IEEE TRANSACTIONS ON CYBERNETICS, VOL. 46, NO. 4, APRIL

IEEE TRANSACTIONS ON CYBERNETICS, VOL. 46, NO. 4, APRIL 2016 959

Coordination Between Unmanned Aerial andGround Vehicles: A Taxonomy and

Optimization PerspectiveJie Chen, Senior Member, IEEE, Xing Zhang, Bin Xin, Member, IEEE, and Hao Fang, Member, IEEE

Abstract—The coordination between unmanned aerial vehi-cles (UAVs) and unmanned ground vehicles (UGVs) is a proactiveresearch topic whose great value of application has attractedvast attention. This paper outlines the motivations for study-ing the cooperative control of UAVs and UGVs, and attemptsto make a comprehensive investigation and analysis on recentresearch in this field. First, a taxonomy for classification of exist-ing unmanned aerial and ground vehicles systems (UAGVSs) isproposed, and a generalized optimization framework is devel-oped to allow the decision-making problems for different typesof UAGVSs to be described in a unified way. By following the pro-posed taxonomy, we show how different types of UAGVSs can bebuilt to realize the goal of a common task, that is target tracking,and how optimization problems can be formulated for a UAGVSto perform specific tasks. This paper presents an optimizationperspective to model and analyze different types of UAGVSs,and serves as a guidance and reference for developing UAGVSs.

Index Terms—Cooperative control, heterogeneous multivehi-cle, multivehicle system, optimization, unmanned aerial vehi-cle (UAV), unmanned ground vehicle (UGV).

I. INTRODUCTION

THE ADVENT of the single-wing unmanned aerial vehi-cle (UAV) in 1927 caused a great sensation in the

world, which marks another milestone of aviation technol-ogy development since human beings flew into the sky. In theensuing decades, aided by the rapid development of automa-tion and artificial intelligence technologies, research on UAVshas blossomed and made substantial progress. In terms oftheir different structures, UAVs can be further categorizedinto fixed- and rotary-wing ones which are similar but still

Manuscript received June 7, 2014; revised January 13, 2015; acceptedMarch 23, 2015. Date of publication April 17, 2015; date of current ver-sion March 15, 2016. This work was supported in part by the NationalScience Fund for Distinguished Young Scholars under Grant 60925011, inpart by the Projects of Major International (Regional) Joint Research ProgramNSFC under Grant 61120106010, in part by the National Natural ScienceFoundation of China under Grant 61304215, in part by the Foundation forInnovative Research Groups of the National Natural Science Foundationof China under Grant 61321002, in part by the Beijing OutstandingPh.D. Program Mentor under Grant 20131000704, and in part by theResearch Fund for the Doctoral Program of Higher Education of China underGrant 20131101120033. This paper was recommended by Associate EditorT. H. Lee. (Corresponding author: Bin Xin.)

The authors are with the School of Automation, Beijing Institute ofTechnology, Beijing 100081, China, and also with the Key Laboratoryof Intelligent Control and Decision of Complex Systems, Beijing Instituteof Technology, Beijing 100081, China (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TCYB.2015.2418337

very different. Fixed-wing UAVs generally have quicker speedand heavier payload capacity than rotary-wing ones whilethe rotary-wing UAVs can hover and vertically take off andland. Since tasks faced by UAVs are of increasing com-plexity, a single UAV is usually not competent for giventasks. Recently, a significant shift of focus occurred asresearchers began to investigate problems involving multi-ple UAVs, rather than single. Coordinating multiple UAVsand even multiple UAV groups to perform tasks can dramat-ically improve the effectiveness of the whole systems fromthe viewpoint of performance in accomplishing tasks androbustness, and reliability. Nowadays, cooperative control ofmultiple UAVs has become a highly active research area, andhas been extensively employed in various applications, suchas border patrol [1], fire detection [2], [3], cooperative targettracking [4], [5], mobile sensor network [6], and so on.

Likewise, interest in unmanned ground vehicles (UGVs)has grown significantly over the past decades. Recently, someUGVs with high intelligence appear constantly. A bionicrobot called “Robo Lobster (BUR-001)” invented by theNortheastern University can perceive environments like a realanimal. The BigDog developed for the U.S. troop by theBoston Dynamics engineering company can balance itselfrelying on active sense to environments and traverse throughcomplex terrains. These kinds of robots, as the extension ofsoldiers’ hands, eyes and ears, can assist them to accom-plish tasks more efficiently especially for hazardous taskslike bomb disposal. Recently, research on cooperative con-trol of multiple UGVs such as formation control [7], [8],area search [9], and consensus control [10] has been widelyconducted. Additionally, a variety of robotics competitionssuch as RoboCup and the robotic contest organized by theAmerican Institute of Aeronautics and Astronautics havegreatly propelled the research on multiple UGVs systems.

In practice, small UAVs and UGVs are well suited tobe widely deployed and employed due to their low cost.Table I lists the characteristics of interest with respect toUAVs and UGVs. It can be observed from the table that,on one hand, both UAVs and UGVs have their own limi-tations, which notably reduce their efficiency in performingtasks to some extent. On the other hand, it is also obvious thatUAVs and UGVs share strong complementarities in the char-acteristics that we are concerned with. The complementaritiesof UAVs and UGVs are primarily embodied in the follow-ing aspects. First, when capturing ground features, sensors

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960 IEEE TRANSACTIONS ON CYBERNETICS, VOL. 46, NO. 4, APRIL 2016

TABLE IADVANTAGES AND DISADVANTAGES OF DIFFERENT KINDS OF SMALL UNMANNED VEHICLES

Fig. 1. Web of Science data showing the number of publications per yearon the three topics.

located on UAVs (especially fixed-wing ones) are usually lim-ited by operating airspeed and altitude while UGVs can bedeployed to accurately locate ground targets [11]. Second, dueto advantages in altitude, communication links among UAVscan be less blocked by obstacles than that among UGVs.Hence, disconnected UGVs located at different positions canbe connected indirectly with the support of UAVs serving ascommunication relays. Finally, UAVs (especially small ones)are usually restricted by their short voyage due to the limitationof carried energies, while UGVs have larger payload capabil-ity. To sum up, coordination between UAVs and UGVs haspromised a bright prospect due to strong complementaritiesbetween them in sensing, communication, payload abilities,and so on.

Nguyen et al. [12] from Space and Naval Warfare SystemsCenter Pacific, provided a review of recent accomplishmentsand current status of a number of projects in land, sea and airunmanned system research. It can be seen that the researchon cooperative control of aerial platforms and ground (or sea)ones has been rarely seen. As a research topic, the study ofUAVs, UGVs and the cooperative control of UAVs and UGVshas gained increasing attention. Fig. 1 shows the data fromthe Web of Science resulting from a topic search on threeterms marked by TS1, TS2, and TS3, respectively. Each year’sresults indicate the number of publications appearing in thatyear. It is quite clear that there is a significantly increas-ing interest in research on both UAVs and UGVs. However,the research on cooperative control of UAVs and UGVs, asopposed to that of UAVs or UGVs, is still fairly limited.

Fig. 2. Four typical research topics related to coordination of autonomousagents.

Existing studies are still placed at theoretical research stage,and there are numerous technical bottlenecks that need to bebroken through urgently to speed up the related research fromtheory to practice.

Generally speaking, an unmanned aerial and ground vehi-cles system (UAGVS) can be characterized as a set of UAVsand a set of UGVs operating in the same field to work togetherto achieve a common goal. In a broad sense, as shown inFig. 2, the topic of coordination between UAVs and UGVsfalls under the field of heterogeneous robots coordination, mul-tirobot coordination, and multiagent coordination. Multirobotsystems (MRSs) are typical examples of multi-agent sys-tems (MASs) in physical world, and much attention has beengiven to MRS. In [13], the authors surveyed the current appli-cations of cooperative control of multivehicle systems andsummarized some of the key technical approaches that hadbeen explored. To clearly classify the large variety of MRS,scholars have proposed many different methods. In [14], threecriteria for characterizing MRS are proposed in light of thetypes of interactions from the perspective of distributed intel-ligence, while in [15], another taxonomy which is focused onthe coordination aspect is developed. Heterogeneous robotscoordination which is one of the branches of MRS research is,nowadays, an important research topic. UAVs/UGVs coordina-tion, one of the most typical scenarios of heterogeneous robotscoordination, has drawn special and wide attention. The greatheterogeneity and complementaries between UAVs and UGVsin dynamics, speed, sensing, communication, functions, andso forth, make the UAGVSs powerful to complete a variety ofcomplicated task. In contrast to existing works on MRS whichare mainly focused on either UAVs or UGVs, the need to dealwith the information from two totally different platforms andthe need to effectively coordinate the behaviors of UAVs andUGVs, make the research on UAGVSs more challenging.

CHEN et al.: COORDINATION BETWEEN UNMANNED AERIAL AND GROUND VEHICLES: A TAXONOMY AND OPTIMIZATION PERSPECTIVE 961

A. Motivations of This Paper

According to the literature survey, a variety of specific appli-cations of UAGVSs have been proposed and validated, butinfrequently analyzed. In addition, current research results onMRS cannot be directly applied to UAGVSs due to the greatdifference between UAVs and UGVs, and a mass of newcharacteristics exhibited in UAGVSs should be payed specialattention and be researched further. Duan and Liu [11] pre-sented a preliminary survey of recent research on this topic,in which some key issues involved in the topic were dis-cussed and some future directions were also analyzed. Here,we extend current surveys on UAGVSs, propose a taxonomyfor classification of existing UAGVSs, and develop an opti-mization framework to allow different types of UAGVSs tobe described in a unified way.

B. Contributions

The main contribution of this paper is the proposalof the taxonomy for unfolding different coordinationpatterns/mechanisms in UAGVS as well as anoptimization-based decision-making framework for UAGVS.The taxonomy provides a tool and guidance for analyzingvarious UAV–UGV coordination patterns, and the frameworkallows the decision-making problems for different typesof UAGVSs to be described in a unified way. Besides, asa secondary contribution, this paper also makes a comprehen-sive investigation and analysis on recent research regardingUAGVS.

The remainder of this paper is organized as follows.In Section II, some basic symbolic notations and defini-tions are introduced. In Section III, we propose a taxonomyfor classification of UAGVSs, followed by an analysis ofsome reprensentative types of UAGVSs in Section IV. InSection V, we use a typical example, cooperative target track-ing, to show how different types of UAGVSs can be built toachieve the same goal and highlight their differences froman optimization perspective. In Section VI, the conclusionis given.

II. PRELIMINARIES

A key challenge in UAGVS research is determining thecontrol inputs of UAVs and UGVs they should take to con-tribute to the overall system objective, which can be viewedas an optimization problem. It will be helpful to have a clearnotion of terms which are used to formulate the problems inthe sequel.

It is assumed that NA UAVs and NG UGVs are involved inthe system. We assume that the dynamics of the ith UAV canbe written as{

xAi = f A

i

(xA

i , uAi

), xA

i ∈ RnA , uAi ∈ RmA

yAi = hA

i

(xA

i

) i = 1, 2, . . . , NA

where xAi is the state of the ith UAV, uA

i is the input thatcontrols the state of the UAV, f A

i represents its dynamics, andyA

i is the output which mainly includes the position and postureof the UAV in 3-D space. nA and mA are dimensions of the

state and inputs of UAVs, respectively. Similarly, the dynamicsof the jth UGV can be written as⎧⎨⎩

xGj = f G

j

(xG

j , uGj

), xG

j ∈ RnG , uGj ∈ RmG

yGj = hG

j

(xG

j

) j = 1, 2, . . . , NG

where the symbols are akin to that of UAVs. For easeof presentation, let XA = (xA

1 , xA2 , . . . , xA

NA) and UA =

(uA1 , uA

2 , . . . , uANA

) represent the complete state and inputs fora collection of NA UAVs, and XG = (xG

1 , xG2 , . . . , xG

NG) and

UG = (uG1 , uG

2 , . . . , uGNG

) for UGVs.Different types of UAVs (fixed- versus rotary-wing UAVs)

and UGVs (wheeled versus tracked UGVs) characterize differ-ent dynamics, and are suited to different application domains.Real dynamics models of vehicles are usually very complexsince sophisticated constraints on state and control vari-albes, which makes the real-time computation for practicalapplications infeasible. Therefore, simplified models such asDubins vehicle [16] for fixed-wing UAVs, Reeds and Sheepvehicle [17], car-like model [18] for UGVs, are frequentlyapplied in practice.

Additionally, it is assumed that the vehicles are able tocommunicate with each other only if they enter into their effec-tive communication range in carrying out the task. We definean interaction graph to describe possible communicationchannels among vehicles

= {, (t)

}where is the set of nodes representing individual vehicles,and (t) ⊂ × is the set of time-varying edges indicatingthe communication channels between vehicles. Denote by Ni

the neighbors of vehicle i, that is the set of vehicles whichcan communicate with it, and denote by |Ni| the number ofthe elements of Ni.

Given these definitions, we can define a task in terms of theperformance function

J =∫ T

0L(XA, UA, XG, UG|E)dt + V(XA(T), XG(T)|E)

where T is the horizon time over which the task should beaccomplished, L represents the cost of the task (e.g., energycost), V represents the terminal cost of the task (e.g., task com-pletion time), and E indicates environmental factors includingterrains, obstacles, wind fields, and so on, which may be eitherstatic or dynamic.

Given the optimization objective for a task, the controlinputs of UAVs and UGVs in theory can be derived by solvingthe following optimization problem:

arg min J =∫ T

0L(XA, UA, XG, UG|E)dt + V(XA(T), XG(T)|E)

s.t. f (XA, UA, XG, UG|E) = 0

g(XA, UA, XG, UG|E) ≥ 0

where f and g are relevant equality and inequality constraintfunctions, respectively.

In practice, finding the optimal solution to the optimizationproblem is intractable due to the great complexity of objec-tives and constraints. Most often, the optimization objective


TABLE IISOME TYPICAL INSTANCES OF OBJECTIVES AND CONSTRAINTS FOR THE OPTIMIZATION FORMULATION OF UAGVSS

, and and are the effective comunication and the Kranges of the

includes contradictory sub-objectives such as using mini-mal resource expenditure to achieve maximal benefit, whichmakes the optimization problem very challenging. Moreover,complex and diversified constraints also contribute to theproblem difficulties. Commonly, both UAVs and UGVs areconstrained by their dynamics, collision avoidance, limitedenergy, task-related constraints, and so on. In particular, UAVs,especially smaller ones, are sensitive to environmental fac-tors (e.g., wind fields), which should be taken into accountin the planning [19]. In some special applications like tar-get surveillance, occlusion of the line of sight of UAVs byground buildings reduces the feasible observation space ofUAVs. Meanwhile, connectivity maintenance which is a sig-nificant precondition to coordinate the behaviors of vehicles isanother vital constraint we should consider. Besides the abovementioned factors, the uncertainty of environments, real-timerequirement, and so on, should also be taken into account inthe resolution of the optimization problem. Table II presentssome typical objectives and constraints which were frequentlyconsidered in literatures.

III. TAXONOMY OF UAGVSS

In view of the great heterogeneity and strong complementar-ities between UAVs and UGVs, the way of interaction between

Fig. 3. Visual representation of the three axes used in the taxonomy. Samerole (SR), different roles (DRs), coupled goal (CG), decoupled goal (DG),centralized decision-making (CD), decentralized decision-making (DD), andhybrid decision-making (HD).

them may be fairly complex. To date, a variety of UAGVSshave been developed for specific applications. To clearlyunderstand the types of UAGVSs, we propose a taxonomyof UAGVSs along three different axes as shown in Fig. 3.

Different from the work of [14] and [15] which are mainlyfocused on the coordination mechanism within UAVs or


Fig. 4. Simplified block diagram of a UAGVS.

UGVs, the emphasis of the proposed taxonomy is laid on thecoordination patterns between UAVs and UGVs. For a suitableclassification of the works, it is important to clearly define theaxes that are used. In the following, we define the classifica-tion axes and discuss the different cases that may appear alongeach axis.

A. Functional Role: Same Role Versus Different Roles

The axes defined here mainly describe the functional rolesplayed by UAVs and UGVs in a task. An easy division oftasks is achieved by assigning roles according to the skillsand capabilities of individual vehicles. Fig. 4 depicts the mainfunctional modules of a UAGVS where we define four func-tional roles: mobile sensor, mobile actuator, decision maker,and auxiliary facility. Theses roles are primarily identifiedfrom a control system viewpoint.

In many situations, UAVs and UGVs play the same func-tional roles in tasks. For example, both UAVs and UGVs actas mobile sensors when they are used for situational awarenessin a given region or used to track an evader.

There are indeed several cooperation schemes in whichthe complementarities of such heterogeneous robots can beexploited to enhance the efficiency of autonomous roboticoperations. A variety of systems with UAVs (UGVs) serv-ing as an auxiliary facility to assist UGVs (UAVs) have beendeveloped to improve the efficiency of the whole system. Anauxiliary facility can provide chief agents with energy, com-munication, computation, and other services other than thefunction of sensors, actuators and decision makers. The sce-narios in which UAVs and UGVs play different functionalroles in existing literatures mainly include the following twocases [24].

1) UGVs Act as Mobile Actuators, UAVs Act as DecisionMakers, Mobile Sensors, or Auxiliary Facilities: In these sys-tems, UGVs act as mobile actuators, and UAVs make decisionsfor UGVs [25]–[29], provide UGVs with environment andtask information [30]–[32], or act as communication relays toprovide communication links with the remote operator stationand between UGVs [22], [33], [34].

2) UAVs Act as Mobile Sensors or Actuators and UGVs Actas Auxiliary Facilities: In these systems, UAVs act as mobilesensors or actuators. However, in some cases, the low pay-load capacity and short flight endurance of UAVs (especiallysmaller ones) greatly restrict their efficiency in accomplishingtasks. As an alternative, UGVs can assist UAVs (especiallysmall rotary-wing ones) to complete tasks more efficientlyby providing them with energy or transporting them in largefields [35]–[38].

B. Task Goal: Coupled Goal Versus Decoupled Goal

It is concerned with the task goals that the two kinds ofvehicles are aimed. For coupled task goals, the actions of bothkinds of vehicles should be tightly coordinated so that thetask objective is optimized [39]. In some special cases, taskscan be broken up or decomposed into multiple steps or sub-tasks, where different parts of the task can be accomplishedby different kinds of vehicles. In this case, the shared taskgoal can be decoupled into different sub-goals so that eachsub-goal can be achieved by only UAVs or UGVs.

For ease of presentation, it is generally considered that theequality and inequality constraints are included in the objectivefunction of the task (e.g., via Lagrange multipliers). In the fol-lowing, a strict definition of decoupled task goal with respectto UAVs and UGVs is given from a view of optimization.

Definition 1: For a minimization (or maximization) task goalfunction J = ∫ T

0 L(XA, UA, XG, UG|E)dt+V(XA(T), XG(T)|E),it can be said that the task goal is decoupled with respectto UAVs and UGVs if the following two conditions aresatisfied.

1) The task goal can be decomposed into two independentoptimization sub-goals with respect to UAVs and UGVsas follows:

JA =∫ T

0LA(XA, UA|E)dt + VA(XA(T)|E)

JG =∫ T

0LG(XG, UG|E)dt + VG(XG(T)|E)

where JA and JG are sub-goal functions which are onlyrelevant to the states and control inputs of UAVs andUGVs, respectively.

2) The goal function of the whole task J can be writtenas J = F(JA, JG), and there is a positive correlation ornegative correlation between J and JA (JG) under theassumption that JG(JA) remains constant.

The first condition ensures that the UAVs (UGVs) canmake their decisions irrespective of the control inputs andstates of UGVs (UAVs), and the second condition ensuresthat the overall goal function of the task can be improvedby independently optimizing two sub-goals. In these cases,UAVs and UGVs make their own decisions independently,and approaches developed for homogeneous vehicles can beeasily applied for both of them. Obviously, the decouplingcharacteristic of task goals can greatly ease the difficultyof problem-solving. Unfortunately, the task goal cannot bedecoupled in many cases.

C. Decision-Making: Centralized Decision-Making VersusDecentralized Decision-Making Versus HybridDecision-Making

It is concerned with the way of decision-making amongUAVs and UGVs, which mainly includes centralized decision-making (CD), decentralized decision-making (DD), and hybriddecision-making (HD). In a centralized way, a central vehiclecollects task-related information from both UAVs and UGVsand makes decisions for all members. Although higher com-munication and computation load on the central vehicle occurs


in centralized approaches, the optimality of solution can beguaranteed in theory. In contrast, if a decentralized schemeis adopted, each vehicle has autonomy and makes its owndecisions by only using information of its neighbors. Hence,decentralized approaches have better robustness and scalabilitythan centralized ones at the loss of optimality of solution.

Both kinds of approaches (centralized and decentralizedones) have their own advantages, and are suitable for certainsituations. For small-scale systems, centralized approaches arelikely a better choice while decentralized approaches are fit forlarge-scale systems. In HD approaches, both centralized anddecentralized approaches are applied. Hybrid approaches canbe implemented in a variety of ways. The simplest imple-mentation can be that both UAVs and UGVs make their owndecisions in a centralized way, and then the leader of UAVsand the leader of the UGVs negotiate to achieve a consen-sus decision. HD approaches combine the advantages of bothcentralized ones and decentralized ones, which can achievea better tradeoff between solution quality and time cost fordecision-making.

Remark 1: The first two axes “functional role” and “taskgoal” are concerned with the interrelationship between UAVsand UGVs, while the third one “decision-making” is concen-trated on the decision-making manner of the whole system.The main reasons why we select the three axes lie intwo aspects. On one hand, the three axes mentioned hereare not meant to be exhaustive, but are broad enough tocharacterize most of current UAGVSs. On the other hand,corresponding optimization models and solving strategies forUAVs and UGVs can be determined once the properties ofa UAGVS along the three axes are identified. So, the taxon-omy along with specifically designed axes provides a usefultool to describe different kinds of UAGVSs under a general-ized optimization framework, which benefits a comprehensiveunderstanding and general analysis of UAGVSs.

IV. ANALYSIS ON REPRESENTATIVE TYPES OF UAGVSS

In this section, some representative types of UAGVSs fromthe taxonomy proposed in Section III are discussed. We denotea particular type of UAGVSs by a triple of two-letter abbre-viations drawn from Section III (sometimes may only includeparts of the axes for classification). For each kind of UAGVS,some existing research results are also presented and analyzed.Finally, some common issues involved in various UAGVSs arediscussed.

A. Representative Types of UAGVSs

1) Same Functional Role and Decoupled Goal: In this typeof UAVGS, UAVs, and UGVs play the same functional role,and they act as mobile sensors or mobile actuators. In thiscase, UAVs and UGVs can be viewed as two independentsub-systems and independently make their own decisions toachieve decoupled sub-goals.

For example, consider the problem of monitoring a givenarea including both land and sea where both UAVs and UGVsact as mobile sensors. The UGVs are appointed to monitorthe part of the land while UAVs are for the part of the sea.

Control inputs of UAVs and UGVs can be derived throughsolving two independent optimization problems. In this sce-nario, the optimization function can be simply defined as(other forms could be used){

UA = arg min JA = w1 · ∫ T0 EA(t)dt + w2 · tAf

UG = arg min JG = w1 · ∫ T0 EG(t)dt + w2 · tGf

where EA(t) and EG(t) denote the energy consumed by UAVsand UGVs during the task, tAf and tGf are the task comple-tion time for UAVs and UGVs, respectively, and w1 and w2are weighting coefficients. In this case, algorithms developedfor homogeneous robot can be directly applied to solve thecorresponding optimization problem with respect to UAVsand UGVs.

A typical application of this kind of UAGVS is shown inthe work of Tanner [40] who developed a switched cooperativecontrol scheme to coordinate groups of ground and aerial vehi-cles for the purpose of locating a moving target in a given area.In this paper, the task goals of UAVs and UGVs are decou-pled, with UGVs building a guarding formation and UAVsuniformly scanning the enclosed region.

2) Different Functional Role and Decoupled Goal: Due tothe strong complementarities between UAVs and UGVs, eachkind of vehicles can improve its efficiency in completing taskswith the assistance of the other in many cases. This type ofUAGVS primarily consists of the following two situations.

a) UGVs act as mobile acutators, UAVs act as mobilesensors, decision makers, or auxiliary facilities: AlthoughUGVs can perform increasingly sophisticated tasks, the restric-tions of speed, view scope, communication and so on, some-times limit their application ranges. In this case, UAVs can aidUGVs to complete tasks more efficiently. Here, we primarilyfocus on the following three scenarios.

i) UGVs act as mobile acutators and UAVs act asdecision makers: Michael et al. [25] developed an abstrac-tion method for the team of ground robots, which allows theaerial vehicle to control the team without any knowledge of thespecificity of individual vehicles. An ellipsoidal approximationof the shape of the team formation is adopted, and the posi-tion and orientation of the team in the plane are also defined.The controllers are derived that allow the team of robots tomove in formation while avoiding collisions and respectingthe abstraction commanded by the aerial vehicle.

Chaimowicz and Kumar [26] addressed the problem ofdeploying groups of tens or hundreds of UGVs in urbanenvironments. The results presented in [26] were extended tothe scenario with multiple UAVs and multiple UGV groups.These UAVs are taken as “aerial shepherds,” and a probabilis-tic approach based on expectation maximization is proposedto assign the shepherds to the UGV groups. In the processof movement, these aerial shepherds control the splitting andmerging of the UGV swarm into groups according to theenvironment information.

Rao et al. [27] proposed a controller which can generatecontrol strategies for a UGV with feedback from overheadimage obtained by a UAV. Similarly, Frietsch et al. [28] usedan unmanned micro aerial vehicle (MAV) to improve the navi-gation solution of a UGV. The MAV detects the UGV from the


image acquired by an on-board camera and further estimatesthe location and yaw angle of the UGV. Then, the informationis transmitted to the UGV and utilized to improve its naviga-tion. The navigation ability provided by UAVs to UGVs caneffectively expand the application ranges of UGVs in complexenvironments, especially in GPS-denied cases.

With the exploration and exploitation of ocean movingforward, unmanned underwater vehicles (UUVs) have beenextensively adopted in ocean operations. Sujit et al. [29] pro-vided a mechanism to coordinate UUVs and a UAV to performan ocean exploration mission. The main mission for the UAVis to acquire information from UUVs and assign tasks to them.Different from UGVs, UUVs can communicate with the UAVonly when they surface. Hence, the UAV needs to considerthe uncertainty of the surfacing of a UUV when it generatesa new mission plan for the UUV.

Using UAVs to provide effective management and controlsupport to UGVs not only improves the capability of UGVsto perform tasks in complex environments, but also providespossibilities to deploy large-scale UAGVSs.

ii) UGVs act as mobile actuators and UAVs act asmobile sensors: UAVs can fast capture environment and taskinformation because of their quick speed. So, using UAVs totransmit gathered environment and task information to UGVscan remarkably aeccelerate the response of UGVs.

MacArthur et al. [30] concentrated on the detection and dis-posal of mines using cooperative UAVs and UGVs. The UAVfirst surveys the target area and creates a map of the area.The map was transmitted to the base station and processedto extract the locations of the targets. Then, correspondingwaypoints were generated for the UGV to navigate. Then, theUGV proceeded to each of the targets and disposed the ord-nances. Cheung and Grocholsky [31] used a PackBot UGVand a small Raven UAV to pursuit and track a dynamic target.The UAV first surveys the area and localizes the target. Then,the Raven-PackBot team will collaboratively pursue the targetto maintain track on the target.

Ramirez et al. [32] presented a sea rescue system based ona coordinated team of a sensing/monitoring UAV and a rescu-ing unmanned surface vessel (USV). First, an artificial neuralnetwork (ANN), trained before the rescue, is used to pre-dict the castaway location using the map of the sea wind andcurrents. Based on the predicted location, the UAV searchesfor the castaways using another ANN trained with search-ing behaviors. Then, the USV employs particle filtering toestimate the castaway location using all the predicted andmeasured data.

iii) UGVs act as mobile actuators and UAVs act asauxiliary facilities: As mentioned in Section III-A, an auxil-iary facility can provide energy, communication, computation,and other services to chief agents. For example, maintainingcommunication connection among all UGVs is one of the pre-conditions to efficiently accomplish specified tasks. However,short communication range of UGVs greatly limits their oper-ations in large fields. Using UAVs as auxiliary facilities toprovide communication links between UGVs is a promisingapproach. The key problem in this case is how to place theseUAVs at proper positions so that all UAVs form a connected

network and each UGV can communicate with at least oneUAV. The problem can be solved by optimizing the posi-tions of UAVs given the following optimization objective andconstraints:

min J = H(XA, XG)

s.t. r( ) = 1

∀j = 1, 2, . . . , NG, d(

xGj , xA

Kj

)≤ min

(rG

j , rAKj

)

where H is a measure concerning the performance of the net-work such as the energy cost, reliability, and so on. r( ) isan evaluation on the connectivity of the graph constructedby UAVs whose detailed definition can be found in the lit-erature [33]. Kj, rG

j and rAKj

are defined the same as that inTable II. The first constraint ensures that the network con-structed by UAVs is connected while the second one ensuresthe connectivity between aerial and ground vehicles.

This problem can be solved in either a centralized manneror a distributed manner. Chandrashekar et al. [34] achievedfull connectivity to disconnected ground nodes by dynami-cally placing UAVs to act as relay nodes using a heuristicalgorithm. The authors considered both the minimal numberof UAVs required to provide full connectivity and their corre-sponding locations. One distributed approach is given in thework of Gil et al. [22], who took the communication-link qual-ity into account and developed a gradient-based distributedcontroller to position a team of aerial vehicles so as to opti-mize the communication-link quality. Using UAVs as relays toprovide communication service to UGVs remarkably extendsthe operational range of UGVs.

b) UAVs act as mobile sensors and UGVs act as auxil-iary facilities: Small UAVs, especially rotary-wing ones withvertical takeoff and landing abilities, are usually limited bytheir payload capability which limits the amount of batteriesthat can be carried as well as the operating range of UAVs. Onthe contrary, UGVs can take more payloads. Therefore, onepossible solution is the application of UGVs for the purposeof recharging long-range transportation for UAVs.

Voos and Bou-Ammar [35] proposed a novel nonlinearcontroller for rotary-wing aerial vehicles to track and landon a moving base station. Wills et al. [36] described theautomated UAV mission system for small vertical takeoffand landing (VTOL) UAVs, which can provide forwardstaging, refueling, and recovery capabilities for the VTOLUAV through a host UGV. In this case, UGVs serve aslaunch/recovery platforms and service stations.

Tokekar et al. [37] studied the problem of coordinatinga symbiotic UAV and UGV system to collect data for a preci-sion agriculture application. Measurements collected by a UAVand a UGV are used for estimating Nitrogen levels acrossa farm field. These estimates in turn guide fertilizer applica-tion. Taking into account the limited voyage of the UAV, theauthors used a symbiotic UAV/UGV system where the UGVcan mule the UAV to various locations. Simulation resultsdemonstrated that, compared with the scenario using UAVsonly, UAV/UGV coordination can greatly raise the operationalefficiency.


Saska et al. [38] built a UAGVS for periodical surveillancein indoor environments. During the mission, a UGV followsa preplanned path and sequentially scans the places of interestby its sensors. Once a position of interest cannot be reached bythe UGV due to environment constraints, a UAV is launchedfrom the UGV to perform the inspection. After the UAV com-pletes the inspection, it returns to the UGV helipad, and theycontinue toward the next place. The use of UAVGS not onlyovercomes the limitation of UAVs in their short voyage, butalso reduces the susceptibility of UGVs sensing ability toenvironmental factors.

Remark 2: In SR–DG and DR–DG types of UAGVSs, UAVsand UGVs have decoupled task sub-goals, which means thatthey can make their own decisions independently. If UAVsand UGVs make their own best decisions, then the wholetask goal can be reached optimally. In this case, simultaneousconsideration of the decision-making for UAVs and UGVs isneedless. Different kinds of decision-making approaches, suchas centralized, decentralized, and hybrid approaches, can beadopted within UAVs and UGVs according to specific taskrequirements.

3) Same Functional Role, Coupled Goal, and CentralizedDecision-Making: In most cases, UAVs and UGVs act as thesame functional role and share a coupled task goal where tightcoordination mechanism should be designed to complete thetask effectively. An intuitive and simple way to address theproblem is using a centralized approach, where information ofboth UAVs and UGVs is collected, and decisions for UAVs andUGVs are made by one vehicle or by a central controller. Thecontrol inputs of UAVs and UGVs can be derived via solvingthe following optimization problem:

[UA, UG] = argmin J =∫ T

0L((XA, UA, XG, UG)|E)dt

+ V(XA(T), XG(T)|E).

Although CD approaches can find the best solutions tocorresponding optimization problems in theory, their weakrobustness, poor scalability, and the extended period of timerequired to obtain final solutions usually limit their appli-cations. Hence, CD approaches are prone to be used forinitial planning at the start or be used for online planningfor small-scale UAGVSs.

One application of this system is presented in the workof Phan and Liu [41], who conducted research on the taskassignment problem in wildfire detection and fighting usingcooperative UAVs/UGVs platforms. A cooperative controlframework for a hierarchical UAVs/UGVs platform is pre-sented. In the top-most level, an airship is used as a mobilemission controller to perform mission planning, assign-ment and system-level decision-making for both UAVs andUGVs. Then, the task assignment problem is formulated asa pure integer linear program which can be easily solved byMATLABs optimization toolkit. The algorithm proposed fortask allocation is typically a centralized one whose effective-ness highly relies on the airship.

4) Same Functional Role, Coupled Goal, and DecentralizedDecision-Making: For coupled task goals, CD approaches arecapable of obtaining high-quality solutions. However, they are

sometimes computationally unfeasible and unreliable espe-cially for large-scale systems. Meanwhile, in view of theconstraints on physical systems such as limited computationalresources and energy, limited wireless communication rangesand bandwidths, the decentralized approaches have been payedmore attention and widely appreciated. In SR–CG–DD sys-tems, the decision-making for UAVs and UGVs can be givenas follows:

UA = argmin J = J(XA, UA, XG, UG

)UG = argmin J = J

(XG, UG, XA, UA

)

where XG and UG (XA and UA) are the states and control inputsof UGVs (UAVs) received by UAVs (UGVs) throughout thecommunication network between UAVs and UGVs.

Roughly speaking, DD manners can be mainly splitted intotwo categories: emergent and intentional coordination.

a) Emergent coordination: It is rooted in the observa-tion of large-scale animal behavior such as flocks of birdsand schools of fishes which can travel information to defendthemselves against predators. Applying the idea of animalcoordination to multivehicle coordination, intelligent activitiescan be achieved through simple and distributed agent-to-agentinteractions where each vehicle makes its own decision onlybased on the information of its neighbors [42]–[48].

In emergent coordination manners, relatively simple controllaws executed by vehicles result in emergent group behav-ior without the need for a complex coordination architecture.These kind of systems are well suited for large-scale devel-opment, but they are usually applied to very simple taskssuch as flocking [42]–[44], formations [45], coverage [46],search [47], rendezvous [48], and so on.

b) Intentional coordination: In intentional coordinationapproaches, robots cooperate with purpose, and the com-mon objective is optimally achieved often through task-relatedcommunication and negotiation between vehicles. It can beasserted that, as compared with emergent coordination man-ners, intentional coordination manners are better suited toperform complicated tasks, and have higher requirements ofvehicles’ performance such as computational and commu-nication ability. Here, we examine the intentional coordi-nation approaches for a typical problem in MRS: motioncoordination.

An important problem in MRS is the motion coordi-nation for multiple vehicles through a shared workspace,which requires vehicles to move through the workspace ina coordinative way so that fleet-level task objectives areoptimized while avoiding collisions between vehicles. Themost used optimization criteria include the minimization oftotal vehicle path lengths, the minimization of consumedtime and energy, and so forth. Substantial literatures formotion coordination are based on intentional coordination,such as the idea of cooperative variables and cooperativefunction [49], game theory-based approach [50], sequentialdecision approach [51], and so on.

5) Same Functional Role, Coupled Goal, Hybird Decision-Making: For a large-scale UAGVS, CD approaches are


usually computationaly infeasible while DD approaches con-sume more time and the optimality of solutions cannot beguaranteed. To achieve a better tradeoff between optimalityand efficiency, a promising way is using HD approaches,which incorporates the advantages of both centralized anddecentralized ones.

HD can be achieved in a variety of ways according to dif-ferent types of system organizations. A typical way is that, allvehicles are divided into some small-scale sub-teams, and eachsub-team selects one vehicle as its leader. Each leader makesdecisions for all members in this sub-team and coordinatesthe behaviors of different sub-teams using a decentralizedapproach.

One conceivable application scenario of this type of systemis wildfire fighting with multiple fire-points in a large field.In such a dynamic, uncertain, and real-time environment, howto realize the real-time planning and control is a very chal-lenging problem. Managing the whole system on a team levelcan promote the efficiency of making decisions and achievea better tradeoff between solution quality and solution time.It is assumed that both UAVs and UGVs act as mobile actua-tors, and all vehicles are divided into several sub-teams. Eachsub-team consists of a certain number of UAVs and UGVs,and one vehicle (e.g., a UAV) is selected as the leader of thissub-team. Each sub-team, represented by the leader, partici-pates in the decision-making as a whole. During the mission,all the leaders constitute a connected network. Each leadermakes decisions for all members in the sub-team, and coor-dinates its behaviors through negotiation with other leaders ina decentralized way. Within each team, a centralized approachis adopted to solve a small-scale optimization problem, whichguarantees the quality of solutions to some extent. Among allsub-teams, decentralized coordination approaches improve therobustness and scalabality of the whole system.

B. Common Issues Involved in Various UAGVSs

In the above, some representative types of UAGVSs are pre-sented, and their corresponding optimization models are alsoestablished. In this section, two common issues, computationalcomplexity and real-time peroformance, involved in variousUAGVSs are discussed in detail.

1) Computational Complexity: Taking the SR–CG–CD sys-tem as an example, the decision-making for the UAGVS isformulated as follows:

[UA, UG] = argmin

{J =

∫ T

0L((XA, UA, XG, UG)|E)dt

+ V(XA(T), XG(T)|E)

}

s.t. f (XA, UA, XG, UG|E) = 0

g(XA, UA, XG, UG|E) ≥ 0.

The solution of the problem should be expressed as con-tinuous control inputs of UAVs and UGVs over the wholetime horizon. For ease of calculation, the whole time hori-zon is decomposed into a series of small time horizons, anda constant control input is generated during the period ofeach small time horizon. Denote the duration of each small

TABLE IIICOMPUTATIONAL COMPLEXITY OF REPRESENTATIVE UAGVSS

time horizon by �T . So, the total number of optimizationvariables is �T/�T · (NAmA + NGmG). Assume that eachvariable is discretized into δ levels. Then, the total num-ber of possible solutions is (�T/�T · (NAmA + NGmG))δ .Obviously, the time for evaluating a solution is approxi-mate to t′ = tobjective(NA, NG) + tconstraint(NA, NG), wheretobjective(NA, NG) and tconstraint(NA, NG) denote the time for cal-culating the objective and constraints, respectively, and bothof them are functions with respect to the number of UAVsand that of UGVs. So, the total time for evaluating all solu-tions is (�T/�T · (NAmA + NGmG))δ · t

′. In other words,

the worst-case computational complexity of the optimizationproblem using enumeration methods is O((�T/�T · (NAmA+NGmG))δ · t

′).

The analysis on the computational complexity ofSR–CG–CD system can be extended to other types ofsystems in a similar way. It is noteworthy that differentschemes for implementing DD or HD may result in differentcomputational complexity. For example, as a possible wayof the DD, the sequential decision-making by which agentsmake decisions one by one according to certain prioritiesgives rise to computational complexity of

O(NA · (�T/�T · mA)δ · t′ + NG · (�T/�T · mG)δ · t′

).

As illustrated in Definition 1, for a decoupled task goal,UAVs and UGVs have independent individual sub-goals.Denote by t′A and t′G the time of calculating their individ-ual sub-goals, respectively. The result of complexity analysison typical UAGVSs is presented in Table III where DD isachieved by sequential decision-making. For HD, it is assumedthat the centralized manner is used for interUAVs (interUGVs),and the decentralized manner is adopted between UAVsand UGVs.

2) Real-Time Performance: In practical applications, real-time performance is an important index reflecting the systemperformance especially for online decision-making. The real-time performance of doing mathematical optimization haschanged very much thanks to the developing of computersand computing algorithms.

It is assumed that, at each decision time, the maximumallowed time for producing the final solution is Tmax. To satisfythe real-time requirement, the maximum number of solutionevaluations in the optimization algorithm must satisfy thefollowing constraint:

Nmax · (t + t′′) ≤ Tmax


where t can be t′, t′A, or t′G, and t′′ denotes the time con-sumed by the operations of the optimization algorithm itself,such as the generation of a solution, comparison between twosolutions, and so on.

The above analysis shows that problem formulations, algo-rithms, and computer performance all have important impactson the real-time performance of the system. Next, we will talkabout some commonly used measures to improve the real-timeperformance.

a) Choose appropriate computation platforms with desir-able performance: Given a specific optimization formulationand optimization algorithm, the computation platform shouldbe selected to satisfy

t + t′′ ≤ Tmax

Nmax.

In other words, a platform with better computation abilityallows more time to be used to find a better or even the bestsolution.

b) Simplify the problem formulation: Besides the opti-mization scale of the problem, the actual problem formulationitself also has a significant impact on the real-time perfor-mance of the system. Taking the optimization objective fortarget tracking problem shown in Table II as an example,the objective function is the sum of integral terms, and thedetection probability p(D|xA(t), xG(t), E) is determined bya variety of factors such as the models of onboard sensors,the relative position and heading between the UAV (UGV)and the target, the occlusion of light of the sight by obsta-cles like buildings, and so on. In addition, the real kinematicmodels of UAVs and UGVs are usually very complex. Itwill consume large amount of time by the computer to cal-culate their accurate values. To reduce calculation burden,some equivalent presentation can be adopted. For example,the relative position between the UAV (UGV) and the tar-get is the most important factor in the detection probabilityintuitively, so the accurate calculation of detection probabil-ity can be replaced by a function of the relative positionbetween the vehicle and the target. In addition, the integralterm can be calculated approximately by the sum of somecritical sampling points. For the kinematic models of UAVsand UGVs, some simplified models such as Dubins car modelfor fixed-wing UAVs and mass point model for UGVs canbe used.

c) Given the specific platform and the optimization for-mulation, corresponding optimization algorithm should beselected and designed: To solve the optimization prob-lems, a large number of algorithms including constructivemethods and search methods can be used as candidates.Constructive methods, often referred to as constructive heuris-tics, generate feasible solutions by some heuristic rules whichmay be extracted from the problem structure or domainknowledge [52]. Search methods rely on samplings in solutionspace to find a better or even the best solution. Enumeration,random sampling, traditional gradient-based search, improve-ment heuristics (e.g., local search methods), various meta-heuristics including trajectory search (e.g., tabu search), and

population-based search (e.g., genetic algorithm and differen-tial evolution) all can be classified as search methods [53].Constructive methods usually feature simple operations butcannot guarantee optimality. In contrast, search methods, espe-cially meta-heuristics, are effective for problems with complexlandscapes. However, they usually consume much more timethan constructive methods.

According to the real-time constraint, the maximum num-ber of solution evaluations for the optimization algorithm mustbe less than Tmax/(t′ + t′′), which provides us a principleto select a suitable algorithm. For example, if Tmax/(t′ + t′′)is pretty small, it is impossible to rely on population-based search to find the best or even a satisfactory solutionwithin very limited time. In this case, constructive meth-ods (e.g., some greedy heuristics) can be specially chosen ordesigned.

V. DIFFERENT TYPES OF UAGVSS FOR TYPICAL

APPLICATION: TARGET TRACKING

In the above, different types of UAGVSs have beenexplored, and a general optimization framework has beendeveloped. In this section, we make a detailed discussion onthe diverse possibilities of achieving target tracking by usingUAGVSs according to the proposed taxonomy.

The reasons why UAVs and UGVs are coordinated to trackthe evaders lie in two aspects. First, from illustration above,UGVs are suitable for accurately locating ground targets butthey do not have the ability to move rapidly and see throughobstacles such as buildings and fences while UAVs can makeup these deficiencies. Second, fusing data from different vehi-cles which are described by distinct types can effectivelyremedy the drawback of low accuracy and reliability causedby using only one kind of vehicles. For ease of analysis, itis assumed that one UAV and one UGV are involved to trackone evader. A variety of models can be built to formulatethis problem. Here, we cast the problem in a probabilisticframework whose objective is to detect the evader with thebiggest probability. In the following, we first give some basicdefinitions.

Given the evader location xT(t), the UAV locationxA(t), and the UGV location xG(t) at time t, we letp(D/xA(t), xG(t), xT(t), E) represent the joint detection prob-ability of the target by the UAV and the UGV, where D isan event that the evader is detected correctly, and E indi-cates environmental factors including obstacles, buildings, andso on. These environmental factors make great effect on thefeasible motion space of vehicles, and occlusion of the lineof sight between vehicles and targets should be taken intoaccount in the planning. Then, the marginal detection proba-bility given the UAV location and the UGV location at time tcan be given by

p(

D/xA(t), xG(t), E)

=∑xT (t)

p(

D/xA(t), xG(t), xT (t), E)

· p(

xT (t))

where p(xT(t)) means the probability that the evader occu-pies xT(t) which can be estimated by a presupposed motionmodel. Here, the motion state of the evader is described by


using dynamic occupancy grids, which separate the environ-ment into a grid of equally spaced cells representing varyingbeliefs about the evader state. The motion of the evadercan be estimated by a predefined model such as second-order Markov chain model [20], random walk model [54],and so on.

A. Different Coordination Patterns for TargetTracking by UAGVS

1) DR–DG–DD System for Target Tracking: In some spe-cial cases, the final objective is to capture the evader or thetarget. In these systems, the main mission of the UAV is track-ing the evader and transmitting obtained information to theUGV while the UGV moves toward the target and captures it.In this kind of UAGVS, the UAV acts as mobile sensor andthe UGV acts as mobile actuator.

2) SR–DG–DD System for Target Tracking: The coordina-tive target tracking by UAVs and UGVs is essentially a prob-lem with a coupled goal. However, some researchers solve itwith decoupling methods like the work of Owen et al. [20], inwhich the UAV and UGV track the target independently andthere is no information exchange between them. In this case,the control inputs of the UAV and UGV can be derived bysolving two separate optimization problems as follows:

uA = arg max JA =∫ T

0P(

D∣∣xA(t), E

)dt + wA ·

∫ T

0CA(t)dt

uG = arg max JG =∫ T

0P(

D∣∣xG(t), E

)dt + wG ·

∫ T

0CG(t)dt

where uA and uG denote the control input sequence of the UAVand UGV over the whole horizon, respectively. P(D|xA(t), E)

and P(D|xG(t), E) are the marginal detection probability bythe UAV and the UGV, respectively. CA(t) and CG(t) are theenergy cost of the UAV and UGV, respectively. The drawbackof the approach is that both UAV and UGV plan their ownpaths using only the information gathered by itself.

3) SR–CG–CD System for Target Tracking: Tight coordi-nation between the UAV and UGV can significantly improvethe efficiency of the whole system. A simple and direct wayto achieve a coupled task goal is using a centralized approachwhere the control inputs of the UAV and UGV are optimizedby one vehicle (the UAV or UGV) or by a central station

[uA, uG

]= arg max J =

∫ T

0p(

D∣∣xA(t), xG(t), E

)dt

+ wA ·∫ T

0CA(t)dt + wG ·

∫ T

0CG(t)dt.

4) SR–CG–DD System for Target Tracking: In light of thelarger computation burden in centralized approaches, a fea-sible method is to break the original problem into smallersub-problems which can be solved by individual vehicles.A variety of decentralization mechanisms such as the game-based approaches [50], sequential decision-making [51], andso on, can be applied.

B. Simulation

Here, a simple simulation is provided to intuitively demon-strate the differences of diverse UAGVSs. Two representativetypes of UAGVSs, SR–DG–DD and SR–CG–CD, are builtto track the target by a UAV and a UGV. Due to uncertainmovement of the target, decision-making over the whole timehorizon is needless. Receding horizon optimization is adoptedto generate h steps look-ahead control inputs for the UAV andUGV. According to the discussion on real-time performance inSection IV, simplified problem formulations based on reced-ing horizon optimization strategy for the UAV and UGV arepresented as follows.

For SR–DG–DD system, the UAV and UGV have respec-tive optimization sub-goals and make their own decisionsindependently. Intuitively, maximum detection probability canbe achieved by the UAV (UGV) through keeping a certaindistance to the target. So, the accurate calculation of detec-tion probability can be replaced by an equivalent functionwith respect to the relative distance between the UAV (UGV)and the target. At the meantime, the Dubins car modelis adopted to approximate the kinematic of the UAV. Theoptimization formulation for the UAV at time tk is givenas follows:

uA(k) = arg max

⎧⎨⎩JA =

h∑j=0

e−|d(PA(tk+j·�t),PT (tk+j·�t)

)−de|⎫⎬⎭

s.t.

⎧⎪⎨⎪⎩

xA = vA cos(θA

)yA = vA sin

(θA

)vA = 0, θA = vA

rA uA, uA ∈ [ − 1, 1]

where PA(tk + j · �t) and PT(tk + j · �t) denote the positionsof the UAV and the target at time tk + j · �t, respectively,and d(PA(tk + j · �t), PT(tk + j · �t)) denotes the Euclideandistance between the two positions. (xA, yA), vA, θA, and rA

denote the position, velocity, heading, and turning radius ofthe UAV, respectively. �t denotes the duration of each timehorizon, and de denotes the expected distance between theUAV and the target.

Similarly, the optimization model for the UGV is formulatedas follows:

uG(k) = arg max

⎧⎨⎩JG =

h∑j=0

e−|d(PG(tk+j·�t),PT (tk+j·�t)

)−de|⎫⎬⎭

s.t.

⎧⎨⎩

xG = vG cos(θG

)yG = vG sin

(θG

)vG ∈ [

vGmin, vG

max

]

where vGmin and vG

max denote the minimal and maximal veloc-ities of the UGV, respectively.

For the SR–CG–CD system, the UAV and UGV sharea common objective of maximizing the marginal detectionprobability. From the perspective of data fusion, the UAV andUGV are expected to keep themselves on different sides of thetarget with a certain distance to enhance the complementarityof detecting the target. Analogously, a heuristic optimization


Fig. 5. Results of target tracking for the UAGVS with different systemtypes. The result for the UAGVS with system types (a) SR–DG–DD and(b) SR–CG–CD.

objective is adopted. The optimization model for the UAGVSis formulated as follows:

[uA(k), uG(k)

]= arg max

⎧⎨⎩J =

h∑j=0

e− min(d1,d2)

⎫⎬⎭

d1 = d(

PA(tk + j · �t), PR(tk + j · �t))

+ d(

PG(tk + j · �t), PL(tk + j · �t))

d2 = d(

PG(tk + j · �t), PR(tk + j · �t))

+ d(

PA(tk + j · �t), PL(tk + j · �t))

s.t.

⎧⎪⎪⎨⎪⎪⎩

xA = vA cos(θA

)yA = vA sin

(θA

)θA = vA

rA uA, uA ∈ [ − 1, 1]vA = 0⎧⎨

⎩xG = vG cos

(θG

)yG = vG sin

(θG

)vG ∈ [

vGmin, vG

max

]where PR(tk + j · �t) and PL(tk + j · �t) are expected posi-tions of the UAV and UGV at time tk + j · �t, which arelocated at the right and left sides of the target with an expecteddistance de.

The algorithm described above is simulated in MATLABand implemented on a PC with 2 GB RAM and 2.1 GHzPentium dual-core CPU under Windows XP operating sys-tem. In the simulation, the model parameters of the UAV andthe UGV are set to: vA = 5 m/s, rA = 4 m, vG

min = 0,and vG

max = 3 m/s. It is assumed that the target is located atthe position (20, 20) initially, and then it moves along theblack lines shown in Fig. 5 with a constant velocity 2 m/s.In the algorithm, the target position in the future is esti-mated according to current position with unchanged velocityand heading. In addition, differential evolution algorithm isadopted as the optimization tool, and the relevant parametersare set to: NP = 5ND, F = 0.5, and Cr = 0.2 [19], where ND

is the number of the decision variables. The maximal num-ber of evolution generations is set to 50 for the UAV andthe UGV of SR–DG–DD system, and 100 for the UAGVS ofSR–CG–CD system. The length of prediction horizon and theduration of each time domain are set as h = 4 and �t = 3 s.

Fig. 5 presents the results of cooperative target trackingby UAGVS with two different types of systems. The initialpositions of the target, UAV, and UGV are indicated in thefigure. As shown in Fig. 5(b), there exists obvious coordina-tion between the UAV and UGV, and they keep themselveson the different sides of the target for most of the time forthe SR–CG–CD system. On the contrary, there is no obvi-ous synergy behaviors exhibited by the UAV and UGV forSR–DG–DD system. Additionally, for the SR–DG–DD sys-tem, the mean optimization time for the UAV and UGV ateach decision point is 0.21 s and 0.20 s, respectively. In con-trast, the mean optimization time is 0.85 s for the SR–CG–CDsystem.

Although the example illustrated here only involves oneUAV and one UGV, it sufficiently demonstrates how differentcoordination mechanisms can be adopted to solve a specificproblem, and different types of systems may exhibit differentperformane. The coordination pattern can be selected accord-ing to specific task demand. For example, as to meet thedemand of capturing the target, DR–DG–DD system should beselected. But for detection purpose, SR–DG–DD, SR–CG–CD,and SR–CG–DD can be selected as candidates. In practice, thecoordination behavior between UAV and UGV is expected,however, obatining a satisfactory solution to the optimizationproblem in this case usually consumes more time especiallyfor large-scale problems. So, besides the task demand, real-time performance is another important factor when selectinga coordination pattern.

VI. CONCLUSION

UAV/UGV coordination has attracted worldwide attentionbecause of its great application value. To date, the majorityof research on UAGVSs has been concentrated on specificapplications and has been validated by proof-of-concept meth-ods or by simulations. These research efforts are undeniablyvaluable, since they demonstrate that successful coordinationbetween UAVs and UGVs provides a promising approach foraccomplishing complex tasks. However, to date there is no sys-tematic analysis on general coordination between UAVs andUGVs. The goal of this paper is to identify the kernel elementsfor the coordination of UAVs and UGVs and their possiblerealizations by building a taxonomy for differentiating diverseconfigurations of UAGVS.

In spite of diverse realization schemes for coordinatingUAVs and UGVs, the decision-making for vehicles in eachcase can be viewed as optimization problems with the aim ofdetermining the control inputs for UAVs and UGVs to achievetheir goals in an optimal way. Here, to simplify the represen-tation, the control inputs of vehicles are optimized directlyaccording to the task goal. In practice, due to the complexityof the dynamics of vehicles, optimizing the control inputs ofvehicles directly induces large computation burden. Therefore,the optimization problem is usually decomposed into plan-ning and control levels. For example, in the target trackingproblem, the path planning for vehicles can be done first, andthen each vehicle determines its control scheme for path track-ing. Although the decomposition-based approaches can reduce


computational complexity, the feasibility of paths for vehiclesshould be considered in the planning stage.

Once the optimization model for a UAVGS is established,corresponding algorithms including intelligent optimizationalgorithms, others from operations research, economics, andso on, can be applied to find a solution to the problem.A remarkable feature of the decision-making for UAGVSs isthe rigorous requirement on real-time performance. Therefore,a desirable tradeoff between computation cost and solutionquality is a requisite in practical use of UAGVSs. Hence,designing efficient algorithms to generate approximate solu-tions or anytime solution which can achieve a better tradeoffbetween solution quality and decision-making time is also animportant topic for the development of UAVGSs.

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Jie Chen (M’09–SM’12) received the B.S., M.S.,and Ph.D. degrees in control theory and control engi-neering from the Beijing Institute of Technology,Beijing, China, in 1986, 1993, and 2000, respec-tively.

He is currently a Professor of Control Scienceand Engineering with the Beijing Institute ofTechnology. His current research interests includecomplicated system multiobjective optimization anddecision, intelligent control, constrained nonlinearcontrol, and optimization methods.

Xing Zhang received the bachelor’s degree fromthe University of Science and Technology Beijing,Beijing, China, in 2009. He is currently pursuing thePh.D. degree with the School of Automation, BeijingInstitute of Technology, Beijing.

His current research interests include intelli-gent optimization, path planning, and multivehiclesystems.

Bin Xin (S’09–M’10) received the B.S. degreein information engineering and the Ph.D. degreein control science and engineering, both from theBeijing Institute of Technology, Beijing, China,in 2004 and 2012, respectively.

He is currently a Lecturer with the Schoolof Automation, Beijing Institute of Technology.His current research interests include search andoptimization, evolutionary computation, operationsresearch, and combinatorial optimization.

Hao Fang (M’15) received the B.S. degree fromthe Xi’an University of Technology, Xi’an, China,in 1995, and the M.S. and Ph.D. degrees from theXi’an Jiaotong University, Xi’an, in 1998 and 2002,respectively.

He held two Post-Doctoral appointments at theINRIA/France Research Group of COPRIN andat the LASMEA (UNR6602 CNRS/Blaise PascalUniversity, Clermont-Ferrand, France). Since 2011,he has been a Professor with the Beijing Instituteof Technology, Beijing, China. His current research

interests include all-terrain mobile robots, robotic control, and parallelmanipulators.

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