Distributed Task Allocation and Coordination

Scheme for a Multi-UAV Sensor Network

Simi S1, Rakesh Kurup

2, Sethuraman Rao

3

Center for Wireless Networks and Applications

Amrita Vishwa Vidyapeetham

Amritapuri, Kerala, India 1simis@am.amrita.edu, 2rakeshmcet@gmail.com, 3sethuramanrao@am.amrita.edu

Abstract—Unmanned Aerial Vehicle (UAV) networks require

successful mission execution, which is dependent on task

planning and control. In this paper, we propose a distributed

task allocation algorithm for a multi-UAV sensor network for

sensing thorium intensities in the coastal areas of India. A power-

aware coordination and planning approach is used to coordinate

the activities of multiple UAVs. Depending on the availability of

resources in each sensing UAV unit, each UAV initiates a task-

scheduling algorithm and distributes its remaining tasks

dynamically to other UAVs. The control station sends control

information to the UAVs and receives the sensed parameters

along with the location information from each UAV. The task

allocation algorithm was simulated successfully using a network

of static motes. The network was more effective and efficient with

the use of this task allocation algorithm, thus contributing to

improved mission execution in UAV networks.

Keywords—distributed algorithms, sensor network, taskshare

I. INTRODUCTION

Electricity has become essential for the successful economic development of any country. Atomic reactors are one of the most efficient methods of power generation, due to the amount of energy generated without carbon emissions. The nuclear materials necessary to make electricity are scarce and are distributed randomly across the globe. India has an abundant deposit of thorium, in another form called monazite. According to India’s department of atomic energy, India has a deposit of 846477 tons of thorium, which is an estimated 30% of the total thorium deposit in the world. Monazite is located mainly in the black sand of India’s coast, across various states of India, including Kerala, Tamilnadu, Andhra Pradesh, Odisha, West Bengal and Bihar [1].

Intensity mapping of this wide area (across the coast of six states of India) is required as the intensity of the material in each area is not uniform. Nowadays such mapping is achieved using helicopters, equipped with sensors, which will roam the area to generate an intensity map. Using helicopters for frequent or continuous explorations is an extremely costly method, due to the size of helicopters required, trained pilots, fuel, maintenance, availability, and cost of rent or procurement. However, this paper presents an alternative, efficient and cost effective method for producing intensity maps. Variations in terrain dictate that ground vehicles will not be nearly as effective as aerial vehicles for intensity mapping. Therefore, Unmanned Aerial Vehicle (UAV) are one of the most suitable

options for intensity mapping of thorium, as most of these areas requiring monitoring are coastal and do not have so many trees and buildings which could cause obstructions to aerial sensing.

Other applications of this research into UAV systems include radioactive mineral surveying. In many cases, the areas to be surveyed may not be suitable for ground-based explorations. This design can be used for other aerial sensing applications such as pollution monitoring, airborne radiation measurement (i.e. to map radiation spread due to nuclear disaster. After nuclear disaster, ground based surveying may not be possible, so aerial surveying is a good option), etc.

This paper proposes a system consisting of multiple co-operating UAVs and a control station, which effectively senses the radioactive intensity of black sand that contains thorium. The control station is a laptop computer equipped with specialized software. The area to be scanned will be loaded into the software along with the GPS location information of the initial point. The software will divide the area, to be scanned, into a sequence of fixed locations called waypoints and each location will have a GPS co-ordinate. The control station controls the UAV flight and generates an intensity map to show the thorium intensities at each location and the measured UAV height at the time of sensing.

The organization of the paper is as follows: Section 2 describes the existing work in this field. Section 3 and 4 includes the system architecture and the design of the algorithms. Section 5 discusses the experimental results followed by conclusion.

II. RELATED WORK

Low costs, light weight unmanned aerial vehicles (UAV) are one of the best options for remote sensing instead of their costly alternatives: satellites and helicopters. Haitao et al explains the design and implementation of an agricultural remote sensing system based on a UAV. A system was designed that uses an autonomous UAV to monitor agricultural crops with a multi-spectral camera [2]. The system was used to study the effect of herbicide on turf grass. Custom-made software was used for the ground station, which generates waypoints from a 2D map. These waypoints are then loaded to an onboard computer and the UAV automatically navigates to these locations and collect the pictures using a multi spectral camera. After the image is captured, a footprint of the image is

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drawn at the ground station so that the operator can verify whether any waypoint has been missed or not. Radio controllers were used for landing and taking off. A single UAV was used to cover the entire area, however for larger areas requiring scanning, multiple UAVs could have been a more efficient option, thus distributing the task between more than one UAV.

UAVs could also be used to collect data from sensor fields. Andre et al.’s paper titled, “Throughput per Pass for Data Aggregation from a Wireless Sensor Network via a UAV,” discusses the possibilities of using a UAV for collecting data from a sensor field [3]. These authors concentrated their design on the data communication between the UAV and the sensor field. The receiver at the UAV was a RAKE receiver, which receives the same information from different nodes. This communication appears to occur between a single virtual source, through different delayed paths and the UAV. The UAV combines the information from different delayed paths, by using the RAKE receiver as a Maximally Radio Combining receiver (MRC). The efficiency of the proposed system was tested, by simulating a one square kilometer area, with 100 nodes uniformly distributed. Field tests were not conducted to validate the performance of this method. The authors assumed that all the sensors, in a particular sensor field, would be able to share the same data that can be received by a UAV, however this is not usually the case. Their design is limited to an unlikely application. However, we could make a sensor field where all sensors share their data so that this system would be viable. However energy restraints in the mass sharing required may further drawback this system.

The authors M. Quaritsch et al. report the details of their ongoing project in the paper titled: “Networked UAVs as an aerial sensor network for disaster management applications,” [4]. This paper draws attention to the use of co-ordinate UAVs for disaster management. A group of multiple UAVs, is proposed, that fly over a disaster area and send images of the disaster to a ground station. At the ground station, the data is processed and combined to generate valuable information for the end user. Using the information of GPS, maps and the amount of UAVs ready for flight, the ground station computes the points where each UAV will take pictures to ensure coverage of the whole disaster area. These points are chosen such that the area is covered efficiently but with a minimum number of pictures. Once the points are fixed, the route for each UAV is calculated so that the energy expenditure is minimized and all points are covered.

In all the above three papers discuss a common factor that is the use of single or multiple UAVs for the applications: agricultural monitoring, sensor data gathering and disaster management. These papers all focus on aerial sensing and processing. In all the three cases, the UAV has to navigate to waypoints that are fixed prior to flight. The automatic navigation methods implemented in the UAV are used to navigate to the waypoints automatically. The navigation method using Global Positioning System (GPS) and the Inertial Measurement Unit (IMU) is the preferred option, which is discussed by Kevin et al in the paper titled: “Inertial Navigation,” [5]. The combination of GPS and the IMU is called an Inertial Navigation System (INS). The IMU consists

of accelerometers and gyros. The authors have discussed errors likely to occur in IMU measurement, due to these factors: temperature, vibration, hysteresis, bias and drift. To remove the random variations in the IMU data, the IMU data is filtered using a lower order IIR filter and also passed through an Extended Kalman Filter. Both the GPS and Kalman filter are used to gain more accuracy and tests were conducted with and without using these filters. For better navigation, a slow accurate GPS and fast less accurate IMU are effectively combined.

Jan et al., in the research paper titled: “An integrated GPS/MEMS-IMU navigation system for an autonomous helicopter,” discuss the use of GPS and IMU along with other sensors such as a barometer and magnetometers, for better navigation, when GPS signals are not continuous [6]. The system works in two modes. In the first mode, measurements from the GPS, the magnetometer and the barometer are passed through a Kalman filter. The magnetometer is used to reduce error in the yaw calculation. The second mode is selected when there is no GPS signal for a particular period. In the second mode, the IMU, the magnetometer and the Kalman filter are used to provide attitude. The authors have simulated their research using original flight data and verified that, this method of switching between two modes provides improved accuracy in attitude calculations, in the presence and absence of GPS signals.

The system presented in this paper includes a ground station and a UAV, such as a quad copter, which could be used as the platform for creating a multi-UAV sensor network for sensing thorium intensities in coastal areas of India. The ground station is able to receive the sensor measurements, from the UAV network, and can generate an intensity map in real time. A distributed task-sharing algorithm, run in each of the UAVs, will distribute tasks dynamically.

III. SYSTEM ARCHITECTURE

A schematic diagram of the system is given in Fig. 1. The diagram mainly portrays the ground control station, the quad copter and the area to be scanned. The communication links between quad copters and the control station are shown as directional arrows. The ground station is dedicated to perform mission planning, mission control and intensity map generation. The quad copter has mechanisms to perform sensing, navigation, routing, flight control and task share. The functions of each block are explained in detail.

A. Control Station

The ground control station has the software that controls the mission and generates the thorium intensity map. This software has two parts - mission controller and map generator. The mission controller initiates and controls mission. The mission control takes the map of the area to be scanned and initial GPS coordinates, as the input, and generates a set of fixed co-ordinates in GPS format (thus forming a scanning waypoint set). The mission controller carefully divides these waypoints among all the available UAVs to avoid any path overlap. The mission controller uploads the divided waypoints into corresponding UAVs. The mission controller monitors the

mission and takes necessary actions upon receiving mission status messages from UAVs.

The map generator is another important module in the control station; its main function is to map the thorium intensities, received at the control station. The quad copters send the sensor measurements in a predetermined format, which contains sensor readings along with the location co-ordinates, corresponding to the GPS waypoint of that measurement. At the receiver, this information is plotted to convey a correct picture of thorium intensities at each location.

Fig. 1. Architecture of the system

B. Quad Copter

The Unmanned Aerial Vehicle (UAV) used is a quad copter, which is a vertical takeoff and landing vehicle with four rotors. This quad copter contains five basic blocks: sensing unit, navigation module, routing module, dynamic path planner and a hardware block. The sensing module contains the sensor to measure the thorium intensity, mainly the radiation intensity. The sensing module will generate a voltage depending on the radiation intensity and that voltage is given to the analog to digital converter of the microcontroller in the hardware block. The navigation module works along with the hardware block to navigate the quad copter to each of the GPS waypoints, assigned by the control station. The navigation module continuously checks whether the target is reached or not, by reading the GPS at regular intervals. The navigation module also communicates with the dynamic path planner to read the next GPS waypoint to be navigated.

The routing module establishes the most energy efficient route to the control station. The routing module runs at regular

intervals – i.e.: after collecting certain amount of sensor readings from certain waypoints. The measurements are stored in the UAV, and the routing module periodically empties this buffer. The routing module attempts to find the route either directly to control station or through its neighboring UAVs. Out of all possible paths, the one with least energy consumption is chosen for transmission.

The dynamic path planner is responsible for changing the set of waypoints based on certain circumstances such as in the event of low battery power, which is also monitored. When the dynamic path planner finds that the quad copter’s energy is not enough for sensing the remaining waypoints, then the dynamic path planner attempts to divide the job among its neighboring UAVs. For that purpose, dynamic path planner initiates a task share request to the neighboring UAVs. When a neighboring UAV receives a task share request, it will calculate an estimate of its own power requirement based on its own remaining waypoints and the average power that will be needed for its return flight to the control station. If the battery power is above the estimated power requirement then the receiver, sends back a positive acknowledgment to the share request sender. A negative acknowledgment is send when the power requirement for new task is more than the available power. A negative acknowledgment can also be sent when the receiver has already sent a task share request. Upon receiving positive acknowledgments, the quad copter will divide its remaining task among those neighbors who send positive acknowledgments and send the task to them. If there are no positive acknowledgments then the quad copter will send a mission-abort control signal to the control station and will fly back to the control station.

IV. ALGORITHM DESIGN

A dynamic task share and conflict avoidance algorithm is

designed for efficient sensing. The need for such an algorithm

arises due to the uniqueness in sensing process faced by each

UAV. For each waypoint, a UAV’s hovering time varies and

depends upon various factors such as environment,

measurement quality, etc. Therefore, batteries of some UAVs

drain faster than the batteries of other UAVs in the group. This

fact demands a system, in each UAV, to check whether it has

enough power to continue the mission or not. Thus, the task

share and conflict avoidance algorithm will help each UAV to

take decisions and share its remaining tasks with nearby UAVs

without creating any conflicts. Each UAV will execute this

algorithm after successfully collecting data from each

waypoint.

The algorithm is executed when the following conditions are met.

1. Amount of targets to be covered > 0

2. Battery power < Power required to cover next

waypoint and return to origin

When both the conditions are satisfied, the UAV will broadcast a task share request and start a response timer. The information in this request contains the UAV’s current position and remaining waypoints.

When a UAV receives a task share request, it initiates a power estimation function. This function has to establish a maximum estimate of power required: to finish all of its own waypoints and to cover the new waypoints requested and to return back to the control station. The estimation function will take the expected distance to be travelled and the power expenditure per unit distance as parameters. The estimation function calculates the power estimate to cover the expected distance and the maximum possible incremental power that has to spend at each waypoint. The estimation function is shown in (1).

The expected distance is calculated from two sets – remaining waypoint set (Srt) and shared waypoint set (Sst). The remaining waypoint set contains waypoints from the UAV’s initial allocation that still have to also be completed. The shared waypoint set contains the set of waypoints obtained from the request. The expected distance includes the distance to cover in Srt, distance to reach the beginning of Sst, the distance to cover in Sst and the return journey. The expected distance calculation is given in (2).

max* ( ) *E E a tot

P D P P T= + ∆ (1)

The variables are,

PE = Power Estimate

Pa = Average power for unit distance

DE = Expected Distance

∆P = Incremental power needed at each waypoint

Ttot = Remaining waypoints + waypoints from share request

( [ ], [ ]) ( [ ], [0])

( [ ], [ ]) ( [ ], )

E rt rt rt st

st st st init

D Dist S i S last Dist S last S

Dist S i S last Dist S last T

= +

+ +

(2)

Iteratively calculating the power estimation for each waypoint in Sst and comparing it with the available power establish the amount of waypoints that can be covered. This iterative computation continues until the comparison fails and the amount of successful comparisons is counted. If this number is zero a NACK is sent, and if positive an ACK is sent, with the new waypoints that can be covered.

When the response timer finishes, the request initiator will check for any ACKs. If there are no ACKs, then the initiator will return to the control station by aborting the mission. However, if there are ACKs then the UAV with maximum extra waypoint covering ability will be selected for the task share and an ACCEPT message will be sent. On receiving an accept message the extra waypoints are accepted and added to that UAV’s remaining waypoint set. A formal description of this algorithm is given in the fig. 2.

V. EXPERIMENTAL RESULTS

The features of the dynamic task share algorithm were tested using a wireless network made up of micaZ motes. The algorithm is implemented in NesC language and burned into three motes and all were switched on simultaneously. These three motes represent three UAVs. Each mote was allocated 100 tasks and each task was of 10 seconds duration. At the beginning of a task, each mote executed the task share

algorithm. The algorithm will check the mote’s battery voltage after finishing each task. A task share request was sent if the battery voltage was less than the power calculated, to cover the remaining tasks. The motes successfully showed the share request broadcasts through their LEDs.

Fig. 2. Dynamic task share algorithm

The messages sent between task sharing motes were captured using a packet sniffer. The experiment is conducted for two different cases, one is without the task share algorithm and the second is with task share algorithm. The Motes were programmed such that each waypoint was visited only once.

Fig. 3 gives the task completion plot for the three motes without the task share algorithm. The vertical axis shows the percentage of tasks completed by each mote. The tasks allocated to each mote are shown in three different colors – blue, red and green in the plot. In the first experiment, Mote 1 has enough energy to complete all the tasks and achieves 100% in the plot. However, Mote 2 completes 80% of the allocated tasks. Mote 3 does not have enough energy to complete 50% of its tasks, so after finishing 50 tasks, Mote 3 stops. The inference from this experiment was that out of the 300 tasks allocated to the three Motes only 230 tasks (76.67%) were completed. The remaining 70 tasks were left unfinished. To complete these tasks another round of task division has to be performed and the process has to be repeated. Even if any UAV has enough power left to finish a few tasks more, without task share, it is of no use, thus proving the necessity of this task share algorithm.

Fig 4 gives the task completion plot after implementing the task share algorithm in all the three motes. Here, the experiment setup and the plot representations are similar to the first experiment. To test the effectiveness of the algorithm, Mote 1 was provided with a battery that had more than enough power to complete its mission and the other Motes were provided with lesser power batteries. All the three Motes started their missions at the same time. Mote 3 initiated the task share process after finishing 50 tasks (50%) and the Share Request was be received by both Mote 1 and Mote 2. Mote 2 responded with a NACK and Mote 1 with an ACK. However, Mote 1 could accommodate only 20 tasks more and Mote 3 successfully sent those additional 20 tasks and stopped its mission. In figure 4, Mote 1 completes all the 100 tasks allocated to it and performed the additional 20 tasks of Mote 3. Mote 2 then also started its own share request, which Mote 1 responded to with a NACK because it already was at its capacity. Mote 1’s battery power was sufficient to complete all its remaining tasks.

Fig. 3. Task Completion Plot (without task share algorithm)

Fig. 4. Task Completion Plot (With task Share Algorithm)

After implementing the task share algorithm, 250 tasks (83.33%) were completed and 50 tasks left unfinished. This is an improvement of 5% compared to the system without the task share algorithm.

VI. CONCLUSION AND FUTURE WORK

The algorithm for dynamic task share and conflict avoidance has been designed and an initial level of testing completed. The results of this initial level of testing has been analyzed and presented. The initial results show a 5% improvement in task completion when using the task share algorithm presented in this paper. Future work includes a comparative study and integration of this algorithm to the real system presented at the beginning of this paper.

ACKNOWLEDGMENT

The authors would like to express their immense gratitude to their beloved Chancellor, Shri. (Dr.) Mata Amritanandamayi Devi, for providing great inspiration for doing this research work. We would like to thank Dr. Maneesha V Ramesh, director of our department for giving valuable suggestions and support. We extend our sincere thanks to Mr. Manesh V Mohan, for helping us out with the initial task separation part. We also thank Mr. Ram Kumar for his timely help in UAV flight tests. We also thank all our colleagues for their contributions that made this research successful.

REFERENCES

[1] Dept of Atomic Energy, “Annual report 2009-2010,” Department of

Atomic Energy, Tech. Rep., 2010.

[2] H. Xiang and L. Tian, “Development of a low-cost agricultural remote

sensing system based on an autonomous unmanned aerial vehicle,”

Biosystems Engineering, vol. 108, pp. 174–190, 2011.

[3] A. Giorgetti, M. Lucchi, M. Chiani, and M. Z. Win, “Throughput per

pass for data aggregation from a wireless sensor network via a uav,”

IEEE Transactions on Aerospace And Electronic Systems, vol. 47, pp.

2610 – 2626, 2011.

[4] M. Quaritsch, K. Kruggl, D. Wischounig-Strucl, S. Bhattacharya, and B.

R.M. Shah, “Networked uavs as aerial sensor network for disaster

management applications,” Elektrotechnik & Informationstechnik, vol.

127, pp. 56–63, 2010.

[5] K. J. Walchko and D. P. A. C. Mason, “Inertial navigation,” in Florida

Conference on Recent Advances in Robotics, 2002.

[6] J. Wendel, O. Meister, C. Schlaile, and G. F. Trommer, “An integrated

gps/mems-imu navigation system for an autonomous helicopter,”

Aerospace Science and Technology, Elsevier, vol. 10, pp. 527–533,

2006.

[7] Edison Pignaton de Freitas, Tales Heimfarth, Armando Morado

Ferreira, Carlos Eduardo Pereira, Flávio Rech Wagner and Tony

Larsson, “Decentralized Task Distribution among Cooperative UAVs in

Surveillance Systems Applications”, IEEE/IFIP WONS 2010.

[8] Introduction to Nuclear and particle Physics 1st ed, PHI Learning Pvt Ltd, New Delhi, 2009, pp, 270-273

[9] Edison Pignaton de Freitas, Tales Heimfarth, Armando Morado Ferreira, Carlos Eduardo Pereira, Flávio Rech Wagner and Tony

Larsson, “Decentralized task distribution among cooperative UAVs in surveillance systems applications”, IEEE/IFIP WONS 2010

DIY Drone Dev Team (2013, April 2) [Online]. Available: https://code.google.com/p/arducopter/wiki/ArduCopter.