Continuous Airborne Communication Relay Approach Using ...

Continuous Airborne Communication Relay Approach Using Unmanned Aerial Vehicles

Omer Cetin  Ibrahim Zagli

Abstract As a result of unmanned aerial vehicles being widely used in different areas, studies about increasing the autonomous capabilities of unmanned aerial vehicles are gaining momentum. Today, unmanned aerial vehicle platforms are especially used in reconnaissance, surveillance and communications areas. In this study, in order to achieve continuous long-range communication relay infrastructure, artificial potential field based path planning of Unmanned Aerial Vehicles is discussed. A novel dynamic approach to relay-chain concept is proposed to maintain the communication between vehicles. Besides dynamically keeping vehicles in range and appropriate position to maintain communication relay, artificial potential field based path planning also provides collision avoidance system. The performance of the proposed system is measured by applying a simulation under the Matlab Simulink and Network Simulator environment. Artificial potential field based flight patterns are generated in Matlab, and performance of the communication between vehicles is measured in Network Simulation environment. Finally the simulation results show that an airborne communication relay can be established autonomously by using artificial potential filed based autonomous path planning approach. Continues state communication is provided by obtaining a resistant communication relay which depends on artificial potential field based positioning algorithm.

Keywords Artificial Potential Fields  Airborne Communication Chain Relay  UAVs Swarm Formation  UAVs Path Planning

O.Cetin . I.Zagli Aeronautics and Space Technologies Institute, Turkish Air Force Academy, Yesilyurt, Istanbul, 34149, Turkey e-mail: [email protected], [email protected]

1 Introduction A communication relay can be defined as a station that relays messages from one station to the next one or between various points, so as to facilitate communications between units [1-3]. Communication relays are used for extending the communication range by repeating communication signals. There are several ways to establish a communication relay, and one of these techniques is airborne relay by using aircrafts, satellites or unmanned aerial vehicles (UAV). One of the popular usage areas of UAV is providing communication relays to extend the communication range between two points. Today most of the tactical size UAV platforms such as Predator, Fire Scout, and Hunter provides communication relay feature [4]. UAV communication relays can be used for controlling another UAV from outside of its communication range. Also more than one communication relay can be used to extend the range as a communication chain [5]. One of the problems in establishing a communication relay by using multiple UAV platforms is planning the path of the each member of the chain. This is very complex and critical process, because navigation of the chain members in a comparatively narrow area comes with a serious collision risk. Also, flight area of each UAV is limited with the communication range of another UAV. To provide an airborne communication relay, while rotary wing crafts can be positioned at a point, fix wing crafts are positioned on holding pattern around a point. These points are determined at the boundaries of communication ranges to establish the maximum total range as shown in figure 1. This approach is called as a chain relay [5].

Fig. 1 UAV Communication Chain Relay Representation In the classical chain communication approach, to establish a link between the first and last UAV in the chain, each vehicle must fly in an area which is defined before the flight. This method can be applied for static or predictable end points by discarding unexpected situations or atmospheric distortion effects. This method is not suitable to adapt to dynamic situations. In this work, a dynamic communication relay is providing by using multiple UAV platforms as a swarm and in a formation that is establishing autonomously and is flexible against changing situations by using artificial potential fields (APF). APF is one of the common approaches which is using for path planning for robotic applications [6-8] and UAV path planning studies [9-10]. In the scope of this work, the aim is demonstrating autonomous path planning task for each member of swarm by using APF to provide continuous airborne communication relay. Local minimum problem is one of the major problems of APF based path

planning. In order to avoid local minimum problem, 2nd order exponential harmonic functions are used for defining the APF. The motivation of the work is to provide stable, continues state, dynamic APF based communication chain architecture. Maintaining continuity of chain is provided by producing dynamic APF based flight paths for each UAV in the swarm. APF approach is implemented in Matlab environment to generate flight patterns. The success of the APF based flight patterns has been evaluated by using simple point mass model. After generation of flight pattern for each UAV, communication through chain is measured in Network Simulator 2 (NS-2) simulation environment. Continues state communication is provided by obtaining a resistant communication relay which depends on APF based positioning algorithm. Communication chain is established by autonomously positioned vehicles. As the most important advantage of using APF based communication relay, permanence of the communication is not affected by unexpected displacement of a chain member. Flight pattern of the vehicles calculated by using APF and it is dynamically adaptive against the different situations to provide the communication between nodes. In the second chapter of the work, related studies are examined. 2nd order exponential harmonic function definitions and characteristics are defined in chapter three. In the next chapter, pattern planning with simple point mass model is explained. In the fifth chapter, communication relay design, requirements and the protocols are defined. In the sixth chapter of the work, simulation problems are defined and the simulation works are provided. Attractive and repulsive potential fields are defined and examples APFs are developed. Flight patterns of the UAVs are produced by using MATLAB and Simulink environment. Then by using these 2d flight patterns, communications between UAVs are evaluated in the Network Simulator (NS-2). Also simulation results are discussed in this chapter of the work. In the final chapter, the success of achieving continuous airborne communications relay by using APF based UAV swarm is explained and the future works are presented. 2 Related Works APF based navigation approach is very popular task for robotic applications, because of their simple architecture, easy modeling opportunities and also flexible architectures [6-10]. The attractive points which represent the sinks; and the repulsive points which represent the hills; are measurable values and they depend on basic functions, flexible and effective definitions. An algorithm and structure can be achieved by separating the components of these measurable values to reach the target point in a pattern. APF is one of the common techniques to define the track which aims to reach target point by avoiding obstacles. This approach is suggested first time in the literature by Khatib to planning the robots path in an area which includes obstacles [8]. In this approach, force control is achieved by gradient of the attractive and repulsive fields. Common formula of APF is not able to prevent from local minimums. This case causes a fault which can be defined as going the incorrect fixed point for the moving robot instead of going to the real target point. Kodisteck shows that an APF can be designed by using some special parameters for a mobile robot which provides always to go the real target point without effecting local minimums [11]. Combination of global and local path planning by using APF is demonstrated by Krough and Thorpe [12] and

application of APF for the robots which have real time sensors are improved by Brooks [13] and Arkin [14]. Producing vector fields by combining APF and grids is applied by Borenstein [15]. Connolly aims to develop a path planning algorithm for the mobile robots which depends on APF [16]. APF based path planning approaches which are not containing local minimum points are developed by using static fields. Ahmad has generalized APF based navigation in an area which has known static obstacles [17]. 3 Harmonic Functions for Building APF One of the useful harmonic functions for building artificial potentials is the uniform flow potential, which varies linearly along the direction of the uniform flow it represents. Uniform flow can be used for modeling the total potential of the area of the interest. In 2d space, when the fluid flows in a direction, that makes an angle “α” with the x-axis. The potential function for this uniform flow is like shown with equation 1. (1) The magnitude of the coefficient “δ” is called as the strength of uniform flow. The source/sink singularities are used to derive the repulsive magnitude (high potential) of the obstacles and the attractive magnitude (low potential) of goal position. The uniform flow is used to derive a more effective APF and provides a linearly decreasing potential in the direction from a starting point to the goal position for the unbounded environment. It is a harmonic function in Ω  R n space which provides Laplace equation: (2) In this work, harmonic function which is defining in equation 3 is used to generate the APF. The same function is used for defining the attractive potential field and repulsive potential fields. (3) This equation produces spherical symmetry form potential field which is changing according to the “ ” variable‟s value. If then the equation result becomes a circle form potential field, for the other values of “ ” it will be in ellipse form. The center of the field will be the point which is defining with parameters. “α” is the magnitude factor of the potential fields. This “α” factor will be very big number in attractive field relatively to the value in the repulsive field. The target point, which vehicle tries to reach, is a kind of center point of the attractive field and the obstacles which vehicle tries to avoid while going to target point, are the center points of the repulsive fields in the same coordinate system. By calculating the gradient of the sum attractive and repulsive fields, every force of the point in the area can be achieved. The equation which is using for the calculation of total gradient can be seen in equation 4. (4)

Attractive APF which is modeling the target point is produced by using the equation 3. The counter lines and the vectors of this 2 dimension attractive APF are shown in figure 2-a. Because of the characteristics of logarithmic function which is used for defining the attractive field, the vector forces will change. Vector lengths are longer in the closer of the target. In figure 2-b, we can see the 3 dimension of the attractive APF.

(a)

(b)

(c)

(d)

(e) (f) Fig. 2 Demonstration of APF and Local Minimum Analysis (a) Attractive Vector Field (b) 3d Attractive Potential Field (c) Repulsive Vector Field (d) 3d Repulsive Potential Field (e) The 3d Harmonic Function Potential Field (f) The 3d NonHarmonic Function Potential Field To define the repulsive fields as the obstacles, the equation 3 which is used for defining attractive field is used in opposite side by multiplying with minus. The counter lines and the vectors of this 2 dimension repulsive APF are shown in figure 2-c. In figure 2-c, we can see that vector length becomes shorter when the

point comes to further place from its center point. This is a desired change in APF, because vehicle will be pushed stronger when it comes closer to the obstacle. In figure 2-d, we can see the 3 dimension of the repulsive APF, the center point is accepted as (0, 0) point. Harmonic potential field definition is a kind of important solution for the harmonic function based APF which doesn‟t have a local minimum problem. Harmonic functions are the result of Laplace equations. Harmonic functions implement max-min property. If harmonic functions are used a limitation factor in Laplace equations, it is impossible that local minimum problem is occurred [18]. Not occurring local minimum problem is proofed by using the 2nd order Laplace equation as equation 5: (5) In equation (5), χi implements ith Cartesian coordinate and n dimension. Superposition property of the potential functions is related to the linearity of the Laplace equation. If 1 and 2 are harmonic functions (they satisfy the Laplace equation), then any linear combination of 1 and 2 is also a harmonic function and a solution of Laplace equation. Other properties of harmonic functions about the potential fields are mean value property, the maximum potential property and the minimum potential property [19]. (6) Harmonic and non-harmonic function based potential fields can be compared to detect the local minimum problem. In this work equation (3) is used to generate harmonic function based potential field, and equation (6) is used to generate nonharmonic function based potential field. Figure 2-e and 2-f demonstrate the potential fields of the equation f ( x , y ) and equation g ( x , y ) in orderly. Fig. 3 Collision Avoidance and Comunication Range Limit Boundries of UAVs

(7) To obtain a limited potential field which provides the collision avoidance and communication range, the harmonic functions that is used for producing APF are

limited with exponential functions [10]. To limit the APF, the equation (7) is calculated. The parameter “r” represents the communication range. By using equation (7) within exponential function as shown in equation (8), collision and communication bounds can be defined as shown in the figure 3. By limiting the APF, attractive and repulsive forces of the fields are generated in different directions. Vector fields are obtained by calculating gradient of the function. (8)

Fig. 4. Collision Avoidance and Comunication Range Limit APF Representation As shown in figure 4, attractive forces are on the outbound to keep the UAV in the communication range of other UAV. Repulsive force is applied from the center point of the UAV communication range and provides collision avoidence by forcing other vehicle(s) to the communication range bound. In this work, each UAV has the same APF defination. Desired positions of vehicles can be detected by using identical APF algorithm for each UAV. 4 Pattern Planning A point mass vehicle is the simplest model of a real robot or UAV and is the most common model employed during the study of various kinds of potential field approaches in the area of motion planning. Vehicle is assumed to exhibit the dynamics of a single point mass particle with two degrees of freedom in the x-y plane as shown in figure 5. Mobile vehicle is assumed as a point mass in 2d space. The success of APF based patterns in communication chain construction can be monitored easily with the help of the point mass model. In this study, first of all success of the APF evaluated by using simple point mass model for each vehicle. After generation the flight paths of vehicles by using point mass model in Matlab enviroment, communication ranges and the traffic evaluated in NS-2 enviroment by using generated tracks.

Fig. 5 A Single Point Mass Vehicle Posses Two Degrees of Freedom

While examining action of a point mass modeled vehicle in 2d space, it is possible that action can be characterized as 2d speed vector. Integration of this speed vector in time domain produces the position of the model in space. The speed vector (shown by ) is the position of the vehicle in 2d space. The dynamics equation of a point-mass vehicle can be described by the following equation (10) by using the basic equation (9): (9) (10) Where M denotes the mass of the point-mass vehicle, has the form of and shows the system input. is dissipative term added to stabilize the system. The block diagram of the model by using these equations can be seen in figure 6. Fig. 6 The Block Diagram of the Dynamics Equation of a Point-Mass Vehicle

5 Airborne Communication Relay Design In previous chapters, APF design basics and point mass model based basic trajectory generation concepts are explained. In this chapter, the design of airborne communication relay using produced framework will be explained. By the nature of the relay mission there are three types of tasks. First one is defined as collecting the mission critical information and initiating the transmission. A special type of UAV which is called “Head UAV” is assigned for this task. Its mission is getting into and sustaining a location to keep the target in sensor range as well as sustaining a location to stay in the communication range of the successor UAV. Second type of task is defined as delivering collected and relayed information to mission base. Another special type of UAV which is called “Tail UAV” is assigned for this task and -similar to “Head UAV”- its mission is defined as getting into and sustaining a location to keep mission base in transmission range as well as a location to stay in the communication range of the preceding UAV. And last type of task is defined as maintaining continuity of the communication as long as the physical conditions allow. A common type of UAV is assigned for the last task, which is called as “Bond UAV”, and has only one mission that is staying in communication range of at least two other vehicles as long as the physical conditions allow. Communication relay mission is achieved by a swarm of “Bond UAV” positioned between one “Head UAV” and one “Tail UAV”. Each UAV in swarm is positioned dynamically and autonomously by use of APF based navigation. APF based navigation is simulated in MATLAB and position information of each UAV transferred to NS-2 environment together with the time information. Details of this transformation and simulations are explained in next section. A sample positioning which is constructed in NS-2 environment can be seen in figure 7. In terms of system design, each vehicle and base can have completely different radio transmission range values and characteristics. In order to simplify the simulation implementation, radio transmission range for all UAV types and mission base is assumed to be equal and in perfect circular shape. Sensor range on “Head UAV” is also assumed to be the same as transmission range. Fig. 7 UAV Platforms Definition Demonstration in NS-2

Each vehicle periodically broadcasts a small data packet, called pFSI, containing flight status information like self position and status. Every other vehicle which receives this packet retransmits it once to enable other vehicles to have flight status information of originating UAV. As in a regular flood routing, redundancy can be prevented simply by applying a unique sequence number to each packet. In order to APF model work properly, every UAV must have flight status information of every other vehicle as much quickly and precisely as possible and with no exception. It is also essential that, flight status information contains very small amount of data which is most likely smaller or equal to the data that a route request or a route reply packet may have. Because of that, the overhead that would be caused by the route discovery and route maintenance of a regular routing protocol most likely would be higher than the data itself. Under these circumstances, flooding is selected as the most appropriate protocol in order to send pFSI to all other vehicles. It is the only location and status data -not the payload data- that is sent by flooding. In addition to broadcasting pFSI, “Head UAV” is also responsible transmitting information that is gathered through sensors to mission base, which is also known as payload data. This is done by another connection established from “Head UAV” to mission base, in which, data packets (payload) are routed through “Bond UAVs” and delivered by “Tail UAV”. Streaming of video or radar image captured by “Head UAV” or Voice over IP (VoIP) communication between base and target can be given as examples of this connection. A directed routing protocol is used in streaming payload (sensor) data, because, when the “Target UAV” is relatively close to mission base, flooding may cause excessive data to be broadcasted to unnecessary vehicles while a directed routing may not. A directed routing would try to use the shortest or cheapest path in terms of transmission cost. As a result, it would keep one or more vehicles out of path when possible. In order to take in account the negative effects on transmission of sensor data, flooding process is also included in simulation implementation in NS-2. 6 Simulations To demonstrate the performance of the approach, a two level simulation work is performed. In the first level, the flight path for each communication chain member is produced by using APF in Matlab. APF provides a mechanism for the vehicles to stay in communication range and avoid collision. As a second level simulation work, communication between vehicles is evaluated in NS-2 environment by using flight patterns generated for each UAV in Matlab. The simulation scenario chosen for this paper is getting the online video of a target which is located at a distance only coverable by multiple vehicles in a line. A target point and also ground station (base) are defined. These points‟ initial positions in the simulation area can be seen in table 1. Target and base positions are static during the simulation and the distance between these points is about 1185 meters. In the simulation, five UAV platforms which have the same perfect circular communication range that is about 250 meters as diameter is used. All of the vehicles are able to communicate with the ground station and they have a camera as a payload which also has 250 meters range. As defined in previous part of this work, one of the vehicles is selected as “Head UAV” and another one is selected as “Tail UAV”. Others will be “Bond UAV” which provides

communication between end points, which does not need to carry payload. Initial positions of the vehicles can be seen in table 1. Physical distances between points (between vehicles or base and target point etc.) are taken as units and can be accepted as metric values. Simulation area is defined as 40 x 40 units in Matlab environment and 1000 x 1000 meter in NS-2 environment. Table 1 Definition of the Initial Points of Simulation in Matlab Item Target Head UAV Bond UAV 2 Bond UAV 3 Bond UAV 4 Tail UAV Base

Initial position in 2d coordinate system X Y 35 34 7 8 6 6 6 5 4 4 2 3 1 1

Airborne communication relay must be established to solve this simulation problem. Because the target is out of the mission base range, a network infrastructure must be defined to get the online video from target to base and also to continue controlling the vehicles that are in a position outside of the communication range of mission base. A network infrastructure must be provided to apply a chain type continuous airborne communication relay and appropriate flight patterns must be defined for each vehicle. While planning these patterns, each vehicle‟s flight pattern must be calculated considering the others.

Fig. 8 Vector Field of the Total Forces Producing by APF APF based pattern planning algorithm is implemented in MATLAB Simulink for a 40 x 40 unit area. An APF based vector field is defined for each vehicle and shown in figure 8. These vector fields are in dynamic state and every motion of the each vehicle will update the fields. In figure 8, we can see the vector field of the total forces. Target point attractive field affects only the “Head UAV”. Also

base which is defined as another attractive field, affects only the “Tail UAV”. The vehicles which are acted as “Bond UAV” don‟t affected by these attractive fields. By this implementation, “Tail UAV” stays in the communication range of base and “Head UAV” navigates to appropriate position to reach the target point at the same time staying in the range of others. At the same time every vehicle has a limit APF which are defined as repulsive fields to avoid collision while moving. Also every vehicle has got the repulsive field to keep at least two UAVs in communication range. By using these APF models, each one in the swarm will move the correct place to apply a communication relay chain between “Head UAV” and “Tail UAV”. After establishing the chain, if one of the UAVs moves towards to bounds of the communication range because of the unexpected reasons like wind or performance issues etc. others will update their positions to prevent the broken chain autonomously. Also “Head UAV” sweeps the swarm towards to target position and “Tail UAV” keeps the swarm in the communication range of the base over itself. The success of the designed APF based path planning can be seen in the figure 9 as a simulation result.

Distance “Head UAV” and “Target UAV”

Distance Between “Head UAV” and Other UAVs

Distance Between “Bond UAV 2” and Other UAVs

Distance Between “Bond UAV 3” and Other UAVs

Distance Between “Bond UAV 4” and Other UAVs

Distance Between “Tail UAV” and “Base” Fig. 9 Distance Between Base, UAVs and Target. By using the point mass model during the simulation in Simulink, basic flight path can be produced as an array which includes 2d position of the UAVs for the each simulation step and also including speed parameters. In the Network Simulation v2 software environment (NS-2), a standard wireless node is generated corresponding to each vehicle in airborne communication relay. Communication parameters set for wireless nodes can be seen in table 2. Positioning and navigation data of vehicles involved in NS-2 simulation is generated by APF model design in MATLAB and obtained data is transformed into a scenario file format for NS-2 network simulation software in order to perform further simulations to evaluate communication performance of produced APF based formation. To reduce the MATLAB simulation time and space complexity, values related to distances used in MATLAB are taken relatively small and scaled during NS-2 transformation process to meet realistic measures.

TwoRayGround is used as the propagation model in NS-2 environment, because perfect circular shape is chosen as coverage boundary of wireless nodes and RXThreshold_ parameter is set to a value to enable a 250 meters (10 units in Matlab) communication range in simulation environment for each mobile node (representing UAVs) using tools supplied with NS-2 package. Simulation environment dimensions defined as 1000x1000 meters (40x40 units in Matlab). Simulation time is set to 60 seconds enough to allow “Head UAV” reach the target and stream for a while. Table 2 Communication Parameters of Wireless Nodes Representing UAV Platforms in NS-2 Simulation. Component

Setting

Note

Channel

Channel/ Wireless Channel

Propagation

Propagation/ TwoRayGround

Network Interface

Phy/ WirelessPhy

MAC

Mac/802_11

Link Layer

LL

LL Queue

Queue/ DropTail/ PriQueue

FIFO - Priority Queue

Antenna

Antenna/ OmniAntenna

Omni-Directional

Routing Agent

AODV

Adaptive On Demand Vector Routing

RXThreshold_ is set to obtain desired communication range

In the transformation process, at each simulation step, coordinates of each vehicle has been taken together with the velocity data obtained using derivation of calculated force from APF model as acceleration. These two data together with the assumed communication ranges is scaled to realistic communication needs of wireless environment. After that, communication agents added to NS-2 simulation environment so that each mobile node transmits a 16 bytes broadcast message at every second as shown in figure 10. An appropriate phase shift set between mobile nodes to reduce the probability of wireless channel collisions. Also another UDP/CBR agent is set to simulate constant data transmission from socalled mobile node representing target to mission base. The source node is selected as target node to be able to simulate sensor data of “Head UAV” vehicle.

Fig. 10 Position and Status Broadcast Implementation in NS-2

Transmission interval for each run was 25 milliseconds and each setup iterated 100 times. Detailed simulation results are shown in table 3. Decrease in the ratio towards high packet size is because of periodic flooding performed by each vehicle to inform others about its position and status. As the streaming packet size increase transmission time of each packet increase and boosts the probability of collision in wireless channel. Table 3 Results of Communication Relay Simulation for Various Data Packet Sizes Packet Size (bytes) 32

# of Sent packets 1681

# of Arrived packets 1673

# of Lost packets 18

Delivery Ratio (%) 98.92

64

1681

1658

23

98.63

128

1681

1649

32

98.09

7 Conclusions and Future Work The purpose of this work has been to demonstrate establishing continuous airborne communication relay by UAV platforms using autonomous APF based navigation planning approach. The simulation study has shown that communications between the vehicles are successfully obtained during the flight in chain formation. Chain formation is established and encouraged successfully during the simulation work by using APF. Also collision avoidance task between the UAV platforms is maintained successfully. This approach can be used in controlling an unmanned vehicle to extend the two way communication range. As a future work we will demonstrate the architecture to follow the mobile targets which are outside the mission base‟s communication range by establishing an APF based airborne communication relay chain. Also we are planning APF

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