Design Strategies of Unmanned Aerial Vehicle-Aided Communication for Disaster Recovery

Gurkan Tuna Department of Computer Programming

Trakya University Edirne, Turkey

gurkantuna@trakya.edu.tr

Tarik Veli Mumcu, Kayhan Gulez Department of Control and Automation Engineering

Yildiz Technical University Istanbul, Turkey

tmumcu@yildiz.edu.tr, gulez@yildiz.edu.tr

Abstract—This paper presents a novel approach of using unmanned aerial vehicles (UAVs) to establish a communication infrastructure in case of disasters. Due to the collapse of buildings, power systems, and antennas, the collapse of communication infrastructure is the usual effect of disasters. In this study, we propose an emergency communications system established by unmanned aerial vehicles (UAVs). The system is a post-disaster solution; hence, it can be used anywhere required. In this study, first we briefly explain the details of the system in three aspects: end-to-end communication, localization and navigation, and coordination Then, we evaluate the efficiency of the localization and navigation subsystem. Our simulation studies with AirRobot quadrotor helicopters in Unified System for Automation and Robot Simulation (USARSim) simulation environment show that UAV-aided communications system can be used in case of disasters to establish an emergency communications infrastructure in terms of localization and navigation.

Index Terms—Unmanned aerial vehicles, post-disaster communications, disaster recovery, navigation and localization.

I. INTRODUCTION

Natural disasters are unforeseeable events and cannot be prevented, also mostly precautions planned to be taken are of little use. Disasters cannot be prevented but their effects can be minimized through proper warnings and post-disaster recovery procedures. The collapse of communications infrastructure is the usual effect of disasters, due to the collapse of antennas, buildings, power etc. In most cases, most of the local communications infrastructures would be destroyed. After the disasters, communications system is vital to support emergency management facilities. Repairing or replacing of destroyed local communications infrastructures take a long time, therefore alternative communications systems are of great value for emergency management.

Unmanned Aerial Vehicles (UAVs) are used for various military and non-military tasks. Their application areas are numerous. Surveillance and reconnaissance operations, monitoring, and aerial photography are common uses of UAVs. Different from general uses, an emerging approach is to use UAVs as communication relays. Boeing demonstrated its narrowband communications relay aboard two UAVs [1]. An UAV was used successfully to relay TV broadcasts [2]. In addition to these field applications, [3]-[8] present theoretical background into using UAVs to serve as relays. In [3], a

method to place UAVs as relay nodes in locations to support the robustness and capacity requirements of an underlying mesh wireless network is presented. In [4], [5], and [7] the answer of where UAVs acting as relays should be placed for optimum performance is investigated, and algorithms capable of generating such relay chains are explained. In [8], calculating the route for a relay UAV to ensure communication at certain time points, given the route of the surveillance UAV is explained. In [6], a multi-source cooperative communication system consisting of multiple UAVs which are used to relay the source signals to the destination nodes is given. Also, in the paper, the outage probabilities at the relay UAVs and the destination are analyzed. Different from these researches we propose an emergency communications system to provide network connectivity to emergency management field workers for applications, and analyze end-to-end communication, localization and navigation system, and coordination and task allocation strategies of this proposed system.

Overall, in this paper, three main aspects of an UAV aided emergency communications system are investigated. End-to-end communication, localization and navigation system, and coordination and task allocation system are explained in detail. Also, the effectiveness of the proposed system is discussed.

The remainder of this paper is organized as follows. The details of the proposed system are given in Section II. Performance evaluations are given in Section III. The paper is concluded in Section IV.

II. UNMANNED AERIAL VEHICLE AIDED COMMUNICATION

In this study, we propose using UAVs in two different scenarios. In the first scenario, radios on the ground are placed at fixed locations. The radios implement a mesh network. If two nodes are not in direct communication range, then intermediate nodes relay the traffic. In mesh wireless networks, generally throughput decreases and latency increases with more hops. When a node failure or congestion happens, new routes are found. This common scenario generally consists of several fixed ground stations connected to each other. This scenario provides good connectivity between ground nodes except for the case when source and destination nodes become disconnected due to a disaster. In this scenario, we propose using UAVs as communication relays to connect disconnected ground stations/nodes as shown in Fig. 1. In this way, ground nodes which are not in direct communication range with other ground nodes can communicate through the UAV. As a result,

using many low-cost UAVs connectivity over wider ranges can be achieved.

Fig. 1. Scenario 1- Using an UAV to relay between ground stations.

In the second scenario, the aim is to maintain connectivity between a ground station and an UAV as shown in Fig. 2. This scenario is a long- distance mission, and mainly relies on a mesh network of UAVs. Due to the constraints limiting communication range such as power, weight, volume on micro UAVs, intermediate UAVs are used to extend the communication range.

Fig. 2. Scenario 2- Using multiple UAVs to relay between a ground station and an UAV.

In both scenarios described above, localization and navigation system is of critical importance. The importance of this system lies in the fact that correct positioning is important for reliable end-to-end communication. A. End-to-End Communication

A wireless communication network of UAVs and a ground station node which acts as the control center constitute the system’s network architecture. For both scenarios described above, general performance measures should be evaluated. The performance and effectiveness of the communications plays an important role on the success of the proposed system. The performance of the proposed system depends on the several factors as follows:

• Data Throughput: In communication networks, data throughput rate is the average of successful packet delivery over a communication channel.

• Latency: Latency is an expression of how much time it takes for a packet of data to get from a source node to the destination node.

• Jitter: Jitter is a variation in packet transit delay which is generally caused by congestion or route changes.

• Packet Loss: Packet loss can occur for a variety of reasons including signal degradation, channel congestion, routing routines, faulty hardware or drivers. For wireless networks, main causes of packet losses are congestion and radio.

• Communication Availability: Communication availability is a measure of the fraction of time during which packets can be reliably sent.

• Communication Range: Communication range is the distance which two wireless nodes can communicate with each other. When communication range is increased, packet losses resulting from radio increase.

• Hardware Reliability: Hardware reliability means how often a network equipment can function properly. Besides performance, the effectiveness a communications

system needs to be investigated. Main factors which determine the effectiveness of a communications system are as follows:

• Self-Configuration & Self-Organizing: Self-configuration and self-organizing means whether wireless devices can turn on via auto configuration and be able to start communicating with each other via automatic routing.

• Self-Healing: Self-healing is a measure of the robustness of a network to failures. When a wireless device responsible for routing stops functioning, packets are delayed till a new route is found.

• Ease of Deployment & Ease of Operation: Ease of deployment is a measure of the difficulty encountered during installation of a communications system. Ease of operation shows the complexity of operating a communications system.

• Data, Voice, Video Communication: The efficiency of a communications system for different services needs to be tested.

B. Task Allocation and Coordination Strategies

The use of multiple UAVs requires information sharing among the UAVs in addition to a task allocation and coordination strategy. Packet delays and losses should be taken into consideration in any task allocation and coordination strategy. Hence, a two phase design which consists of communication phases and motion phases is preferred. While mobile nodes communicate each other to define their waypoints in communication phases, they move to their designated waypoints during motion phases.

In this study, we propose a layered control architecture in order to handle various system dynamics. The layered control architecture is organized in three layers for each UAV, and is comprised of a local supervisor, a mission controller, and a maneuver controller. The mission controller handles the event-based coordination of the UAVs, and assigns the designated waypoints during a mission. The supervisor specifies a set of maneuvers in addition to helping the mission controller handle possible faults. The set of maneuvers is interpreted by the maneuver controller. Then, appropriate control commands are executed on the UAV. The mission controller of each UAV works in slave mode. On the other hand, the mission controller on the ground station works in master mode and sends control inputs to the slave mission controllers.

C. Localization and Navigation System

The localization and navigation system plays an important role for the design of the system. Different positioning schemes for a relay for both of the scenarios described above can be determined. Fig. 3 shows some of the positioning schemes for the first scenario. In these schemes, ground stations are static nodes, and they are located near the maximum range when communicating by using a relay.

Fig. 3. Scenario 1- Various configurations with an UAV responsible for relaying between multiple ground stations. (a) Two ground stations, (b) Three

ground stations, (c) Four ground stations.

For the second scenario, connectivity between a ground station and an UAV is maintained by intermediate UAVs. Communication range can be further extended by using more intermediate UAVs as shown in Fig.4.

Fig. 4. Scenario 2- Various configurations with a number of UAVs to relay between a ground station and an UAV. (a) One UAV, (b) Two UAVs, (c)

Three UAVs.

The localization and navigation system fuses data from an

Inertial Navigation System (INS) and a Global Positioning System (GPS) receiver. The INS acts as the primary sensor and the GPS receiver acts as the complementary sensor. We preferred GPS and INS sensors in this study, since these sensors are very common in UAV navigation systems. GPS receivers provide position and speed information without the need of information about their previous states [10], [11]. Since GPS receivers have low sample rates besides requiring a clear view of four or more GPS satellites, they generally are not preferred as primary sensors for navigation systems [11]. We designed a loosely coupled direct feedback INS-GPS architecture shown in Fig. 5 using the INS-GPS integration architectures in [12] and [13]. The architecture involves data preprocessing, filtering, and smoothing steps.

Fig. 5. GPS-INS integration architecture.

In the proposed architecture, the observation delivered to

the filter is actually the observed error of the inertial navigation, and the filter estimates the errors in this inertial navigation solution. The localization and navigation system is based on Kalman Filter (KF) [9]. In the INS-GPS integration architecture, when an observation arrives, the filter estimates the error in the vehicle states. The observation is the observed error of the inertial navigation system. Whenever the GPS receiver provides position and velocity data, the observation error becomes,

( )

( )

z P ( ) - P ( )z( )=

z V ( ) - V ( )

p k INS GPS

v k INS GPS

k kk

k k

=

(1)

When position and velocity errors are integrated, the observation model becomes

( )

( )

z P ( ) - P ( )z( )=

z V ( ) - V ( )

p k INS GPS

v k INS GPS

k kk

k k

=

(P ( ) + P( )) - (P ( ) - v ( ))

(V ( ) + V( )) - (V ( ) - v ( ))

T T P

T T V

k k k k

k k k k

!

!

=

v ( )P( )

v ( )V( )

P

V

kk

kk

!

!

= +

(2)

The observation is the error between the position and velocity obtained from the INS system and that of the GPS receiver. The noise of the observation of the GPS receiver reflects the uncertainty in the observation.

Optimal fixed-interval smoothers provide optimal estimates using measurements from a fixed interval. There are several commonly used smoothing algorithms. In this study we preferred The Rauch-Tung-Striebel (RTS) smoother which is a fixed-interval two-pass smoothing algorithm. RTS smoother can be used to bridge GPS outages in the post-processing mode. In this algorithm, the standard Kalman estimate and covariance are computed in a forward pass, and then the smoothed quantities are computed in a backward pass.

III. PERFORMANCE EVALUATIONS

Different UAV-Ground station configurations need to be tested to identify any issues, and to prove the overall effectiveness of the proposed method. In this study, we mainly

concentrate on proving the performance of the proposed localization and navigation system.

A. Simulation Studies of the Localization and Navigation

System To show the localization and navigation performance of an

UAV acting as a relay, we performed simulation studies using USARSim [14], [15] and Visual C++ 2008. In order to control UAVs and robots in USARSim, a control application was developed. The control application interfaces with the USARSim server, receives input from GPS and INS sensors of the UAV and sends commands to the UAV. We implemented EKF-based SLAM algorithm in the control application. Communication with the USARSim server is realized via port 3005. The data from USARSim is received using a callback function.

We manually positioned ground stations in the environment. Pioneer P2AT robots act as ground stations and they are stationary. In addition to the P2ATs we used an AirRobot [17] as the UAV responsible for relaying. The simulation to evaluate the performance of the localization and navigation system is depicted in Fig. 3 (a) and shown in Fig. 6.

Fig. 6. Simulated AirRobot and two P2ATs. In this simulation, the P2ATs act as ground stations and the AirRobot acts as a relay.

In USARSim, USARBot.AirRobot class is used to represent an AirRobot. Simulated AirRobot is equipped with one tilt-only color camera as the exteroceptive sensor by default. Since the system uses INS and GPS measurements, an INS and a GPS receiver were placed on the AirRobot. The GPS sensor calculates the current AirRobot position in meters and converts it to latitude and longitude. The main problem with using a GPS sensor in USARSim is the mapping of a virtual location to a real one since USARSim worlds do not inherently have a GPS coordinate associated with them. Though there are alternative methods to define a GPS reference point, we preferred editing the USARBot.ini file and adding ZeroZeroLocation inside the GPSSensor section. The INS sensor simulates a physical INS sensor by using angular velocities and distance traveled [16].

During the evaluation of the localization and navigation system, positional errors and heading errors from the real trajectory were calculated. We did not apply altitude control and did not estimate roll, yaw and pitch angles. Positional errors from the East and North are shown in Fig. 7 and Fig. 8, and heading errors are shown in Fig. 9. Only first 500 seconds of the simulation are shown in these figures. To calculate the positional errors and the heading errors, the outputs of the filter are compared with the ground truth values provided by USARSim.

Fig. 7. Trajectory Errors - X (in meters).

Fig. 8. Trajectory Errors –Y (in meters).

Fig. 9. Trajectory Errors - Heading (in degrees).

Trajectory errors of the simulated UAV are around 0.6 m

for X and Y. Deviations in these limits are negligible for most outdoor applications. Hence, we can conclude that the UAV can localize and navigate itself successfully when loaded with a priori map and a waypoint.

In addition to these performance evaluations, we are planning to conduct field tests with two hexarotor helicopters, and one of them is shown in Fig. 10. Helicopters with multiple rotors can hover and take-off/land vertically. This is their distinct advantage. They also offer higher payloads than helicopters with a single rotor. Our hexarotor helicopters run Robot Operating System (ROS) [18] on Ubuntu. Main specifications of our hexarotors are as follows:

• Embedded x86 computer with an Intel ATOM 1.6GHz CPU

• Powerful ARM 32bit controller

• Hokuyo indoor laser scanner

• Pointgrey USB color camera

• WiFi module

• XBee module

• Ultrafast IMU

• Fast GPS module

• GPS based position hold

• High precision altimeter

• Stable attitude & position control

Fig. 10. Autonomous hexarotor helicopter.

IV. CONCLUSIONS

This paper focuses on using UAVs to establish a temporary emergency communications system in case of disasters. Communication infrastructure is very critical since almost all post-disaster recovery procedures rely on it. Proposed system takes the benefit of UAVs, which can be used to establish a temporary communications system anywhere required. Therefore a single UAV team can serve many cities in a small region. The proposed system has a practical use in disasters.

In this study, we specifically address end-to-end communication, localization and navigation, and coordination issues. The results of USARSim based simulation studies show that the proposed system can be used to set up a temporary communications system in terms of localization and navigation. Future work includes conducting field tests to prove the effectiveness of the proposed system in detail. We are planning to collect statistics over 10 second intervals along with timestamps in order to evaluate detailed performance values such as jitter, throughput, availability, and range in addition to delay and packet losses. Another opportunity with the field tests will be conducting subjective tests on data transfer, video, and VoIP to evaluate connection quality.

ACKNOWLEDGMENT

This research has been supported by Yildiz Technical University Scientific Research Projects Coordination Department. Project Number: 2010-04-02-KAP05 and Project Number: 2010-04-02-ODAP01

REFERENCES

[1]. Boeing: Boeing Communications Relay Links Radios with Multiple Unmanned Platforms [Online]. Available: http://boeing.mediaroom.com/index.php?s=43&item=1882

[2]. Aeronautics has operated UAVs as relay for TV broadcasting [Online]. Available: http://www.aeronautics-sys.com/?CategoryID=259&ArticleID=191

[3]. I. Rubin and R. Zhang, “Placement of UAVs as Communication Relays Aiding Mobile Ad Hoc Wireless Networks,” in Proc. of IEEE Military Communications Conference, pp. 1-7,Orlando, 2007.

[4]. O. Burdakov, P. Doherty, K. Holmberg, J. Kvarnstrom, and P.-M. Olsson, “Positioning unmanned aerial vehicles as communication relays for surveillance tasks,” in Proceedings of the 5th Robotics: Science and Systems Conference (RSS), Seattle, Washington, 2009.

[5]. O. Burdakov, P. Doherty, K. Holmberg, J. Kvarnstrom, and P.-M. Olsson, “Relay Positioning For Unmanned Aerial Vehicle Surveillance,” International Journal of Robotics Research, vol. 29 no. 8 pp. 1069-1087,2010.

[6]. L. Xin, Y. D. Zhang, “Multi-source cooperative communications using multiple small relays UAVs,” in Proc. of IEEE GLOBECOM Workshops, pp. 1805-1810, Miami, 2010.

[7]. O. Burdakov, P. Doherty, K. Holmberg, and P.-M. Olsson, “Optimal placement of communications relay nodes,” in Proceedings of the 3rd European Conference on Operational Research (EURO), Bonn, Germany, 2009.

[8]. A. Holmberg and P.-M. Olsson, “Route Planning for Relay UAV,” in Proceedings of the 26th Congress of the International Council of Aeronautical Sciences (ICAS), 2008.

[9]. G. Dissanayake, P.Newman, S. Clark, H. F. Durrant-Whyte and M. Csorba, “A Solution to the Simultaneous Localization and Map Building (SLAM) Problem,” IEEE Transactions on Robotics and Automation, vol. 17 (3), pp. 229-241, 2001.

[10]. J. E. Guivant, F. R. Masson, E. M. Nebot, “Simultaneous Localization and Map Building Using Natural Features and Absolute Information, Robotics and Autonomous Systems, vol. 40, no. 2-3, pp. 79-90, 2002.

[11]. J. Huang, H.-S. Tan, “A Low-Order DGPS-Based Vehicle Positioning System Under Urban Environment,” IEEE Transactions on Mechatronics, vol. 11, no. 5, pp. 567-575, 2006.

[12]. A. Giremus, A. Doucet, V. Calmettes and J.-Y. Tourneret, "A Rao-Blackwellized particle filter for INS/GPS integration,” in Proceedings of the 2004 IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-04, vol.3, pp. 964-967, 2004.

[13]. S. Sukkarieh, “Low Cost, High Integrity, Aided Inertial Navigation Systems for Autonomous Land Vehicles,” Ph.D. Thesis, University of Sydney, 2000.

[14]. S. Carpin, M. Lewis, J. Wang, S. Balakirsky, C. Scrapper, “USARSim: a robot simulator for research and education,” in Proceedings of the 2007 IEEE Conference on Robotics and Automation, pp. 1400-1405, Roma, 2007.

[15]. USARSim [Online]. Available: http://sourceforge.net/apps/mediawiki/usarsim/index.php?title=Introduction

[16]. 10.2 Sensors [Online]. Available: http://usarsim.sourceforge.net/wiki/index.php/10.2_Sensors

[17]. AirRobot [Online]. Available: http://www.airrobot.de/ [18]. ROS [Online]. Available: http://www.ros.org/wiki/