AUTOMATED RECHARGING FOR PERSISTENCE

MISSIONS WITH MULTIPLE MICRO AERIAL VEHICLES

Yash Mulgaonkar

A THESIS

in

Robotics

Presented to the Faculties of the University of Pennsylvania in Partial

Fulfillment of the Requirements for the Degree of Master of Science in Engineering

2012

Dr. Vijay Kumar Dr. Jonathan Fiene

Supervisor of Thesis Thesis Committee Member

Dr. Camillo J. Taylor

Graduate Group Chairperson

Contents

Acknowledgements vii

Abstract viii

1 Introduction 1

1.1 Thesis Contributions and Outline . . . . . . . . . . . . . . . . . . . . 2

1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Problem Formulation and Overview of Approach 5

3 Micro Aerial Vehicles 7

3.1 Propeller Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.2 Battery Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4 MAV Charging System 13

4.1 Charging Station Structure . . . . . . . . . . . . . . . . . . . . . . . . 13

4.2 Charging Ports on the Quadrotor . . . . . . . . . . . . . . . . . . . . 15

4.3 Charging Station Status Panel . . . . . . . . . . . . . . . . . . . . . . 17

5 Software Infrastructure 20

5.1 Robot Operating System (ROS) . . . . . . . . . . . . . . . . . . . . . 20

5.2 Finite State Machine and MATLAB . . . . . . . . . . . . . . . . . . . 21

5.3 VICON Motion Capture System . . . . . . . . . . . . . . . . . . . . . 22

ii

iii

6 Single Agent Surveillance 24

7 Multi-Agent Persistent Surveillance 27

8 Conclusion and Future Work 32

List of Tables

3.1 Propeller Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 Battery Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.1 Format of Serial Output from Charger . . . . . . . . . . . . . . . . . 18

iv

List of Figures

1.1 The Hummingbird Quadrotor on the Charging Station . . . . . . . . 2

3.1 The Hummingbird MAV in Flight . . . . . . . . . . . . . . . . . . . . 7

3.2 Battery Discharge Curves at Hover for Three Propellers . . . . . . . . 9

3.3 Estimated Flight Times Based on Battery Capacity . . . . . . . . . . 12

4.1 The Hummingbird Quadrotor Docked on the Charging Station . . . . 14

4.3 Charging Lead for a 3-Cell Li-Po Battery . . . . . . . . . . . . . . . . 14

4.2 Prototypes of Charging Ports . . . . . . . . . . . . . . . . . . . . . . 15

4.4 The Revoelectrix[1] Li-Po Charger with the Xbee module . . . . . . . 17

4.5 A Screenshot of the Charging Station Status Panel . . . . . . . . . . 19

5.1 The VICON MX Motion Capture System . . . . . . . . . . . . . . . . 22

5.2 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

6.1 Fully Autonomous Single Agent 180 minute Persistence Mission . . . 24

6.2 A Flowchart of the Persistent Surveillance Algorithm . . . . . . . . . 25

6.3 Selection of Vmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

7.1 Final Persistence Mission Setup . . . . . . . . . . . . . . . . . . . . . 27

7.2 Target Area Covearge During a Fully Autonomous Multi-Agent 912

Hour Persistence Mission . . . . . . . . . . . . . . . . . . . . . . . . . 29

7.3 2D Position of Each MAV Throughout the Experiment . . . . . . . . 30

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7.4 Battery Voltages of Each Quadrotor . . . . . . . . . . . . . . . . . . . 31

8.1 The Pelican MAV Being Transported by the iRobot PackBot . . . . . 34

Acknowledgements

It is di�cult to overstate my gratitude to my advisor, Dr. Vijay Kumar, for his unwa-

vering support and assistance during this project and for giving me the opportunity

to work at the General Robotics Automation Sensing and Perception (GRASP) Lab-

oratory. I would also like to thank Dr. Jonathan Fiene for being the committee

member for this thesis.

My heart felt thanks to Daniel Mellinger, Quentin Lindsey, Kartik Mohta and

everyone else at MRSL for their constant encouragement and support. I really appre-

ciate the e↵ort of the laboratory sta↵ in the Department of Mechanical Engineering

and Applied Mechanics for granting us access to the 3D Printers and the tool cribs.

This thesis would not have been possible without the immense support of my

friends, the GRASP Lab and the faculty at the University of Pennsylvania. Most of

all, my parents for being my pillars of strength and for providing me with the best

education possible.

vii

Abstract

The past decade has seen an increased interest toward research involving Micro Aerial

Vehicles (MAVs). The predominant reason for this is their ability and agility to

perform tasks too di�cult or dangerous for their human counterparts and navigate

into places where ground robots cannot reach. Among MAVs, rotary wing aircraft

such as quadrotors have the ability to operate in confined spaces, hover at a given

point in space and perch[2] or land on a flat surface. This makes the quadrotor a very

attractive aerial platform giving rise to a myriad of research opportunities.

The potential of these aerial platforms is severely limited by the constraints on

the flight time due to limited battery capacity. This in turn arises from limits on the

payload of these rotorcraft. By automating the battery recharging process, creating

autonomous MAVs that can recharge their on-board batteries without any human

intervention and by employing a team of such agents, the overall mission time can be

greatly increased. This thesis describes the development, testing, and implementation

of a system of autonomous charging stations for a team of Micro Aerial Vehicles. This

system was used to perform fully autonomous long-term multi-agent aerial surveil-

lance experiments with persistent station keeping.

viii

Chapter 1

Introduction

Over the past decade, the use of unmanned aerial vehicles (UAVs) in reconnaissance

and search and rescue has greatly increased throughout the world. Remotely piloted

unmanned aircraft provide tactical aid to the military in real-time. These vehicles

have proved to be e�cient first responders in a number of hostile mission scenarios.

Teams of multiple such vehicles can also be used for persistence missions in hazardous

or unknown environments reducing the risk to their human counterparts. However,

the most significant constraint on the any aerial vehicle for such persistent missions

is the depletion of its on-board power resource - electrical or fossil fuel.

A number of researchers have explored persistent UAV operations[3, 4, 5]. In

[3], researchers demonstrated video surveillance over an area using two fixed-wing

UAVs and a team of ground vehicles. In [4, 5] researchers explored similar battery

recharge systems to increase mission time of micro aerial vehicles. These papers

focus on asset allocation and mission management during flight operations. Though

many papers discuss how to perform the operations using terrestrial platforms, this

thesis discusses the development and implementation of charging stations for a team

of autonomous unmanned aerial vehicles (UAVs) performing long-term (over 6 hr)

persistent surveillance operations. This research was aimed at not only significantly

1

2

reducing but completely avoiding down time, maximizing mission time and removing

the dependency on a human operator.

As an illustration of long-term persistent autonomy, using the indoor Micro Aerial

Vehicle Testbed[6] at the GRASP Lab, this thesis aims at presenting results which

show that, with the implementation of the charging stations, the UAVs are able to

carry out such tasks as surveillance for far longer than they would have been able to

without recharging.

Figure 1.1: The Hummingbird Quadrotor on the Charging Station

1.1 Thesis Contributions and Outline

This thesis describes the development and implementation of an autonomous charging

station for micro aerial vehicles (MAVs). Subsequently, a system of such charging

stations was used to conduct fully autonomous, persistent surveillance experiments

with single and even multiple UAVs. The successful implementation of these charging

3

bays have also enabled other researchers to incorporate this system for achieving

longer mission time while minimising the downtime. In addition, this thesis also

describes the first docking station for ground transportation of Micro Aerial Vehicles

on-board terrestrial robots[7].

This document begins with Chapter 2 describing the problem formulation and

the plan of action for conducting experiments towards accomplishing the goal of this

thesis. Chapter 3 discusses the UAVs used in the experiment. Chapter 4 highlights the

electromechanical characteristics of the Charging Station while Chapter 5 describes

the software infrastructure used for the experiments. Chapters 6 and 7 describe the

implementation of multiple Charging Stations in single and multi-agent persistence

surveillance experiments. Chapter 8 concludes the thesis with final thoughts and

future work.

1.2 Related Work

The first autonomous robots, “Elmer” and “Elise” were created by William Grey

Walter in the late 1940’s in Bristol, England[8]. Exhibiting a simple and elegant

design, in addition to executing designated tasks, these robots had the ability to

autonomously recharge themselves.

Even today, a large number of research groups continue to probe into autonomous

vehicle recharging. At the University of Southern California, researchers have demon-

strated a ground robot that has the ability of recharging autonomously on a stationary

“charging dock” and resume its original task[9]. On similar lines, another group has

been working towards implementing a battery swapping station for small co-axial

helicopters[10].

This thesis however, primarily focuses on the autonomous recharging of a team

of UAVs which must interrupt their current task and return to the charging bays to

4

recharge their on-board batteries. They must also delegate their task to a secondary

vehicle before leaving the target air-space.

Similar autonomous recharging capabilities are exhibited iRobot’s commercially

available floor cleaning robot, “Roomba” where it autonomously navigates to the

charging dock[11]. A few other ground robots too implement similar autonomous

recharging capabilities, but a mainstream application for unmanned aerial vehicles

has still not been successfully deployed. Researchers at the Massachusetts Institute of

Technology[4, 5] and ETH Zurich[12] have been working towards persistence missions

with autonomous recharging stations for UAVs.

For their experiment, the team at MIT[4] addressed the resource management issue

for missions having more number of MAVs than chargers. They had also included

autonomous recharging stations in the experiment for long-term flight tests. However,

battery balancing was not implemented in their charging stations during the recharge

cycles. This imposed strict limitations on the maximum charging current and thus

increasing the downtime for each UAV to be about 50 minutes. In addition to this, the

charging ports and charging pads were made of copper causing the contact surfaces

to oxidize due to sparking.

At ETH Zurich, such an autonomous charging system was used for a long duration

construction experiment where a team of MAVs was used to construct a 6 meter tall

tower. In this case, similar charging stations were placed on raised platforms where

the MAVs could land and recharge. Further information is not available due to

absence of documentation about the project.

Chapter 2

Problem Formulation and

Overview of Approach

To create a background for this thesis, I wish to present the issues surrounding ex-

periments involving Micro Aerial Vehicles that require mission times that are longer

than the MAV’s flight time on a single battery charge. The obvious solution would

be to recall the MAV, swap out the depleted battery and resume the mission. This

solution, however simple, requires periodic human intervention. The ideal situation

would be to either autonomously swap out the depleted batteries or have the MAV

land on a charging station, recharge and resume its original mission. All, without

any human intervention. This thesis will address the later solution and implement

the system for a long duration multi-agent experiment.

The basic setup for the multi-agent persistence surveillance mission can be gener-

alised as follows:

M Agents:

A mission has a set of mission agents that are allocated certain tasks. The current

state of each of these agents can be represented by a set of unique parameters like

position, orientation, fuel (or battery level) etc. These agents can either be homo-

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6

geneous, having similar tactical capabilities or they can be a group of heterogeneous

agents that can be used in a variety of mission scenarios. These parameters for each

of the agents are time variant.

N Charging Stations:

A certain mission system will also have a series of base stations (or charging

stations) the agents involved return to, for maintenance and refuelling. The instan-

taneous state of each base station is also characterized by certain parameters like

occupancy, status of vehicles currently at the base station. The mission can be bro-

ken down into four basic deciding factors: the number of agents to be deployed for a

certain task, the current agent’s current fuel (or battery) level, the fuel levels of the

agent’s on standby, and the availability of an unoccupied charging station.

To simplify this problem for the scope of this thesis, a certain degree of redun-

dancy is accepted to separate the mission optimization problem from the validation of

hardware towards the final mission objective. The simplified formulation, focuses on

validating the system for a long duration persistent surveillance experiment with zero

downtime to facilitate further research into optimizing task assignment and scheduling

problems.

To address the problems associated with a short mission time of a battery powered

Micro Aerial Vehicle, this thesis discusses the implementation of a system of charging

stations with autonomous recharging routines for a single and multi-agent team of

MAVs. To corroborate the theory discussed in this thesis, a persistent surveillance

mission is simulated such that at any given time, atleast one MAV must be present

over a certain area in space, whilst minimizing the overlap between two MAVs dur-

ing the change of guard. Experimental test results for a single agent persistence

surveillance in Chapter 6 can be compared to test results from a similar multi agent

experiment in Chapter 7.

Chapter 3

Micro Aerial Vehicles

Figure 3.1: The Hummingbird MAV in Flight

Since the design and development of MAVs is beyond the scope of this thesis,

commercially available, o↵-the-shelf (COTS) quadrotors were selected for use with the

battery recharging system. These “Hummingbirds” were manufactured by Ascending

Technologies, GmbH[13] and a customized version of this is shown in Figure 3.1.

We have chosen quadrotors MAVs for conducting experiments toward this thesis

especially since we would be navigating in highly constrained spaces, and because

they have the ability to hover at a given point in space. An unmodified Hummingbird

has a span of 54cm from blade tip to blade tip, a height of 8 cm, and weighs about 500

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8

grams with a standard 2100mAh 3S battery. The fuselage of this platform is carbon-

fibre reinforced balsa and has a magnesium skeleton, thus making it extremely durable

and light-weight. These MAVs have a thrust to weight ratio of about 2.5 giving it

a payload lifting capacity of about 500g. In addition to this, the Hummingbird also

provides additional user code space on the higher level microcontroller. This high

level interface allows users to command desired roll, pitch, and yaw angles and net

thrust. The low level software interface allows the user to directly set the four rotors

speeds. This enables better control over the MAV and the development of precise

control algorithms.

This thesis aims at maximizing the mission time for single or multi-agent exper-

iments by implementing autonomous charging stations. Thus to increase the overall

e�ciency of the system, it is imperative to maximize the flight times of each MAV

by one or all of three ways: Reducing overall laden weight of the MAV, selecting an

appropriate battery pack or the selection of e�cient propellers.

3.1 Propeller Selection

Figure 3.2 shows the battery discharge curves for the same fully charged battery on

the same quadrotor but having three di↵erent propellers. We tested three di↵erent

sets of propellers for use with the quadrotors during persistent missions. Two sets

were those sold by Ascending Technologies for the Hummingbird quadrotors and the

third set of propellers were the ones manufactured for the Parrot AR. Drone[14].

Through previous experiments, current draw (ih) for the Hummingbird quadrotor

at hover was found out to be 6.6A with the gray propellers. Since the quadrotor is

powered by a 3S battery, its average operating voltage (v) can be estimated to be

11.1V. With the available data, we can calculate the power consumed at hover (Ph)

9

0 2 4 6 8 10 12 14 16 18 20

10

10.5

11

11.5

12

12.5

Battery Discharge Curves at Hover

Time (min)

Battery

Volta

ge (

V)

Gray Asctec PropellersBlack Asctec PropellersParrot AR.Drone Propellers

Figure 3.2: Battery Discharge Curves at Hover for Three Propellers

Propeller Flight Time RPM Mass kf

(min) (g) (N/rpm2)

Asending Technologies Gray 18.26 4500 3 7.03 · 10�8

Ascending Technologies Black 17.23 3800 4 10.24 · 10�8

Parrot AR. Drone 14.93 4750 3 6.16 · 10�8

Table 3.1: Propeller Specifications

and the energy consumed Eh such that the flight time at hover,

th = Eh/Ph

10

Now for the gray propellers, ih = 6.6A and v = 11.1V . Therefore,

Ph(gray) = v · ih = 73.26W

Eh = th · Ph = 1338.50

Similarly for the black propellers, Ph(black) = 77.6843 W and for the AR. Drone

propellers, Ph(AR.Drone) = 89.6517 W

Evidently, since they proved to be most e�cient we chose the gray propellers

available from Ascending Technologies for all further experiments involving persis-

tence missions.

3.2 Battery Selection

To further increase the flight time of the MAVs, battery packs with larger capacities

can be chosen. Figure 3.3 shows the estimated flight times for three batteries with

capacities of 2100mAh, 3300mAh and 6000mAh. The flight times (at hover) of each of

these batteries were experimentally determined using the same quadrotor and keeping

all other test conditions constant.

To formulate the electrical model for the quadrotor, we denote the power required

by the quadrotor to hover as P in Watts, thrust required to hover as T , battery

voltage as V in V olts, battery capacity as Qb in mAh, weight of an unladen quadrotor

as wq in grams, flight time at hover in seconds as th and finally two constants

kp a proportionality constant between the power consumed and thrust and kb, the

reciprocal of specific energy density of the battery.

Keeping the dynamic model of the quadrotor in perspective[15], we can form

11

equations for Power and the Energy of the battery Eb as :

P = kp · T 3/2

Eb = Qb · V

The total weight of the system W can be characterized as weight of the battery

wb terms of Eb and the weight of the quadrotor wq. This would also enable us to

determine an equilibrium condition for the thrust T required for stable hover.

wb = Eb · kb

W = wq + wb

T = W

Eb = P · th

Combining the above equations, we can write Eb in terms of kp, T and th and sub-

sequently derive the final equation to estimate the flight time thest:

Eb = kp · (T 3/2) · th

thest =Eb

kp · (wq + (Eb · kb))3/2

Using the above electrical model, the flight times were estimated for a range of

battery capacities. From the graph, we can see that the 6000mAh battery seems to

be far more e�cient than the other two. This was because the battery belonged to a

di↵erent manufacturer and was optimized to have the lightest weight for its capacity.

It must be noted though, that the curves for the 2100mAh and 3300mAh batteries

hold true for the above claims within a reasonable realm of error. For experiments

described in this thesis, the 2100mAh 3S batteries were used, since the curves for the

12

3300mAh and the 2100mAh batteries showed a similar performance and also due to

the limited real-estate available on the MAV for mounting the battery pack.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 150000

5

10

15

20

25

30

35

Battery Capacity (mAh)

Est

imate

d F

light T

ime (

Min

)

6000mAh 3S battery2100mAh 3S battery3300mAh 3S batteryObserved Values

Figure 3.3: Estimated Flight Times Based on Battery Capacity

Brand Rating Mass Specific Power Specific Energy Energy Density

(mAh @ V) (g) (kW/kg) (Wh/kg) (Wh/L)Turnigy 400 @ 7.4 22.4 2.643 132.1428 235.7063

Zippy 1000 @ 7.4 58.6 3.1569 126.2798 290.196

Turnigy 1000 @ 11.1 94.2 5.03024 117.8344 225.5639

Turnigy 2650 @ 11.1 217.1 3.3873 135.4906 250.9469

Turnigy 2200 @ 14.8 252 5.8142 129.2063 310.5922

Turnigy 3000 @ 14.8 357.7 4.3444 124.1263 275.1287

Turnigy 5000 @ 14.8 533.6 2.7736 138.68807 309.2146

Table 3.2: Battery Specification

Chapter 4

MAV Charging System

This chapter describes in detail, the mechatronic components that constitute the

automated recharging system. The extent of structural modifications to the MAVs is

restricted to the addition of electrical contacts attached under each of the four arms

of the quadrotor. No other mechanical or electrical modifications were made to the

vehicles. The charging stations, however, consist of a custom designed landing pad,

a Li-Po battery charger-balancer and a wireless data uplink. These landing pads are

designed such that the MAVs are securely anchored to the contact surface by way of

neodymium magnets to minimize vibrations during take-o↵ and landing.

4.1 Charging Station Structure

The charging station is designed for MAVs to autonomously land, recharge their on-

board batteries and resume their original task. As shown in Figure 4.1, the charging

station is composed of four stainless steel landing pads which double up as the elec-

trical contacts for charging. When a quadrotor descends onto the pad, the magnets

embedded in the charging ports on the quadrotor, pull it toward the landing pads thus

ensuring a reliable and robust contact. As shown in Figure 4.1, each electrical contact

is made up of a sheet of 400 series stainless steel. Each of these pads is connected

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Figure 4.1: The Hummingbird Quadrotor Docked on the Charging Station

to the smart Li-Po charger to interface with the MAV once it lands on the charging

station. The unique construction of the charging ports on the quadrotor ensures that

all four contacts are made reliably and securely once the quadrotors lands on the the

landing pads. This construction is discussed in detail in Section 4.2.

Pack +ve

Pack -ve

7.4v

3.7v

Figure 4.3: Charging Lead for a 3-Cell Li-Po Battery

15

(a) Iteration 1 (b) Iteration 2 (c) Iteration 3

Figure 4.2: Prototypes of Charging Ports

4.2 Charging Ports on the Quadrotor

The current version of charging ports on each of the MAVs are the result of a couple

of previous iterations. These are shown in Figure 4.2. The material chosen for the

construction of these charging ports was based on the charging technique employed for

Li-Po batteries. The battery charging/balancing connector is shown in Figure 4.3. For

a conventional 3S Li-Po battery, the first and fourth terminal are the pack positive

and pack negative respectively. The two intermediate terminals are the taps for

the internal cell. During charging, the charger pumps current through the pack

positive and negative terminals and trickles current through the other two. For the

experiments described in this thesis, the maximum charge current was set to 4A

which passes through the above mentioned positive and negative terminals. Taking

into account the current flow through the charging station to the charging ports on

the quadrotor, we experimented with various pairs of metals as contacts. After a short

literature review[16] we learned that for an instantaneous current surge, the best pair

of metal contacts would be gold (197Au) on 400 series stainless steel. Though in our

experiments we would be steadily ramping up the current from 0 to 4A, we initially

16

decided to use the above two metals for contacts. In the first case, as shown in 4.2a,

the structure of the port itself was 3D Printed from ABS plastic, and gold plated

neodymium magnets were used as contacts. These ports, though easy to fabricate,

took about 90 minutes to print in a Fused-Deposition Printer and resulted in a line

contact as long as the width of the magnet used. This iteration of the charging

port weighed 11 grams each. The key-feature of this version of charging ports was

the magnetic quick-connect capability where the port could be easily removed or

snapped onto the quadrotor, giving the user an option to remove the ports when not

required hence getting rid o↵ unnecessary payload.

In the second iteration as shown in Figure 4.2b, we fabricated the charging port

as a modified leaf spring. The use of a strip of 0.010” thick spring steel gave each

charging port the necessary compliance to ensure a reliable contact with the landing

pad surface. To minimize loss of contact due to vibrations during landing and take-

o↵, we added neodymium magnets which had proved to be very e↵ective from the

previous design. The most significant improvement in this version over the previous

design was the planar contact surface between the charging port on the MAV and the

landing pad. Each of these new charging ports weighed a little over 3 grams. In spite

of improvements over the previous design, these ports proved to be too sti↵ causing

the MAV to bounce o↵ the surface of the landing pads.

Incorporating the best characteristics of the two previous designs, we chose to

fabricate the compliant charging ports from spring steel. For these charging ports,

we used a strip of 0.005” think spring steel. The design however is a compressive

O-ring which provides the necessary compliance when the MAV lands on the landing

pad. This charging port is shown in Figure 4.2c. A neodymium magnet was also used

to minimise loss of contact due to vibrations. These new charging ports were used for

all the subsequent experiments since the contacts were far more reliable as compared

to the previous two versions and weighed only 3 grams each. Less than a third of the

17

weight of the first iteration.

In the second and third versions, we did not use the gold to stainless steel con-

tacts as initially proposed, since the smart Li-Po chargers prevented an instantaneous

current surge through the contacts.

Figure 4.4: The Revoelectrix[1] Li-Po Charger with the Xbee module

4.3 Charging Station Status Panel

For real-time monitoring of each charging station the serial data output from the

individual chargers was decoded and parsed. Each of the chargers output a 119

character serial string which contained instantaneous status parameters of the charger

and the battery connected to it. The relevant data from this string was recorded at

the rate of 5Hz and transmitted to the base station via a wireless XBee uplink. The

18

format of the string is as shown below:

S012C03AD03AF03AF000003000216000000000095044B07030000014301FF020500C

10062232300002323000003AE03D003AA00951006002C0511

Data Index [1 119] Factor Parameter

6:9 ; 10:13 ; 14:17 0.00446 Individual Cell Voltages

47 1 Charge State

49 1 Number of Cells

62:65 0.02348 Supply Voltage

70:73 1 Time Elapsed

94:97 0.1 Battery Percentage

102:105 0.0061 Charge Current

Table 4.1: Format of Serial Output from Charger

The base station receives this data from the charging station via a Xbee wireless

uplink. The software, written in Matlab then decodes the received data, extracts

relevant information and displays it a graphical form in real-time. This visual data

is shown in Figure 4.5. The red and green regions in the graphs denote the battery

recharging and battery balancing phases respectively.

19

Figure 4.5: A Screenshot of the Charging Station Status Panel

Chapter 5

Software Infrastructure

The software infrastructure used for the experiments in this thesis has three key

components, Robot Operating System (ROS), Inter Process Communication Bridge

(IPC-Bridge) and MATLAB. Another system that enables most of the experiments

with quadrotor MAVs is the VICON motion capture system.

5.1 Robot Operating System (ROS)

The control software for the MAVs is integrated with the VICON motion capture

system and accessed via ROS, an open-source, meta-operating system[17] developed

and supported by Willow Garage and widely used in the robotics community. The

incorporation of ROS greatly simplifies the transition between onboard and external

computation.

The actual operation of ROS is based on executables called nodes. Each of these

nodes operate as a separate application and utilize the ROS message scheme for

communication. Each ROS package contains its own message definitions, source

code, header files, and executable. The “Command” node controls the entire system,

receiving data from all of the various navigation and control systems. It then sends

commands to the MAVs and in turn, to the motor controller to control the robot’s

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actions.

In ROS, data is transferred between nodes in the form of msgs (messages). Msg

files are simple text files that describe the format and data fields of a ROS message.

These messages can contain primitive data types (signed or unsigned int8, int16, int32,

int64), floats (float32, float64), strings, times, other msg files, and arrays of variable

or fixed length. ROS’s asynchronous style of data transfer utilizes msgs continuously

posted to topics from which other nodes may read. A node will “publish” to a topic

at pre-defined intervals, independent of other ROS operations. Any number of nodes

may “subscribe” to this topic. When a node subscribes to a topic, it will constantly

listen for a posting to that topic, and when received, will execute a certain command.

5.2 Finite State Machine and MATLAB

For single and multi-agent experiments, we implement our logic as a sequence of states

for each individual robot. Each state is a unique behavior that defines a particular

segment of the whole experiment. These states range from keeping the robot idle in

a “do nothing” state to complex maneuvers like flying through a window[18]. Each

behavior is defined by initiating and conditions specific to that state. Each state also

has certain transitional conditions that enable it to transition into nested states and

return to the original state. For the experiments described in this thesis, the behaviour

of each MAV is sequentially compiled into a program prior to the commencement

of the experiment. The program is provided to a finite state machine (FSM) that

drives the feedback controller and monitors the performance of each robot. We have

also used KORG Midi controllers to enable the manual transitioning of the FSM.

The integration of the FSM into ROS and MATLAB allows us to run closed loop

controllers by using pose and position estimates from the VICON motion tracking

system and the IMU on-board the MAVs. The MATLAB interface enables us to

22

formulate experiments using previously generated states or by designing new ones.

This FSM running in MATLAB finally generates control signals to be sent to each

of the MAVs in the form of desired attitude, �des, ✓des, des, desired angular velocities

pdes, qdes, rdes, and desired thrust, udes

5.3 VICON Motion Capture System

Figure 5.1: The VICON MX Motion Capture System

The pose and position estimates for each of the quadrotors are provided by the

VICON Motion Capture System[19]. The distinguishing features of the VICON mo-

tion capture system are that it has a fast update rate of upto 200Hz. It also provides

sub-millimeter accuracy of the order of a few microns. The VICON Tracker software

is also able to track a certain object with very high precision with as low as three

cameras as long as the whole object is visible to all of them. The MAV testbed at

the GRASP Lab[20, 6] where the experiments toward this thesis were conducted, has

23

a system of twenty such cameras giving superior coverage of the entire flight arena.

This state-of-the-art motion capture system enables us to track each quadrotor MAV

moving at speeds in excess of of 6m/sec, accelerations of 15m/sec

2 and angular speeds

of 1000 �/sec.

Base StationVicon Mx

MAV Position / Posex, y, z / ϕ, θ, ψ

Q1 -Qn C1 -Cn

MAV 1...MAV n

Charging Pad 1

Charging Pad n

...

IPC

Figure 5.2: System Architecture

Chapter 6

Single Agent Surveillance

Figure 6.1: Fully Autonomous Single Agent 180 minute Persistence Mission

Long duration flight testing of the autonomous charging system was performed to

validate the control software and the hardware associated with the charging stations.

24

25

For this testing, one quadrotor was modified as described in Chapter 4. This MAV was

programmed to take o↵ from the ground, hover over a target area until its battery

had been depleted to a predetermined cuto↵ level (Vmin), and then autonomously

locate and land on an available charging station. Once the charging system had

recharged and balanced the battery to satisfy certain conditions, the MAV would

repeat the cycle. Figure 6.1 shows a MAV hovering over the recharge system during

a 180 minute flight test. This test has been run several times, once for two hours

and several times in excess of three hours. In the span of a three hour flight test,

the MAV proved to be very reliable in terms of landings and take-o↵s. The Cellpro

4s Gold Li-Po charger however transitioned into a “Safety Charge” mode where it

charges at 0.5A from the 4A charge cycle, causing considerable delay in a couple of

cycles. This feature was rather conservative with very low tolerances of 0.2V on the

voltage di↵erence between the internal cells.

Voltage(>(Vmin(

Voltage(Priority(

Quad(in(Air(

Ba5ery(>(95%(&(Balancing(&(Ic(<(2.0(A(

Hover(Over(Target(Area(

Charge(

Yes(

No(

Yes(

No(

Yes(

No(

Ready(

Figure 6.2: A Flowchart of the Persistent Surveillance Algorithm

26

The cuto↵ voltage level (Vmin) mentioned above was determined experimentally

by recording the flight times and corresponding charge times for di↵erent threshold

voltages. The battery voltage with the highest ratio of the flight time to charge time

was chosen to be the Vmin for the experiments. As seen in Figure 6.3, Vmin was chosen

to be 11.0V.

10.7 10.8 10.9 11 11.1 11.2 11.3 11.4 11.5 11.60.55

0.56

0.57

0.58

0.59

0.6

0.61

Voltage (V)

Ratio

of F

light T

ime to C

harg

e T

ime

Figure 6.3: Selection of Vmin

Chapter 7

Multi-Agent Persistent

Surveillance

Figure 7.1: Final Persistence Mission Setup

27

28

After validation of the charging station hardware, multiple such stations were fab-

ricated to enable multi-agent persistence missions. These charging stations were used

to perform multi-agent cooperative missions where the task of surveillance over a tar-

get area was divided among each of them. In the final experiment toward this thesis,

three charging stations were set up for a persistence mission test. The final aim of this

mission was to keep one MAV hovering over a target area for an extended duration.

Figure 7.1 shows the setup for the experiment described above. In this experiment,

one MAV hovers over a designated target area, while the other two MAVs are docked

in the charging bays. Once the active MAV exhausts its battery, it signals the reserve

MAVs to take its place and then returns to an available charging station. In addition,

the base station software deploys an MAV whose battery has been recharged by the

charging station to hover over the specified target area. The algorithm minimizes

downtime and ensures a near 100% mission time. This cycle repeats itself enabling

multi-agent persistence missions without the need for any human intervention.

This algorithm was successfully implemented with a system of charging stations to

conduct a persistence surveillance experiment running for over 912 hours. Figure 7.2

shows the autonomous deployment of three MAVs to hover at the height of 2 meters,

over a target area with specified coordinates in the flight arena at the GRASP Lab.

Figure 7.3 shows the X-Y position of each quadrotor throughout the experiment. The

Yellow patches signify the charging bays and the Purple patch around the origin [0, 0]

denotes the target area over which the MAVs were deployed to patrol.

29

Figure 7.2: Target Area Covearge During a Fully Autonomous Multi-Agent 912 Hour

Persistence Mission

Over the 912 hour experiment, we observed that in four short instances, there was

30

no MAV in the air. This downtime can be seen in Figure 7.2 as white spaces between

the consecutive surveillance phases of two MAVs. This was caused by the “Safety

Charge” feature of the battery charger due to which the MAV in question was not

able to fully recharge its battery in time to take o↵ and take the place of the vehicle

that had left the surveillance staging point. Overall, including these downtimes this

system could o↵er an e↵ective mission time of over 99% during the 912 hour test.

Figure 7.3: 2D Position of Each MAV Throughout the Experiment

With the integration of ROS and MATLAB, as explained in Chapter 5, we were

able to log the battery level on each of the MAVs in real-time. Figure 7.4 shows

the instantaneous voltages of each quadrotor over a part of the experiment. These

31

voltages were read on-board the quadrotor and sent to the base station via a wire-

less uplink. No additional hardware was required to read, transmit or decode these

voltages. This real-time, in-flight information was incorporated into the base station

software to monitor the state of each quadrotor to determine its overall “health” and

develop a deterministic algorithm for the overall mission for task delegation between

the team of MAVs.

0 20 40 60 80 100 120 140 1609

9.5

10

10.5

11

11.5

12

12.5

13

13.5

14Battery Voltages of Each Quadrotor

Time (min)

Vo

ltag

e (

V)

QuebecMikeBravo

Figure 7.4: Battery Voltages of Each Quadrotor

Chapter 8

Conclusion and Future Work

This thesis describes the design, development and implementation of fully autonomous

charging stations for MAVs. The hardware associated with this system was success-

fully validated by addressing and solving the issues mentioned in Section 1.2. The

scalability of the algorithm used in the experiments described in this thesis was also

tested by simulating 10 MAVs and charging stations. Finally, this system was success-

fully implemented to perform a 912 hour multi-agent persistent flight test. Preliminary

implementation of this charging system in experiments involving construction of cubic

structures with quadrotors showed a three-fold increase in e↵ective mission time.

There are many improvements that can be made to enhance the capabilities of the

recharging system. Adaptive control algorithms can be integrated into the system to

predict the remaining flight time in each MAV and also to estimate the remaining

time to recharge. This stochastic approach could also be used by the base station to

improve resource management and task allocation.

The experiments in this thesis implement a control algorithm for M quadrotors

and N charging stations, where M = N. However, another avenue for future research

is to develop control algorithms to enable multiple robots to share charging stations

where M > N.

32

33

Improvements to the charging stations can be made by designing the Li-Po battery

chargers in-house to prevent the delays caused by the “Safety Charge” feature of

the current chargers. The downtime of each MAV can also be greatly reduced by

implementing a battery swapping mechanism where the depleted batteries can be

swapped out for charged ones without powering the UAV down.

To facilitate collaborative 3D mapping of buildings, we have also constructed a

docking station for MAVs that can be mounted onboard unmanned ground vehicles

(UGVs)[7]. This way, ground robots can be used to transport the aerial robots and

deploy them for mapping areas inaccessible by the ground robot, thus increasing

the overall mission time of the system. Figure 8.1 shows the “Pelican” quadrotor

docked on the iRobot “PackBot” [11]. An improvement to this system would be to

incorporate the battery charging or battery swapping mechanism to further increase

the mission time by recharging the MAV while it is docked on the UGV.

34

Figure 8.1: The Pelican MAV Being Transported by the iRobot PackBot

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