Cooperative Multi-Quadrotor Pursuit of an Evader in an Environmentwith No-Fly Zones

Alyssa Pierson1, Armin Ataei1, Ioannis Ch. Paschalidis2, and Mac Schwager3

Abstract— We investigate the cooperative pursuit of anevader by a group of quadrotors in an environment with no-fly zones. While the pursuers cannot enter into no-fly zones,the evader may freely move through zones to avoid capture.Once the evader enters a no-fly zone, the pursuers calculatea reachable set of evader positions. Using tools from Voronoi-based coverage control applied to the evader’s reachable set,we provide an algorithm that distributes the pursuers aroundthe zone’s boundary and minimizes the capture time once theevader leaves the no-fly zone. Robust model predictive control(RMPC) tools are used to control the quadrotors and to ensurethat they always remain in free space. We demonstrate theperformance of our proposed algorithms through a series ofexperiments on KMEL Nano+ quadrotors.

I. INTRODUCTION

In this paper, we propose an algorithm for a group ofquadrotors to cooperatively pursue an evader. The quadrotorsmove in an environment that has a set of “no-fly zones,”which are regions that the quadrotors cannot enter, such asbuildings or forests. The evader can freely move throughthese zones, while the pursuing quadrotors must positionthemselves around the boundary of the no-fly zones, ready topursue the evader when it eventually emerges. Furthermore,the quadrotors can sense the position of the evader when it isin free space, but may not have information about the evaderwhen it is inside a no-fly zone. Our algorithm uses RobustModel Predictive Control (RMPC) tools to always stay aprescribed safe distance away from the no-fly zones whilepursuing the evader, and it adapts methods from Voronoi-based coverage control to position the quadrotors when theevader is inside a no-fly zone. We demonstrate our algorithmin hardware experiments with three quadrotors pursuing amanually-controlled ground robot.

Our algorithm is useful in a number of applications ofemerging importance, such as search and rescue, roboticaerial videography, and security and surveillance. One ex-ample is where the evader is a suspected criminal fleeing thescene of a crime, and the pursuers are police surveillancedrones. The pursuers track the suspect as it flees, but alsomust avoid buildings, bridges, trees and other environmentalobstacles. If the suspect enters a building where the drones

This work was supported in part by ONR grants N00014-12-1-1000and N00014-10-1-0952, by NSF grants CNS-1330036, IIS-1350904, CNS-1239021, and IIS-1237022, and by the ARO under grants W911NF-11-1-0227 and W911NF-12-1-0390. We are grateful for this financial support.

1 Dept of Mechanical Engineering, Boston University, Boston, MA,{pierson,ataei}@bu.edu

2 Dept of Electrical and Computer Engineering, Boston University,Boston, MA, yannisp@bu.edu

3 Dept of Aeronautics and Astronautics, Stanford University, Stanford,CA, schwager@stanford.edu

0 0.5 1 1.5 2 2.50

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3

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3

(b) 0 0.5 1 1.5 2 2.50

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(d)

Fig. 1: Simulation of three pursuers executing the no-fly-zoneplanning algorithm. At each step, the pursuers move to thenearest feasible point to the centroids of the Voronoi cells.

cannot follow, our algorithm will position the drones aroundthe building so that they can continue tracking the suspectonce it re-emerges, illustrated by Figure 1. Other settingswhere the algorithm may be useful include tracking a lostperson or endangered animal in a park, or the subject of asports film such as a snowboarder or mountain biker.

When the evader is in free space, the quadrotors pursueit directly using an RMPC controller to guarantee that theydo not enter the no-fly zones. When an evader enters a no-fly zone, we adopt a three-part strategy for the quadrotors:(i) compute the reachable set of the evader, (ii) tessellatethe reachable set with a centroidal Voronoi tessellation, then(iii) drive the quadrotors to points on the perimeter of theno-fly zone that are as close as possible to the centroidsof the tessellation. The quadrotors may not have access tothe evader’s position inside the no-fly zone, so the evader’sreachable set is a conservative estimate that grows in time,eventually filling up the no-fly zone. The RMPC controlapproach is again used to keep the quadrotors safely outsideof the no-fly zones at all times as they navigate to theirpositions on the perimeter of the no-fly zone.

This work brings together elements of the authors’ previ-ous work in Robust Model Predictive Control for a singlepursuer, and in Voronoi-based coverage control for sensornetworks. In a previous work [1], we consider a singlepursuer, and assume that once the evader enters a no-fly zone,the pursuer still has full information about its location. Incontrast, here we consider multiple pursuers, and we assumethat once the evader enters a no-fly zone, the pursuers maynot have information about its position.

While there has been extensive research on path planningand obstacle avoidance for mobile robots [2], [3], most algo-rithms rely on simple approximations of the robot dynamics[4], [5], [6], [7]. If the underlying robot is highly nonlinearor is subject to uncertainties, these approximation may resultin controller instability and collision with obstacles. In [1],we proposed a robust MPC using linear matrix inequalities

2016 IEEE International Conference on Robotics and Automation (ICRA)Stockholm, Sweden, May 16-21, 2016

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(LMIs) by extending the work of [8]. We showed robustperformance could be achieved under modeling uncertain-ties and measurement noise. Furthermore, we proposed apath planning algorithm which combined with the RMPCtechnique could guarantee collision avoidance in presenceof uncertainties.

We also draw inspiration from Voronoi-based coveragecontrol. A Voronoi-based coverage strategy first proposedby Cortes et al. [9], [10], often referred to as the move-to-centroid controller, drives all robots continuously towards thecentroids of their Voronoi cells. This builds upon previouswork in optimal location of retail facilities [11], and in datacompression [12]. The Voronoi-based control strategy hasalso been used to track intruders by a team of robots withinan environment [13], [14]. However, these works do notconsider no-fly zones, and do not use the guaranteed safeRMPC tools that we use here. Other works [15], [16], [17]have used a Voronoi-based strategy for multi-agent pursuit-evasion, again without considering no-fly zones or RMPCcontrol. Similarly to our approach for placing the pursuersaround a no-fly zone, Susca et. al use a Voronoi-basedstrategy to control a group of agents to monitor an evolvingboundary [18], and [19] uses a bug algorithm to explore anobstacle boundary for finding the best position for sensinginside the obstacle. These works do not consider the pursuit-evasion problem, and don’t use the RMPC tools used here.

Here, we use a Voronoi tessellation to divide up thereachable set of possible evader locations, with the goal ofminimizing the “cost of capture” once the evader emergesfrom the no-fly zone. Using a virtual pursuer position, weemploy a move-to-centroid algorithm to optimally divide thereachable set among the pursuers. Since the pursuers cannotmove inside the no-fly zone, we drive them to the point onthe boundary of the no-fly zone that is closest to the virtualpursuer inside the no-fly zone. We show this minimizes arelevant “cost-to-capture” metric, and demonstrate our algo-rithm in hardware experiments using three KMEL Nano+quadrotors pursuing an m3pi ground robot.

The remainder of this paper is organized as follows. InSection II we describe the algorithm for pursuing the evaderin free space, and in Section III we consider the case whenthe evader is inside a no-fly zone. Section IV describesthe RMPC control method used to robustly execute thealgorithms in the previous sections using a model of thequadrotors’ dynamics with modeling uncertainties. Section Vdescribes our hardware experiments, and we draw our con-clusions in Section VI.

II. ZONE-AWARE PURSUIT IN FREE SPACE

We first consider the case where the evader is in freespace and summarize our path planning algorithm to steerthe pursuers towards the evader while avoiding entering anyno-fly zone. The full details of this algorithm are presentedin the authors’ previous work [20]. In Section III, we providethe tools necessary to track the evader when it is inside a no-fly zone. Throughout this paper, we assume that the pursuerscan determine the position of the evader when it is in freespace, e.g. from an on-board camera or other sensing system,

pi(k)

e(k)

rmax

Qi(k)Z1

Z2

(a)

pi

e

Zr

z

xCCW

xCW

e∗i

xP

Qiε

EZr

(b)

Fig. 2: (a) Construction of the maximum-volume ellipsoid.Shaded areas represent the no-fly zones. (b) Schematic ofthe Path Planning Algorithm ([20], Algorithm 2).

or from a tracking beacon on the evader. We also assume thatthe pursuers know their own positions, e.g., from GPS.

Let np be the number of pursuers in the group. Definepi(k) and e(k) to be the xy-coordinates of the pursuer iand the evader at time k, respectively. Similarly, let hpi (i =1, 2, . . . , np) and he denote the altitude of pursuer i and theevader, respectively. To avoid collision between the pursuerquadrotors, we set hpi 6= hpj for all i 6= j, however this maybe achieved with existing collision avoidance algorithms.

Consider the case where the evader is stationary and isclose enough to the pursuer such that there exists an ellipsoid,completely outside any no-fly zone, that is centered at theposition of the evader and includes pursuer i (see Fig. 2a).We refer to this ellipsoid as the “safety ellipsoid.” Pursuer ican safely reach the evader if there exists a controller whichguarantees both that it never leaves the safety ellipsoid andalso that it asymptotically converges to the center of theellipsoid. In Section IV-A, we review a robust MPC methodwhich can be used to guarantee both conditions.

Let rmin and rmax be the lower and upper bounds onthe semi-minor axis and the semi-major axis of the safetyellipsoid as shown in Fig. 2a. Let υκ, κ = 1, . . . , nυ denotea sampled set of no-fly zone boundary points at the distanceof less or equal to rmax from the evader. For pursuers i =1, ..., np, define the maximum-volume safety ellipsoid as

Qi(k) ,{z ∈ R2

∣∣ (z − e(k))T Qiε (z − e(k)) ≤ 1}, (1)

where Qiε solves the semidefinite programming problem

min trace(Qiε)

s.t. (υκ − e(k))TQε(υκ − e(k)) ≥ 1 + ε,

(pi(k)− e(k))TQiε(pi(k)− e(k)) ≤ 1− ε,

κ = 1, ..., nυ,1

r2max

I � Qiε �1

r2min

I.

(2)

In the above problem, ε > 0 is a parameter that ensuresneither the sampled boundary points nor pi(k) fall on theboundary of Qi(k).

The optimization problem in (2) can occasionally becomeinfeasible, e.g., when the distance between the evader and apursuer is larger than rmax or when the direct line betweena pursuer and the evader crosses a no-fly zone. In suchscenarios, we search for a “dummy” evader which can movethe pursuer in a direction towards the evader. Let e∗i be

321

the dummy evader corresponding to pursuer i and defineZi (i = 1, . . . , nz), a no-fly zone, where nz denotes thenumber of no-fly zones in the environment. Since the no-flyzones can be non-convex, we calculate a minimum-volumeellipsoid around them, which we use to navigate around a no-fly zone. Let EZr be the minimum-volume ellipsoid aroundZr that is centered at the centroid of Zr and is rmin distancefrom the closest boundary point of Zr.

Let line(pi, e) to be the line segment from pi to e. Definelos(pi, e) to be the line-of-sight indicator function such thatlos(pi, e) is 0 if line(pi, e) crosses a no-fly zone and 1otherwise. Consider the case where los(pi, e) = 1. Notethat the optimization problem (2) is infeasible if any pointalong line(pi, e) falls within rmin distance away from ano-fly zone boundary. The Proximity Verification Procedurepresented in [20] verifies if this problem exists and resolvesit by assigning a new dummy evader (e∗i ) to pursuer i.

Even when los(pi, e) = 1 and line(pi, e) is not in rmin

proximity of any no-fly zone, the optimization problem in(2) can become infeasible due to the shape of no-fly zonesand the distance between the evader and pursuer i. In suchscenarios, we create a dummy evader and gradually moveit towards pursuer i until (2) is feasible (see Fig. 2b andAlgorithm 2 in [20]). Finally, if los(pi, e) = 0, we choosethe dummy evader e∗i , to be a point on the minimum-volumeellipsoid of the closest intersecting no-fly zone such thatlos(pi, e

∗i ) = 1 (see Algorithm 2 in [20]).

The Path Planning Algorithm in [20] provides a completepath planning algorithm for a given pursuer i. Each pursuertrack its assigned target evader, ei. When the evader isoutside any no-fly zone, all target evaders are set to e(k).However, when the evader is inside a no-fly zone, eachpursuer is assigned a different target evader to improvecoverage of the no-fly zone as described in Section III.

III. PURSUING AN EVADER IN A NO-FLY ZONE

The previous section summarized our multi-robot pursuitalgorithm when the evader is outside a no-fly zone, and thepursuers know the evader’s current position. In this sectionwe address the case when the evader enters a no-fly zone.Once inside, the pursuers may not have reliable informationabout the evader’s position, so the pursuers construct areachable set of possible positions, based on the evader’smaximum velocity and its entry point. The pursuers arrangethemselves around the perimeter of the no-fly zone to beready to capture the evader when it emerges. We adaptstrategies from Voronoi-based coverage control to partitionthe reachable set of the evader, and position the pursuers asclose as possible to a centroidal Voronoi configuration overthe reachable set, without entering the no-fly zone. We notethat the evader could choose the strategy to stay inside ano-fly zone so that it can never be captured. In this case, thepursuers distribute themselves around the perimeter, therebyeffectively containing the evader within the no-fly zone.

Consider the no-fly zone Zj , where q ∈ Zj is an arbitrarypoint in the zone. The set Zj need not be convex. Let vmax

be the maximum speed of the evader. Suppose an evaderenters the no-fly zone at some time t = τ . The entry point isdenoted as e(τ). For some time t ≥ τ , we can find the

reachable set of the evader locations as a ball of radius(vmax(t− τ)) centered at e(τ), written B(e(τ), vmax(t−τ)).This ball may include points outside the no-fly zone, so wedefine Rj(t, τ, vmax) to be the part of the reachable set thatis inside the no-fly zone, written

Rj(t, τ, vmax) = B(e(τ), vmax(t− τ)) ∩ Zj .We then define the indicator function ρ(q, t) such thatρ(q, t) = 1 if q ∈ Rj(t, τ, vmax) at time t, and 0 otherwise.We can write ρ(q, t) as

ρ(q, t) =

{1, if q ∈ Rj(t, τ, vmax),0, otherwise.

If the pursuers were free to move inside the no-fly zone,we would want to distribute the pursuers to minimize theaverage distance to any possible location of the evader. Letpi be the unrestricted desired position of a pursuer. We canalso define the Voronoi tessellation of the reachable set Rjbased on these desired positions as

Vi ={q ∈ Rj

∣∣∣ ‖q − pi‖2 ≤ ‖q − pk‖2 , k = 1, 2, ..., np

}.

This allows us to formulate a “cost of capture,” modeled afterprevious Voronoi-based coverage cost functions introducedin [9], [10] and written

V(p1, ..., pn) =

np∑i=1

∫Vi

‖q − pi‖2 ρ(q, t) dq. (3)

Intuitively, we see that a low value of V indicates a goodconfiguration of the pursuers. It is also useful to define a“mass” and “centroid” of each Voronoi cell Vi, analogous tophysical masses and centroids, written

MVi =

∫Vi

ρ(q, t) dq, CVi =1

MVi

∫Vi

qρ(q, t) dq. (4)

Although there is a complex dependency between the desiredpositions pi and the geometry of the Voronoi cells, it isknown from locational optimization [11] that minima ofV correspond to configurations where all robots are at thecentroids of their Voronoi cells, or pi = CVi for all i =1, . . . , np. In [9] Cortes et al. introduced a move-to-centroidcontroller that guarantees that the robots are driven to thecentroids of their Voronoi cells, and hence they reach a localminimum of (3). In our scenario, if the pursuers could enterthe no-fly zone, a move-to-centroid controller would positionthem to locally minimize the cost of capture of the evader.However, given that our pursuers must remain outside theno-fly zone, we must adapt the move-to-centroid algorithmto this constrained case.

Remark 1. Note the above approach can easily be extendedto scenarios that utilize predictions about the evader posi-tion. This can be captured in either the reachable set Rj orin the indicator function representing the probability of theposition estimate for q ∈ Rj .

Let di = pi − pi be the vector from the ideal pursuerposition (if the pursuer could enter the no-fly zone) to theactual pursuer position (constrained to lie outside the no-fly

322

zone). Consider minimizing the cost of capture in this case,V(p1, ..., pn), with pi = pi + di, and with pi constrained tolie outside Zj . For i = {1, ..., np}, j = {1, ..., nz}, we canre-write the cost as a function of the vectors di as

H(d1, . . . , dn) =

np∑i=1

∫Vi

‖q − pi − di‖2ρ(q, t) dq, (5)

where pi = CVi , are the unconstrained locally optimalpositions. Our optimization problem is to then solve

min(d1,...,dn)

H(d1, . . . , dn) s.t. di + pi 6∈ Zj .

We find constrained positions to optimize the cost of capturefunction (5) with the following proposition.

Proposition 1 (Projected Centroids). Given pi = CVi forall i = 1, . . . , np, the locations pi that minimize the cost tocapture (5) are given by pi = pi + d∗i , where

d∗i = arg mindi+pi∈∂Zj

‖di‖, (6)

and ∂Zj denotes the boundary of the no-fly zone Zj .Proof. To see that (6) minimizes (5) given our constraints,we first expand the cost function as

H =

np∑i=1

∫Vi

(‖q + pi‖2 − 2dTi (q − pi) + ‖di‖2

)ρ(q, t) dq.

Breaking the above equation into three parts, we find H =H1 +H2 +H3, where

H1 =

np∑i=1

∫Vi

(‖q − pi‖2

)ρ(q, t) dq,

H2 = −2

np∑i=1

dTi

∫Vi

(q − pi)ρ(q, t) dq,

H3 =

np∑i=1

‖di‖2∫Vi

ρ(q, t) dq =

np∑i=1

‖di‖2MVi .

We see thatH1 is the same as the unconstrained cost function(3), and is independent of di, so letting pi = CVi yields alocal minimum of H1 [11]. When pi = CVi , it follows from(4) that H2 = 0. It remains then to minimize H3, whichis accomplished by minimizing ‖di‖, since MVi does notdepend on di. Given that the pursuer cannot enter the no-flyzone, ‖di‖ is minimized when pi is the closest point to thecentroid CVi on the boundary of the no-fly zone Zj .

Note that our Path Planning Algorithm in effect ensuresthat e∗i = di, where e∗i denotes the position of the dummyevader for pursuer i. Therefore, each centroid CVi can betreated as an evader target position assigned to a pursuer.

Figure 1 illustrates the evolution of our no-fly zoneplanning algorithm. Once the evader enters a no-fly zone,the pursuers generate an estimate of the reachable set Rj ,updated at each time step. Between time step, the pursuersuse a move-to-centroid algorithm that projects their desiredposition to the center of their Voronoi cell (see Algorithm 1).A simple target assignment, using the well-known Hungarian

algorithm, matches each centroid point pi = CVi with apursuer. The Path Planning Algorithm [20] is then appliedto each pursuer and its associated dummy evader.

Algorithm 1 No-Fly Zone Planning Algorithm

Input: e(τ), t, Zj , vmaxOutput: pi

1: Calculate B(e(τ), vmax(t−τ)) and Rj(e(τ), vmax(t−τ))2: Compute Voronoi tessellation about pi3: while pi 6= CVi ∀ i do4: Assign pi = CVi5: Recompute Voronoi tessellation6: end while7: Assign pi as target points for pi

IV. QUADROTOR MODELING AND CONTROL

Up to this point, we have assumed that we can control thepursuer positions to a desired waypoint, while staying insidean ellipse. Here we describe the low-level controller usedto accomplish this. We first control the rotational dynamicsof the quadrotor to a desired attitude, which then allows usto control the translational dynamics to a desired location[21]. The translational sub-system controls the position andvelocity of the quadrotor by generating the total rotor outputand the desired roll and pitch angles. The rotational sub-system regulates the attitude of the vehicle to the desiredvalues computed by the translational sub-system.

In [20], we designed a robust MPC controller for the trans-lational sub-system and an LQR controller for the rotationalsub-system. Here, we assume the rotational sub-system isregulated through an on-board controller and consider thecontrol design only for the translational dynamics of theaircraft. We borrow elements from the translational controllerin [20] and expand it to a group of quadrotors. While therelevant theorems and propositions are included here forcompleteness, we refer the reader to [20] for their proof.

Let Ui be the total output of the rotors for pursuer iand define U∗ to be the total output required for hovering.We denote the control inputs to the translational sub-systemcorresponding to the desired roll and pitch angles of thepursuer i by φdi , θdi , respectively. We treat the desiredyaw angle as a parameter set by the user, and without lossof generality set it to zero. Let ξi = (pi, hpi), where pidenotes the xy-coordinates of pursuer i and hpi denotes itsaltitude. Define xi = (ξi, ξi) and ui = (Ui, φdi , θdi) to bethe translational state space and control input vectors.

Suppose e∗i is the dummy evader for pursuer i as definedin Section II and set ξ∗i = (e∗i , he). Let x∗i = (ξ∗i , 03) bea hovering position with u∗ = (U∗, 0, 0). A discrete-timelinear model of the translational sub-system can be derivedby linearizing the nonlinear system dynamics around x∗ andu∗, which yields [20]

xi(k + 1) = Aixi(k) +Biui(k), (7)

where xi = xi − x∗i and ui = ui − u∗. The state and inputmatrices in (7) are given by,

323

Ai =

[I3 TsI303 I3

], Bi = Ts

03

0 0 −g0 g 0−gU∗ 0 0

,where Ts is sampling time and g is gravitational acceleration.

The linearized system in (7) is subject to modeling errorsinduced by system linearization. These effects become spe-cially dominant when the quadrotor performs more aggres-sive maneuver with large roll and pitch angles. Furthermore,the uncertainty caused by disturbance and measurementerrors in pi and e can also be captured as uncertainties in thesystem. We can therefore treat the system as a linear time-variant system with uncertain Ai and Bi matrices. Since thealgorithm requires each pursuer to remain inside its safetyellipsoid, we wish to construct a controller which not onlyachieves stability but also guarantees that a pursuer trajectorynever leaves its corresponding safety ellipsoid even in thepresence of uncertainties. To this end, we use the followingrobust MPC technique proposed in our earlier work in [20]and included here for completeness.

A. Robust Model Predictive Control

To improve the readability, throughout this section, we usesuperscript κ to denote a variable corresponding to pursuerκ. Consider the discrete-time linear time-variant system in(7) for pursuer κ with the output vector yκ defines as,

xκ(k + 1) = Aκ(k)xκ(k) +Bκ(k)uκ(k),

yκ(k) = Cκxκ(k),(8)

where x(k) ∈ Rnx , u ∈ Rnu and y ∈ Rny . Let Dκ ,[Aκ |Bκ] and define the uncertainty set as,

U , {D ∈ Rnx×(nx+nu)∣∣ |Dκ

ij − Dij | ≤ ∆κij , ∀i, j}, (9)

where D = [A|B] denotes the matrix corresponding to thenominal values of Aκ and Bκ, and ∆κ = [∆κ

ij ] is a matrixwith non-negative elements.

Let δ be the vector containing the non-zero elements of∆κ. For the l-th element of δ, let iδl and jδl denote the rowand column index of δl in ∆, respectively (i.e. ∆iδl jδl

= δl).Define Uκ(i, j,∆ij) to be a matrix whose ij−th element isset to ∆κ

ij and the rest are zero. We can now write D as,

Dκ = D +

nδ∑l=1

ζlUκ(iδl , jδl ,∆

κiδl jδl

), (10)

where |ζl| ≤ 1. Note (10) effectively covers the set ofuncertain Aκ and Bκ matrices with polytopic uncertainties.

Let xκ(k + i|k) be an estimate of xκ at sampling timek + i based on the measurements obtained at time k. Forbrevity, we denote xκ(k + i|k) for all i ≥ 0 by xκ(k + i)and extend the same notation for other variables. Consideran MPC problem with an infinite prediction horizon, whereat each sampling time k, a control law uκ(k + i) (i ≥ 0) isdesigned to solve the min-max optimization problem

minuκ(k+i), i≥0

max(Aκ,Bκ)∈U

Jκ∞(k), where (11)

Jκ∞(k) ,∞∑i=0

xκ(k + i)TQ∞xκ(k + i)

+ uκ(k + i)TR∞uκ(k + i),

(12)

Q∞ � 0, and R∞ � 0.Suppose there exists a feedback control law uκ(k + i) =

Fκxκ(k+ i) and a quadratic function V κ(x) = (xκ)TPκxκ

with P � 0 such that for all i ≥ 0,

V κ(xκ(k + i))− V κ(xκ(k + i+ 1)) ≥xκ(k + i)TQ∞x

κ(k + i)+uκ(k + i)TR∞uκ(k + i).

(13)

Then, V κ(xκ(k)) is a Lyapunov function for the optimizationproblem (12) [22]. Note that the control law should satisfy(13) for all (Aκ, Bκ) ∈ Uκ. This, in general, will requiresolving the optimization problem over all vertices of Uκwhich grow exponentially with the dimension of Uκ. In[20], we show an alternative approach where the size of theproblem grows linearly with the dimension of Uκ.

Theorem 1 ([20]). Consider the uncertain discrete system in(8), where uncertainties are defined by (9). Let uκ(k+ i) =Fκxκ(k + i) be the control action for time k + i, ∀ i ≥ 0.If the LMI problem presented in [20], Theorem II.1 has asolution, then Jκ∞ in the worst-case scenario is bounded fromabove by γκ and the minimizing feedback control gain is

Fκ = Gκ(Qκ)−1. (14)

While the above theorem guarantees stability of thequadrotor system for the given uncertainty set, it does notensure that the trajectories will always remain inside thesafety ellipsoid. Let Qκε be the safety ellipsoid for pursuerκ calculated from (2). The following proposition guaranteesthat the pursuer trajectory does not leave the safety ellipsoid.

Proposition 2 (Output Constraints [20]). Consider the un-certain discrete system (8), the feedback control law uκ(k+i) = Fκxκ(k+ i), where Fκ is given by (14), and the safetyellipsoid Qκ (1). Let C = [I2 | 02×4]. If a solution existsto the LMI feasibility problem in [20], Proposition II.3, thepursuer κ is guaranteed to remain in the safety ellipsoid Qκ.

To ensure stability and safety conditions are satisfiedsimultaneously, we add the constraints in Proposition 2 toTheorem 1 and solve the resulting optimization problem.

V. EXPERIMENTS

In this section, we present experimental results demon-strating our proposed tracking algorithm. For the pursuers,we used three KMEL Nano+ quadrotors equipped with Kbeeradios for communication. Localization was performed withNaturalPoint’s OptiTrack system. MATLAB was used toperform all calculations, and the updated waypoints weretransmitted to the quadrotors. Two scenarios are presented.The first experiment uses a simulated evader following a pre-planned trajectory. The pursuers do not know the pre-plannedtrajectory, but react to the evader in real time. The secondexperiment uses a Pololu m3pi robot manually driven with ajoystick, and pursuers react in real time. A video with bothexperimental runs can be found in the video attachment, and

324

0 1 2 3 40

1

2

3

0 10 20 30 40 50 600

0.5

1

1.5

2

2.5

Sample Time

Distance

toEvader

Fig. 3: (Top) Trajectories of the pursuers and evader (red)over time. (Bottom) Min. distance from any pursuer to theevader over time. The red dashed line shows the maximumpossible distance to the evader inside the no-fly zone. Ourstrategy keeps the distance well below the max.

can be viewed on our website1. In both experiments, we seethe pursuers are able to successfully track the evader as itmoves in and out of the no-fly zones.

In the first experiment, three quadrotors tracked a sim-ulated evader as it moved throughout the environment. Thetrajectories can be seen in Figure 3(a), with the evader shownin red and final positions denoted by the circles. Over time,we see that the trajectories of the pursuers remain outside theno-fly zones, demonstrating a successful implementation ofthe path-planning algorithm. Figure 3(b) shows the minimumdistance from any pursuer to the evader over time. Theshaded areas indicate when the evader was within a no-flyzone, and the red dashed line corresponds to the maximumdistance the evader could achieve given its entry point andmaximum velocity. By employing the centroidal Voronoialgorithm, we find that the minimum distance remains rela-tively small, despite the pursuers not knowing the true evaderposition. By the end of the experiment, the pursuers arewithin 10 cm of the evader.

Figure 4 shows stills from the experimental video demon-strating the Voronoi-based coverage strategy while the evaderis in the no-fly zone. Over time, we see the pursuers spreadout as Rj grows. In the final frame, the evader emerges fromthe top of the no-fly zone, with a pursuer nearby.

1http://sites.bu.edu/msl/research/cooperative-multiquad-pursuit

(a) (b)

(c) (d)

Fig. 4: Stills from the experimental video illustrating thecontrol strategy while an evader is in a no-fly zone. Thepursuers (yellow squares) arrange themselves around theboundary, waiting for the evader to emerge in (d).

For our second experiment, we controlled an m3pi robotwith a joystick to create a “live” evader for our threequadrotors to track. The trajectories over time are shownin Figure 5(a). Again, we see the pursuers never enter theno-fly zone. Figure 5(b) plots the minimum distance fromany pursuer to the evader, as well as the maximum possibledistance in red. Our Voronoi-based control strategy keepsthe distance to the evader relatively small, even though itsposition is unknown. Stills from the experimental video areshown in Figure 6. As with the simulated evader, we see thepursuers distributing themselves around the boundary of Zjwhile the evader remains inside the no-fly zone.

VI. CONCLUSIONS AND FUTURE WORK

This paper presents a series of algorithms to coordinatea group of pursuers tracking an evader while avoiding no-fly zones. While the evader remains outside the no-fly zone,we assume the pursuers know the evaders position. Once theevader enters a no-fly zone, an estimate of the reachable setof all evader positions is generated based on the entry pointand the maximum velocity of the evader. Each pursuer is thenassigned to the centroid of a Voronoi cell, which is used todistribute the pursuers about the zone’s boundary. Throughexperiments, we show that as a result of the coordinatedpursuit, the quadrotors remain in close proximity of theevader even when it enters a no-fly zone.

Here, when the evader is in free space, all pursuers track

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0 1 2 3 40

1

2

3

0 10 20 30 40 500

0.5

1

1.5

2

2.5

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toEvader

Fig. 5: (Top) Trajectories of the pursuers and evader (red).(Bottom) Min. distance from any pursuer to the evader.

to the same target. Future work may consider a better wayto track the target using the group for more efficient capture.Another direction may consider the case where pursuers onlyknow the evader’s position if there exists a direct line of sightfrom at least one pursuer.

REFERENCES

[1] A. Ataei and I. C. Paschalidis, “Robust model predictive controlof quadrotors for zone-aware tracking,” Center for Information andSystems Engineering (CISE), Boston University, Tech. Rep. 2015-IR-0002, 2015.

[2] J.-C. Latombe, Robot Motion Planning, ser. The Springer InternationalSeries in Engineering and Computer Science. Springer, 1991.

[3] C. Goerzen, Z. Kong, and B. Mettler, “A survey of motion planningalgorithms from the perspective of autonomous UAV guidance,” Jour-nal of Intelligent and Robotic Systems, vol. 57, no. 1-4, pp. 65–100,2010.

[4] C. Schlegel, “Fast local obstacle avoidance under kinematic anddynamic constraints for a mobile robot,” in Intelligent Robots andSystems, 1998. Proceedings., 1998 IEEE/RSJ International Conferenceon, vol. 1. IEEE, 1998, pp. 594–599.

[5] O. Brock and O. Khatib, “High-speed navigation using the globaldynamic window approach,” in Robotics and Automation, 1999. Pro-ceedings. 1999 IEEE International Conference on, vol. 1, 1999, pp.341–346.

[6] P. Ogren and N. E. Leonard, “A convergent dynamic window approachto obstacle avoidance,” Robotics, IEEE Transactions on, vol. 21, no. 2,pp. 188–195, 2005.

[7] J. Minguez and L. Montano, “Nearness diagram (nd) navigation: col-lision avoidance in troublesome scenarios,” Robotics and Automation,IEEE Transactions on, vol. 20, no. 1, pp. 45–59, 2004.

[8] M. V. Kothare, V. Balakrishnan, and M. Morari, “Robust constrainedmodel predictive control using linear matrix inequalities,” Automatica,vol. 32, no. 10, pp. 1361–1379, 1996.

[9] J. Cortes, S. Martinez, T. Karatas, and F. Bullo, “Coverage control formobile sensing networks,” Robotics and Automation, IEEE Transac-tions on, vol. 20, no. 2, pp. 243–255, 2004.

(a) (b)

(c) (d)

Fig. 6: Stills from the experimental video illustrating the no-fly-zone planning. The pursuers are denoted by the yellowsquares, and the evader is circled in red.

[10] J. Cortes, “Coverage optimization and spatial load balancing by roboticsensor networks,” Automatic Control, IEEE Transactions on, vol. 55,no. 3, pp. 749–754, 2010.

[11] Z. Drezner, Facility location: a survey of applications and methods,ser. Springer series in operations research. Springer, 1995.

[12] Q. Du, V. Faber, and M. Gunzburger, “Centroidal voronoi tessellations:Applications and algorithms,” SIAM review, vol. 41, no. 4, pp. 637–676, 1999.

[13] L. Pimenta, M. Schwager, Q. Lindsey, V. Kumar, D. Rus, R. Mesquita,and G. Pereira, “Simultaneous coverage and tracking (scat) of movingtargets with robot networks,” in Algorithmic Foundation of RoboticsVIII, ser. Springer Tracts in Advanced Robotics, G. Chirikjian,H. Choset, M. Morales, and T. Murphey, Eds. Springer BerlinHeidelberg, 2010, vol. 57, pp. 85–99.

[14] S. G. Lee and M. Egerstedt, “Controlled coverage using time-varyingdensity functions,” in Proc. of the IFAC Workshop on Estimation andControl of Networked Systems, 2013.

[15] H. Huang, W. Zhang, J. Ding, D. Stipanovic, and C. Tomlin, “Guaran-teed decentralized pursuit-evasion in the plane with multiple pursuers,”in Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on, Dec 2011, pp. 4835–4840.

[16] S. Pan, H. Huang, J. Ding, W. Zhang, D. Stipanovic, and C. Tomlin,“Pursuit, evasion and defense in the plane,” in American ControlConference (ACC), 2012, 2012, pp. 4167–4173.

[17] S.-Y. Liu, Z. Zhou, C. Tomlin, and K. Hedrick, “Evasion as a teamagainst a faster pursuer,” in American Control Conference (ACC),2013. IEEE, 2013, pp. 5368–5373.

[18] S. Susca, F. Bullo, and S. Martinez, “Monitoring environmental bound-aries with a robotic sensor network,” Control Systems Technology,IEEE Transactions on, vol. 16, no. 2, pp. 288–296, March 2008.

[19] A. Breitenmoser, M. Schwager, J. C. Metzger, R. Siegwart, andD. Rus, “Voronoi coverage of non-convex environments with a groupof networked robots,” in Proc. of the International Conference onRobotics and Automation (ICRA 10), Anchorage, Alaska, USA, May2010, pp. 4982–4989.

[20] A. Ataei and I. C. Paschalidis, “Quadrotor deployment for emergencyresponse in smart cities: A robust approach,” in Control and Decision(CDC), 2015 IEEE International Conference on, 2015.

[21] S. Bouabdallah and R. Siegwart, “Full control of a quadrotor,” in Intel-ligent robots and systems, 2007. IROS 2007. IEEE/RSJ internationalconference on, 2007, pp. 153–158.

[22] E. Camacho and C. Alba, Model Predictive Control, ser. AdvancedTextbooks in Control and Signal Processing. Springer, 2013.

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