taking into account the forces constraints. Asthe number of vehicles is usually low, both centralized and decentralized controlarchitectures can be applied.

2. Formations. The aerial systems are not physically coupled but their relative mo-tions are strongly constrained to keep the formation. Scalability properties to dealwith formations of many individuals are relevant and then, decentralized controlarchitectures are usually preferred. Section 4.2 will deal with this category.


3. Swarms. They are homogeneous teams of many aerial systems which interac-tions generate emerging collective behaviors. The resulting motion of the vehi-cles does not lead necessarily to formations. Scalability is a main issue due to thelarge number of vehicles involved and then pure decentralized control architec-tures are mandatory. Section 4.3 is devoted to the swarm approaches.

4. Intentional cooperation. The aerial systems of the team move according to tra-jectories defined by individual tasks that should be allocated to perform a globalmission [Parker, 1998]. These trajectories typically are not geometrically relatedas in the case of the formations. This cooperation will be considered in Sect. 4.4.In this case, problems such as task allocation, high-level planning, plan decom-position and conflict resolution should be solved taking into account the globalmission to be executed and the different aerial systems involved. Both centralizedand decentralized decisional architectures can be applied.

a) b)

c) d)

Fig. 1 Graphical illustration of a possible classification for the research areas related to multipleaerial systems: a) Physical coupling (3 aerial systems transporting one object), b) Formations, c)Swarms and d) Team executing tasks represented by crosses following an intentional cooperationapproach. The aerial systems are represented by gray arrows.


4.1 Physical Coupling: Joint Load Transportation

The transportation of a single object by multiple aerial systems is a natural extensionof the moving by several persons of a large and heavy object that cannot be handledby a single person. The coordinated control of the motion of each vehicle shouldconsider the involved forces induced by the other vehicles and the load itself. Forinstance, in the case of several aerial systems transporting a load using ropes, aforce sensor in the rope can provide a measurement of the influence of the otheraerial systems and the load being transported. Each aerial system could be controlledaround a common compliance center attached to the transported object. Under theassumption that each aerial system holds the object firmly with rigid links, the realtrajectories of all of the aerial systems are equal to the real trajectory of the object.However, in some transportation problems this assumption cannot be applied andthe transported object moves with a different dynamic behavior. A suitable approachfor the required coordinated control is the leader-follower scheme that will be moredetailed in the next section. In this scheme, the desired trajectory is the trajectory ofthe leader. The followers estimate the motion of the leader by themselves throughthe motion of the transported object.

Lifting and transportation of loads by using multiple helicopters has been also aresearch topic for many years motivated by the payload constraints of these vehi-cles and the high cost of helicopters with significant payload. In addition, the useof multiple manned helicopters is also problematic and only simple operations, likeload transportation with two helicopters, can be performed by extremely skillfuland experienced pilots. The level of stress is usually very high and, practical ap-plications are therefore rarely possible. Load transportation and deployment by oneand several helicopters is very useful for many applications including the deliveryof first-aid packages to isolated victims in disasters (floods, earthquakes, fires, in-dustrial disasters and many others) and is also a basic technology for other futureapplications: the building of platforms for evacuation of people in rescue opera-tions and the installation of platforms in uneven terrains for landing of manned andunmanned VTOL aircrafts. Lifting and transportation of a load by means of threeautonomous helicopters was demonstrated in [Bernard and Kondak, 2009] and af-ter that first successful test, the same load transportation system was used again in[Bernard et al., 2011] to deploy a camera on the roof of a building with a height of 12meters. Small-size single or multiple autonomous quadrotors are also considered forload transportation and deployment in [Michael et al., 2011, Palunko et al., 2012,Sreenath et al., 2013]. Micro-UAVs can operate in three-dimensional environments,explore and map multi-story buildings, manipulate and transport objects, and evenperform such tasks as assembly [Kumar and Michael, 2012].

On the other hand, safety issues in coordinated transportation of an object viamultiple aerial systems are addressed in [Baizid et al., 2014]. These considera-tions can be also found in [Gimenez et al., 2018], where a kinematic formationcontroller based on null-space theory is proposed to transport a cable-suspendedpayload with two rotorcraft UAVs considering collision avoidance, wind pertur-bations, and properly distribution of the load weight. System stability is demon-


strated using Lyapunov theory along with simulation results. Control synthesis fora slung-load transportation system by multiple unmanned aerial vehicles address-ing both the unmatched and matched uncertainties in the slung-load is presented in[Lee et al., 2018], and Lyapunov analysis is also applied to proof stability conditionof the tracking and parameter estimation error.

Dynamically coupled aerial systems should cooperate to transport loads, in con-trast to the existing results on formation control of decoupled multi-aerial systemsthat are addressed in the next section.

4.2 Vehicle Formations and Coordinated Control

In the formations, the members of the group of vehicles must keep user-defineddistances with the other group members [Guerrero and Lozano, 2013]. The controlproblem consists of maintaining these user-defined distances, and consequently theconfigurations of the neighbors in the formation should be taken into account in thecontrol law. Those configurations can be either received via inter-vehicle communi-cation or estimated using the sensors on-board. Anyway, formation control involvesthe design of distributed control laws with limited and disrupted communication, un-certainty, and imperfect or partial measurements. The stability of the formation hasbeen studied by many researchers that have proposed robust controllers to provideinsensitivity to possibly large uncertainties in the motion of nearby agents, trans-mission delays in the feedback path, and the consideration of the effect of quantizedinformation. The leader-follower approach mentioned in the previous section hasbeen also used to control general formations where the desired positions of follow-ers are defined relative to the actual state of a leader. It should be noted that everyformation can be further divided into simplest leader/follower schemes. Then, inthis approach some vehicles are designated as leaders and track predefined trajec-tories while the followers track transformed versions of these trajectories accordingto given schemes. Other methods are based on a virtual leader, a moving referencepoint whose purpose is to direct, herd and/or manipulate the vehicle group behav-ior. The lack of a physical leader among the vehicles implies that any vehicle isinterchangeable with any other in the formation.

Different works supported by experimental results can be found. The leader-follower pattern is adopted in [Yun et al., 2010] to maintain a fixed geometricalformation of unmanned helicopters while navigating following certain trajectories.The leader is commanded to fly on some predefined trajectories, and each follower iscontrolled to maintain its position in formation using the measurement of its inertialposition and the information of the leader position and velocity, obtained througha wireless modem. In [Gu et al., 2006] two-aircraft formation flights confirmed theperformance of a formation controller designed to have an inner and outerloop struc-ture, where the outerloop guidance control laws minimized the forward, lateral, andvertical distance error by controlling the engine propulsion and generating the de-sired pitch and roll angles to be tracked by the innerloop controller. In the formation


flight configuration, a radio control pilot maintained ground control of the leaderaircraft while the autonomous follower aircraft maintained a predefined positionand orientation with respect to the leader aircraft. An approach for coordination andcontrol of 3D heterogeneous formations of unmanned aerial and ground vehiclesunder hawk-eye-like relative localization is presented in [Saska et al., 2014]. Thecore of the method lies in the use of visual top-view feedback from flying robotsfor the stabilization of the entire group in a leaderfollower formation and is ver-ified in numerous experiments inspired by search-and-rescue applications, wherethe formation acts as a searching phalanx. On the other hand, a solution based ona virtual leader approach combined with an extended local potential field is pre-sented in [Paul et al., 2008] for formation flight and formation reconfiguration ofsmall scale autonomous helicopters. And for fixed wing models, experimental re-sults with YF-22 research aircrafts can be found in [Campa et al., 2007], validatingthe performance of a formation control laws using also a virtual leader configura-tion. Time-varying formation control problems are addressed in [Dong et al., 2015]applying consensus-based approaches and providing necessary and sufficient con-ditions for the UAV systems to achieve time-varying formations. Theoretical resultsare validated with five quardrotors, and outdoor experimental results are presented.The problem of controlling the motion of a group of UAVs bound to keep a forma-tion defined in terms of only relative angles (i.e. a bearing formation) is addressedin [Franchi et al., 2012] by developing a bearing-only formation controller exten-sively validated by means of simulations and experiments with quadrotor UAVsequipped with onboard cameras.

4.3 Swarms

The key concept in the swarms is that complex collective global behaviors can arisefrom simple interactions between large numbers of relatively unintelligent agents.This swarm cooperation is based on concepts from biology [Sharkey, 2006] andtypically involves a large number of homogeneous individuals, with relatively sim-ple sensing and actuation, and local communication and control that collectivelyachieve a goal. This can be considered as a bottom-up cooperation approach. It usu-ally involves numerous repetitions of the same activity over a relatively large area.The agents execute the same program, and interact only with other nearby agentsby measuring distances and exchanging messages. Nevertheless, it should be men-tioned that depending on the particular communication and sensing capabilities ofthe aerial systems in the swarm, simplified mechanisms based on partial or imper-fect information could be required. For example, the estimation of the full state ofthe neighbors is not possible in many swarm-based systems, and partial informationsuch as the distances with the neighbors is the only measurement available. Thesame is applicable to the messages interchanged, that can range from data pack-ets sent through wireless links to simple visual signals based on lights of differentcolors.


Aerial swarms differ from swarms of ground-based vehicles in two respects: theyoperate in a three-dimensional space and the dynamics of individual vehicles addsan extra layer of complexity. Reference [Chung et al., 2018] reviews dynamic mod-eling and conditions for stability and controllability that are essential in order toachieve cooperative flight and distributed sensing. The concept of operations for amicro-UAV system is adopted from nature from the appearance of flocking birds andswarming bees among others. This “emergent behavior” is the aggregate result ofmany simple interactions occurring within the swarm. Exploration of this emergentbehavior in a swarm is accomplished through a high performance computing paralleldiscrete event simulation in [Corner and Lamont, 2004]. In [Kovacina et al., 2002]a rule-based, decentralized control algorithm that relies on constrained randomizedbehavior and respects UAV restrictions on sensors, computation, and flight envelopeis presented and evaluated in a simulation of an air vehicle swarm searching for andmapping a chemical cloud within a patrolled region. Another behavior-based decen-tralized control strategy for UAV swarming by using artificial potential functionsand sliding mode control technique is presented in [Han et al., 2008]. Individualinteractions for swarming behavior are modelled using the artificial potential func-tions. For tracking the reference trajectory of the swarming of UAVs, a swarmingcentre is considered as the object of control. The sliding-mode control techniqueis adopted to make the proposed swarm control strategy robust with respect to thesystem uncertainties and varying mission environment.

In general, the above approaches deal with homogeneous teams without explicitconsideration of tasks decomposition and allocation, performance measures, andindividual efficiency constraints of the members of the team. Those aspects are con-sidered in the intentional cooperation schemes described in the next section.

4.4 Intentional Cooperation Schemes

In the intentional cooperation approaches each individual executes a set of tasks(subgoals that are necessary for achieving the overall goal of the system, and thatcan be achieved independently of other subgoals) explicitly allocated to perform agiven mission in an optimal manner according to planning strategies [Parker, 1998].The aerial systems cooperate explicitly and with purpose, but also has the limitationof independent subgoals: If the order of task completion is mandatory, additionalexplicit knowledge has to be provided to state ordering dependencies in the precon-ditions. It is also possible to follow a design based on “collective” interaction, inwhich entities are not aware of other entities in the team, yet they do share goals,and their actions are beneficial to their teammates [Parker, 2008].

A key issue in these systems is determining which aerial system should per-form each task (task allocation problem) so as to maximize the efficiency of theteam and ensuring the proper coordination among team members to allow themto successfully complete their mission. In order to solve the multi-aerial systemtask allocation problem, some metrics to assess the relevance of assigning given


tasks to particular robots are required. Significant research efforts can be foundin this area, where different taxonomies and surveys [Gerkey and Matari, 2004,Korsah et al., 2013, Khamis et al., 2015] for the multi-agent task allocation problemhave been presented.

Once the tasks have been allocated, it is necessary to coordinate the motionsof the aerial systems, which can be done by means of suitable multi-vehiclepath/velocity planning strategies. The main purpose is to avoid potential conflictsamong the different trajectories when sharing the same working space. It should bementioned that, even if the aerial systems are explicitly cooperating through mes-sages, a key element in many motion coordination approaches is the updated infor-mation about the state of the neighbors.

On the other hand, teams composed by heterogeneous members involve challeng-ing aspects. In [Ollero and Maza, 2007] the current state of the technology, existingproblems and potentialities of platforms with multiple UAVs (with emphasis on sys-tems composed by heterogeneous UAVs) is studied. This heterogeneity is two-fold:firstly in the UAV platforms looking to exploit the complementarities of the aerialvehicles, such as rotary wing aircrafts, fixed wing or airships, and secondly in theinformation processing capabilities on-board, ranging from pure remotely teleoper-ated vehicles to fully autonomous aerial systems.

Finally, cooperative perception can be considered as an important tool in manyapplications based on intentional cooperation schemes. It can be defined as the taskof creating and maintaining a consistent view of a world containing dynamic objectsby a group of agents each equipped with one or more sensors. Thus, a team ofvehicles can simultaneously collect information from multiple locations and exploitthe information derived from multiple disparate points to build models that can beused to take decisions.

5 Examples of Application

Regarding aerial systems physically coupled, different applications can be found.One of them is the transportation of heavy objects with several aerial systemswith limited payload. The first experimental results related to this applicationcan be found on December 2007, when lifting and transportation of a load bymeans of three autonomous helicopters [Bernard and Kondak, 2009] was demon-strated. After that first successful test, the load transportation system was usedagain in 2009 to deploy a camera on the roof of a building with a height of 12meters[Bernard et al., 2011] (see Fig. 2). Notice that in this case the physical cou-pling between the aerial systems are involved through direct interactions of eachvehicle with the joint load. Another recent application where aerial systems arephysically coupled is the dextreous manipulation of objects [Ruggiero et al., 2018,Khamseh et al., 2018].

The application of aerial systems formations can be found in many multi-vehiclemissions including searching and surveying, exploration and mapping, active recon-


Fig. 2 Three autonomous helicopters transporting a wireless camera to the top floor of abuilding with a height of 12 meters in May 2009 in the demonstration of the AWARE FP6project [Bernard et al., 2011]. A device on-board each helicopter is equipped with a force sensorto estimate the influence of the other helicopters and the load itself. The images show the missionduring the actual load transportation (left) and shortly before the load deployment (right). A fourthhelicopter which was used to acquire airborne video footage of the mission is visible on the right.

figurable sensing systems and space-based interferometry. An added advantage ofthe formation paradigm is that new members can be introduced to expand or upgradethe formation, or to replace a failed member. On the other hand, a large group for-mation of small UAVs offers benefits in terms of drag reduction and then increasedpayoffs in the ability to maintain persistent coverage of a large area. Practical ap-plications of formation control should include a strategy for obstacle avoidance andreconfiguration of the formation. The problem of controlling the motion of a groupof UAVs bound to keep a formation defined in terms of only relative angles (i.e.a bearing formation) can naturally arise within the context of several multi-robotapplications such as, e.g. exploration, coverage, and surveillance.

Applications of swarm technologies can be found in missions where the aerialsystems involved have very limited capabilities regarding perception, actuation, pay-load and computation power. Swarming has been considered as a disruptive technol-ogy to enable highly reconfigurable, on-demand, distributed intelligent autonomoussystems with high impact on applications such as tracking, inspection, and trans-porting systems. In any application, autonomous aerial swarms are expected to bemore capable than a single large vehicle, offering significantly enhanced flexibil-ity (adaptability, scalability, and maintainability) and robustness (reliability, sur-vivability, and fault-tolerance) [Hadaegh et al., 2016]. Reference [Dasgupta, 2008]presents a multiagent-based prototype system that uses swarming techniques in-spired from insect colonies to perform automatic target recognition using UAVs in


a distributed manner within simulated scenarios. In [Altshuler et al., 2008] a swarmof UAVs is used for searching one or more evading targets, which are moving in apredefined area while trying to avoid a detection by the swarm (Cooperative Huntersproblem). By arranging themselves into efficient geometric flight configurations, theUAVs optimize their integrated sensing capabilities, enabling the search of a maxi-mal territory.

Finally, in the area of intentional cooperation, a wide spectrum of applicationscan be found as most approaches are designed to fulfill multipurpose missions. Themulti-UAV coordination and control architecture described in [Gancet et al., 2005]was demonstrated for the autonomous detection and monitoring of fires by us-ing two helicopters and one airship [Ollero and Maza, 2007]. Multi-agent (com-bined ground and air) tasking and cooperative target localization has been demon-strated in [Hsieh et al., 2007], as well as multi-target tracking (ground vehicles)with a micro-UAV [He et al., 2010]. An architecture endowed with different mod-ules that solve the usual problems that arise during the execution of multipurposemissions, such as task allocation, conflict resolution, task decomposition, and sen-sor data fusion was presented in [Maza et al., 2011]. The full approach was val-idated in field experiments with different autonomous helicopters equipped withheterogeneous devices onboard, such as visual/infrared cameras and instrumentsto transport loads and to deploy sensors (see Fig. 3). The validation process in-cluded several multi-UAV missions for civil applications in a simulated urban set-ting: Surveillance applying the strategies for multi-UAV cooperative searching pre-sented in [Maza and Ollero, 2007]; fire confirmation, monitoring and extinguish-ing; load transportation and deployment with single and multiple UAVs; and peopletracking.

6 Future Directions for Research

The concepts of coordinated and cooperative control of multiple aerial systems de-served significant attention in the last years in the control, robotics, artificial in-telligence and communication communities. The implementation of these conceptsinvolves integrated research in the control, decision and communication areas. Forinstance, the communication and networking technologies play an important rolein the practical implementation of any multi-vehicle system. Thus, the integratedconsideration of communication and control problems is a relevant research anddevelopment topic.

We have firstly reviewed the existing work on the physical coupling in systemswith multiple aerial vehicles. In order to solve this problem, control theory based onmodels of the vehicles and their force interactions have been applied. In this areathere is still significant research to be done for aerial systems applied in scenariosthat involve dexterous manipulation capabilities such as dexterous transportationand installation, inspection by contact, etc. The chapter also studied formation con-trol. In this problem the application of control theory based on models of the vehicles


Fig. 3 Field experiments presented in [Maza et al., 2011] with different autonomous helicoptersfollowing an intentional cooperation approach.

is dominant. Behavior based approaches that do not use these models have been alsodemonstrated. However, there is still further research required in order to formulatecontrol laws with stability guarantees for aerial systems with complex dynamicsand under the consideration of limited and imperfect communication. The work onswarms has been also reviewed. Approaches inspired in biology and multi-agentsystems are common. The problems are typically formulated for large number ofindividuals but up to now the practical demonstrations involve few physical UAVs.Finally, The intentional task-oriented cooperation of aerial systems, possibly hetero-geneous has been also addressed. The task allocation problem and the path planningtechniques play an important role here, as well as the application of cooperativeperception methods. All of them are nowadays very active research areas with manygaps to be addressed.

Cross References

1. Coordinated Control of Multiple Unmanned Aerial Manipulator Systems2. Aerial Robotic Manipulators3. Unmanned Aerial Vehicles Swarms4. Cooperative Robots5. Distributed Autonomous Robotic Systems


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