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THREE-DIMENSIONAL MULTI-OBJECTIVE UAV PATH

PLANNER USING META-PATHS FOR DECISION

MAKING AND VISUALIZATION

Levi Swartzentruber 1

, Jung Leng Foo2, and Eliot H. Winer

3

Virtual Reality Applications Center, Iowa State University, Ames, IA 50010, USA

Military operations are turning to more complex and advanced automation technology for

minimum risk and maximum efficiency. A critical piece to this strategy is unmanned aerial

vehicles (UAVs). UAVs require the intelligence to safely maneuver along a path to an

intended target, avoiding obstacles such as other aircrafts or enemy threats. Often

automated path planning algori thms are employed to s pecify targets for a UAV to fly to. To

date, path-planning algorithms have been limited to providing only a single solution

(alternate) path without further inputs from the UAV controller. This paper presents a

unique platform for decision making in a three-dimensional path planner where multiple

solution paths are generated in the form of meta-paths. The path planner uses Particle

Swarm Optimization (PSO) to generate multiple solution paths based on predefined criteria.

The problem formulation was designed to minimize risk due to enemy threats, to minimize

fuel consumption incurred by deviating from the original path, and takes into account

reconnaissance targets. Using PSO, al ternate paths are generated using B-spline curves,

optimized based on preferences set for the three objectives. The resulting paths can be

optimized with a preference towards maximum safety, minimum fuel consumption, or target

reconnaissance. For each preference, the top five solutions generated by PSO are presented,

for a total of 15 alternate paths. In order to efficiently present these multiple solution paths,

meta-paths are implemented, which is a single summarized representation of all the paths

generated for a particular preference. This allows the decision making process to be

completed in an efficient and organized manner. The problem formulation and solution

implementation is described along with the results from several simulated scenarios.

I. Introduction

ilitary combat of the future will become highly dependent on the use of unmanned aerial vehicles (UAVs). In

recent years, there has been rapid development in UAV technology such as swarm communicat ion, command

and control, and developing usable interfaces 1. The complexity in UAV technology is rapidly growing, and

according to the Department of Defense (DOD) Roadmap 2, by the year 2012 it is estimated that F-16 size UAVs

will be able to perform a complete range of combat and combat support missions. Thus, the ground control station –

the human operator’s portal to the UAV – must evolve as UAVs grow in autonomy. The ground control station must

facilitate the transformation of the human from p ilot, to operator, to supervisor, as the level of interaction with

UAVs moves to ever-higher levels. As humans interface with UAVs at more abstract levels, a UAV will be trusted

to do more 3. To develop and maintain that trust, a human must be able to understand a UAV’s situation quickly.

Future ground control stations will need to provide an operator with situational awareness and quality information at

a glance.

To address the many research issues involved in the command and control that the DOD roadmap requires, a

“Virtual Battlespace” at Iowa State University was created. In this paper, research into the issue of three -

1 Research Assistant, Department of Mechanical Engineering & Human Computer Interaction, Virtual Reality

Applications Center, 2274 Howe Hall, Iowa State University, Ames, IA 50010, USA, Student Member. 2 Research Assistant, Department of Mechanical Engineering & Hu man Computer Interaction, Virtual Reality

Applications Center, 2274 Howe Hall, Iowa State University, Ames, IA 50010, USA, Student Member. 3 Assistant Professor, Department of Mechanical Engineering & Human Computer Interaction, Virtual Reality

Applications Center, 2274 Howe Hall, Iowa State University, Ames, IA 50010, USA, Member.

M

12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 10 - 12 September 2008, Victoria, British Columbia Canada

AIAA 2008-5830

Copyright © 2008 by Levi Swartzentruber. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

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dimensional (3D) path planning for UAVs as part of the Virtual Battlespace project is presented. The method

described allows a human operator to focus on selecting an appropriate path from a set of alternate paths produced

by the path planner, easing the decision making process. Using a Particle Swarm Optimization (PSO) algorithm, the

task of generating alternate paths is formulated into an optimization problem consisting of three main components:

1) for UAVs to avoid obstacles such as threats (e.g., surface to air missile sites, tanks, and aircraft), 2) maintaining a

fuel-efficient path to maximize mission range, and 3) minimal deviat ion from the original way-points for

reconnaissance purposes.

In the following sections of this paper, the background of the Virtual Battlespace project is presented. This is

followed by a detailed description of the development and implementation of the 3D path planner using a Part icle

Swarm Optimization algorithm. The process of generating the meta-paths for effective visualization of multiple

alternate paths is also discussed. The resulting paths generated from the planner from multiple simulated scenarios

are then presented, with conclusions and future work discussed at the end.

II. Background

A. Virtual Battles pace

Development of the Virtual Battlespace originated in 2000 when a research team at Iowa State University’s

Virtual Reality Applicat ions Center (VRAC) began work with the Air Force Research Lab’s Human Effect iveness

Directorate and the Iowa National Guard’s 133rd Air Control Squadron. The goal of this preliminary version of the

Virtual Battlespace was to develop an immersive VR system for distributed mission training . Virtual Battlespace

integrates information about tracks, targets, sensors and threats into an interactive virtual reality environment that

fuses the available information about the battlespace into a coherent picture that can be viewed from mu ltiple

perspectives and scales 4, 5

. Visualizing engagements in this way is useful in a wide variety of contexts including

historical mission review, mission planning, prebriefing, post-briefing, and live observation of mission training

scenarios. This system comprises of set of tools developed to enhance the user experience using voice recognition,

immersive visualizat ion, and intuitive interaction using a wireless gamepad controller. In addition to that, a tablet

interface with d irect manipulation where mission specific details can be viewed and updated in real t ime was also

developed. The environment in a large-scale VR environment is shown in Fig. 1.

Figure 1. Virtual Battlespace in the C6 six-wall p rojection system at Iowa State University’s Virtual Reality

Applications Center.

B. Path Planning for UAVs

Real time dynamic path alteration is needed when a UAV is presented with an unexpected threat. For example, a

UAV could encounter an unexpected surface-to-air missile (SAM) site. When this happens the operator must be

alerted to this dangerous situation and be able to quickly re-task the UAV to reduce its threat exposure while

considering other factors such as fuel usage.

It is important to consider the impact of the immersive environment on this process. In a con ventional two-

dimensional (2D) interface, the application would have to find some way to convey a 3D path in the 2D interface or

restrict the path-planning algorithm to a 2D solution; limit ing any alternate paths to changes in direction within the

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same elevation when in reality an aircraft could also change altitude to avoid threats. This limitation is lifted since

the Virtual Battlespace operates in an immersive virtual reality environment, which allows true 3D interaction. As

such, there is a need for a path planner application that functions in 3D space. With this tool, the operator can focus

on the decision to be made as opposed to inferring the true shape of the path.

There has been extensive research in the area of path planning especially in the artificial intelligence,

optimization, and video game communit ies but most have been restricted to a 2D form 6, 7

. One of the most popular

path planning algorithms in the video game/art ificial intelligence communit ies has been the A* path planning

algorithm. The A* algorithm strength lies in the ability to heuristically judge or value the best path from point to

point. If this cannot be done with reasonable accuracy then the A* method will not be very effective 8. Th is is not

possible considering the dynamic nature of the battlefield and the variable cost of particular parts of the environment

based on differing criteria. Without human intervention, the path planning algorithms must be able to adapt to a

variable mission environment. This has lead to research of great interest not only to UAV control but other fields

such as robotics 9 – 13

.

Without the ability for human intervention of the UAV’s path, the Air Controllers and human p ilots are solely

responsible for maintaining a manageable airspace. Keeping a human in the loop helps prevent catastrophic mistakes

by taking advantage of the human’s ability to handle and process outside information. The human operators can

issue overall object ives and commands to the vehicles under their control. The issuing of obje ctives as opposed to

exact paths can reduce the amount of awareness needed to control an individual UAV. This reduction could result in

more UAVs under the control of a single operator.

Because of the variable cost nature of the types of path planning that will be done with UAVs, a particle swarm

optimization (PSO) method of path planning was developed. To maintain a human input in the decision making

process, several paths are generated by the developed method. The generated alternate paths are represente d by B-

Spline curves to minimize computation, since a simple curve can be eas ily defined by as little as three control points

and this method has been successfully used to model constrained curves 14

.

C. Decision Making in Path Planners

Decision making forms an important aspect of UAV path planning as a human presence is included in the path

selection process to validate proposed actions of the UAV. The path planner operation and output must be designed

to facilitate quick and accurate interpretations of the data so the UAV controller can choose the appropriate

alternative under the tight time constraints of a combat situation. When military officers encounter such situations,

decisions are generally made by creating a vision for the mission outcome, generating a plan which is evaluated and

refined, and finally issuing the specific orders to carry out that plan. Most frequently, it is intuition based on

experience that leads to the best action plan 15

. The path planner generates a number of alternate paths, relieving the

operator of this task, and allows that person to focus on evaluating the paths in light of the mission objective and

choose the best alternative. With more alternatives there is a much greater chance the operator will intuitively

recognize a path that fits very closely with the mission objective.

Intuition and past experience form a core for how the decision will be made. The three stages involved in this

process are model construction, revision, and falsificat ion. Stage one involves making a mode l of the situation. Then

the user seeks out more detailed information to come to a preliminary conclusion which is accepted if it is not

falsified by further information 16

. Such a thinking process would lend itself well to a tiered informat ion display

approach.

Information display techniques can significantly influence the operator’s ability to make that decision well. The

operator will be biased toward interactive v isual information which is absorbed most quickly while textual

informat ion is processed more slowly and given less weight in decision making processes 17

. Proper path display will

leverage this bias by giving key information, such as path optimization criteria and waypoint positions, the most

visual weight.

More information allows for a better situation up to the point where the user can no longer process it quickly.

Several tactics including overview + detail, focus + context and in formation h iding are commonly employed to

prevent information overload as a user investigates the data 18

. Utilizing these types of techniques would allow a

user time to evaluate more alternatives than before in the same amount of time and make a better-informed decision.

Most research on decision making in path planning takes the alternative approach where the goal is to

completely eliminate the need for human input. The methods include dynamic path planning algorithms that

continuously plan short sections of the path 19

and a hierarchical planning structure to develop an overall solution

which is refined as path sections are analyzed in increased detail 20

. Though they are not currently used in UAV path

planning as described in this paper, they could form the basis for UAV control and decision making in the future.

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The other approach is based on the concept that there can be multip le good alternate paths for a given situation.

One algorithm was designed for dynamic environments using cluster analysis with an evolutionary algorithm. This

would prevent the planner from needing to recalcu late automatically if the dynamics of the situation changed

because there would still be other alternate paths available 21

. The user would select the path that would best fit with

his analysis of the dynamics of the situation.

D. Particle S warm Optimization (PSO)

To facilitate the search for optimal paths, a particle swarm optimizat ion (PSO) technique was used to produce a

large number o f candidate paths for evaluation. PSO is a heuristic optimization method that is based on the

movement of insect swarms introduced in the mid 1990s by Kennedy and Eberhart 22

.

In PSO, an initial randomly generated population swarm (a collection of part icles) propagates towards an

optimal point in the design space, and reaches the global optimum over a series of iterations. Each particle in the

swarm explores the design space based on the informat ion provided by previous best particles. PSO then uses this

informat ion to generate a velocity vector indicating a search direction towards a promising design point, and updates

the locations of the particles.

After reviewing the various current methods and research being done for path planning of UAVs, this paper

presents a new method of path planning in 3D space using the PSO algorithm to generate alternate optimal paths,

using meta-paths for better decision making.

III. Methodology

The path planning process is initialized by determining the starting and ending points of the current path. The

waypoints from the original path are then mapped as reconnaissance targets. The start and end points and any

waypoints in between form the in itial design points of the problem. From this init ial design point, a search space is

defined to scan and locate other UAVS with in range and identify possible threats. The size of the search space is left

open to the user’s judgment, setting it too large will incur a longer computation time, while having a search space

that is too small might cause some UA Vs to be unaccounted for. Fig. 2 shows the process flowchart of the path

planning using PSO.

Once position data of the UAVs within range are obtained, enemy entities are singled out and a 3D threat zone is

generated for each of them. A threat zone is defined as a sphere (a hemisphere for ground vehicles) of radius RT

(user defined) surrounding the obstacle that the path needs to avoid. Threat zones are also generated for non-enemy

(friendly) entities to avoid collision, but with a smaller radius. In addition, reconnaissance zones are also defined as

hemispheres of radius RR. By default, the values for RT are set to be 20,000 feet and RR at 2,000 feet, but can be

changed to suit the UAV controller’s preferences.

Figure 2. Flowchart o f the path planning process using Particle Swarm Optimizat ion.

Identify waypoints of original path and set as reconnaissance targets

Identify enemies or obstacles and generate threat zones.

Run Part icle Swarm Optimizat ion (PSO) to obtain optimal path(s).

Identify start and end points for path planning.

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A. Cost Formulation

Formulat ion of the optimization cost function begins with the description of a B-spline curve to represent the

path of the UAV. Consider the B-spline curve p0(ui), where ui is a sequence of line segments forming the curve, that

requires re-planning when it violates a threat zone ZT (in red), shown in Fig. 4. A resulting alternate curve path p(ui)

is generated that avoids the threat zone while still attempting to be within the reconnaissance zones (in green) and is

illustrated by the red curve in Fig. 4.

The cost function components also depend on the number of parametric samples (line segments that form the

curve) N that define the resolution of the curve. Here, N is user defined and the value of N brings a trade-off effect

between accuracy of the curve and computational efficiency. The cost function components are summations of the

curve characteristics sampled at the regular parametric intervals,

u0 = 0 and

121 ,1

11 ,....,N-, i

Nuu ii

(1)

The initial solution to begin the optimization process is the original path that breaches the threat zone thus

violating the constraint, illustrated in Fig. 4 by the blue dashed line. A new path can be computed by running the

PSO such that the interior control points (between the end-points) satisfy the constraints. To achieve this, the cost

function needs to accommodate the preferences of safety, reconnaissance missions, as well as fue l efficiency of the

alternate paths. The total cost function is represented by the following components:

C =K1 CT + K2 CL + K3 CR (2)

where, CT is the cost due to proximity of enemy entities and violation of the threat zones, CL reflects the cost

incurred from excessive arc length and deviation from the original path, and CR is the cost incurred by deviating

from the reconnaissance locations.

The constants K1, K2, and K3, in Eq. (2), are component weights that determine the relat ive emphasis of the

various cost components with respect to the overall cost function. Each weight is normalized between zero and one.

If a weight is zero then that particular cost function is unimportant for a particular run. All weights sum up to 1.0 in

total. These weighted cost components are then added together to form the total cost function of a particular path.

Table 1 shows an example of generating a set of three different alternate paths, each with its own preference.

Table 1. Example of component weights used to generate a set of alternate paths.

Threat Weight, K1 Fuel Weight, K2 Recon Weight, K3

Threat Avoidance 0.90 0.05 0.05

Fuel Efficiency 0.05 0.90 0.05

Reconnaissance 0.05 0.05 0.90

Figure 4. Two-d imensional illustration of a simple threat zone avoidance problem.

p0(u)

p(u) ZR

ZT

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The threat component CT requires a function to determine the distance from a point p(ui) along the curve p(u) to

a UAV inside the threat zone ZT and is denoted here as d(p, ZT). The function will return a positive value if there is a

violation of the threat zone, and negative one otherwise. With this, the threat cost is then defined a s:

d(p(ui), ZT) = (Threat zone radius, RT) – (Distance between point p(ui) and threat) (3)

0)),(p(

0)Z),(p(

0

,)Z),(p( ,

T1

0

T

Ti

iN

i

i

iiTZud

ududCCC (4)

A significant violation of the threat zone will result in a significant increase in the threat component of the cost

function. Since this simple zone violat ion constraint allows many possible solution curves with probably

unacceptably large length, the second component will simultaneously min imize the curve arc length, thus providing

a solution with the best fit possible along the obstacles.

The curve length component of the cost function is computed using a chordal approximation of the total curve

length, L, relat ive to the initial solution obtained from a line connecting the endpoints. The curve length component

is expressed as follows:

path original ofLength :

)(p)(p ,

0

2

0

10

L

uuLLLCN

i

iiL

(5)

The curve length component translates as a difference between the generated path and the original path. This

represents the additional fuel expense from the alternate path. Should the path planner find a shorter route

(regardless if it violates a threat zone), this component will return a negative value, thus turning this component into

a reward rather than a cost. The goal is to generate a new path for the UAV that avoids a threat, with the lowest

additional fuel expense simultaneously.

A third and final component is for reconnaissance, CR, which is a function to determine the distance from a point

p(ui) along the curve p(u) to a particular waypoint location ZR and is denoted here as d(p, ZR). The reconnaissance

component increases the objective function when the path is outside the specified reconnaissance zone. The further

the alternate path is from the waypoints, the higher the cost to the objective function. With this, the reconnaissance

cost is defined as:

d(p(ui), ZR) = (Distance between point p(ui) and center of ZR) - (Reconnaissance zone radius, RR) (6)

0)Z),(p(

0)Z),(p(

0

,)Z),(p( ,

R

1

0

R

i

RiN

i

i

iiRud

ududCCC (7)

B. Meta-path generation

The PSO algorithm is run three times with the weightings described earlier. Normally, only the best path is

taken, disregarding other potentially good paths found in the solution process. To better utilize the information

generated by the PSO algorithm, five of the top paths from each of the three PSO runs are made available to the

UAV operator.

In order to avoid an overload of visual informat ion to the user, the total of 15 alternate paths are presented as

meta-paths. Three meta-paths are created for visualization; one for each group of five paths. As an intermediate step

in the decision-making process, the three meta-paths need to present the operator with enough informat ion to make a

decision on which set of paths to investigate further. The specific data for indiv idual paths is not needed until the

final step. They are designed to preserve the trends of the underlying data in a simplified fashion so the user can

quickly evaluate the alternatives.

Fig. 5 p rovides a process flowchart for how the meta-paths are generated from the PSO data.

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Figure 5. Process flowchart fo r meta-path creation

Once the PSO path data is acquired, the next step is to select the five paths from the swarm that will be used to

make the meta-path. The first selection criterion is the path’s fitness value. Fitness value and optimality are inversely

related so paths with lower fitness are preferred. By default, the first path selected is the global best, which would

have been returned if only one solution were kept.

To select the next four paths to be returned, a second criterion is needed. As the evolutionary algorithm solves

the problem, solutions will tend to cluster around the optimal point. It is probable that choosing paths solely on

fitness value will lead to duplicate paths being chosen. This would be a case of extra in formation being presented to

the operator with no value being added. The second criterion is added to address this problem.

The second selection criterion is based on the need for the alternate paths to be physically distinguishable. Each

potential path is compared with the paths that have already been selected. Paths are handled in order, moving from

the fittest to the least fit until four are found that meet the requirement. In each comparison the total difference

between the positions of the path points is calculated. If the difference is less than a threshold in any comparison, t he

path is discarded. If the difference is acceptable in all cases, the path is added to the list of alternate paths. The

comparison is laid out in Eq. (8) and Eq. (9):

(8)

(9)

The difference in point locations, diff, is shown in Eq. (8). Th is equation is evaluated n times, where n is the

number of paths already selected. The sum is evaluated for each point in the path, i. Here, pcur is the point in the path

being considered as an alternative path and pn is the nth

path already selected. The x, y and z represent the x, y, and z

coordinates for the ith

point for the respective paths.

The difference values, diffn, for the path under consideration are then compared with a user defined constant. The

constant value, C, is the number of points in the paths mult iplied by a scale factor. The factor is experimentally

chosen to provide good visual separation between paths.

For each of these selected paths, a relative fitness value is computed. With the path planner returning mult iple

paths, sub-optimal paths are being presented to the operator. The user needs a measure of how far the solutions are

from optimal to make an intelligent selection. Relative fitness is a ratio of the fitness of a given path to the fitness of

the best path found through the evolutionary algorithm. This is chosen over presenting the raw fitness values

because the ratio presents the operator with a standard baseline for what is good when fitness values can vary

significantly between test cases.

The calculation for relative fitness is shown in Eq. (10). The calcu lation is performed for each of the five

alternate paths, i. The fitnesspath i is the fitness value for the ith

path and the fitnesspath 1 is the fitness value for the

optimum path which is saved as path one.

(10)

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In relative fitness, the optimal path has a value of 100. All other paths have larger fitness values than the optimal

solution and will therefore have relat ive fitness values greater than 100. The larger the relative fitness value, the

more cost the path incurs and the farther it is from optimal.

The third step is to take these five paths and create a single visual representation for them, a meta -path, which

would be displayed to the UAV operator. The meta-paths need to convey generally what the underlying paths look

like so the user can make an informed decision and is not surprised by the individual path characteristics when they

are displayed. As seen in Fig. 6, a meta-path is a bent, variable diameter tube. It is visualized half open so the user

will be able to see all three paths even when one is in front of or overlaps another. The geometry of the meta -path is

defined by the bend locations and a radius value at those locations.

Below the meta-paths in Fig. 6 are the 15 individual paths in their groups of 5. It is clear that presenting all 15 of

these paths to the user at once would cause informat ion overload. It can also be seen how the individual paths, when

mentally grouped, would intuitively be represented in a manner similar to the meta-paths used here.

Figure 6. Meta-path representation with same v iew of all 15 alternate paths

This similarity is created by constructing the geometry of the meta-path from the data for the five paths it

represents. The bend locations, or waypoints, of the meta-path are the averaged locations of the waypoints for the

five individual paths. Eq. (11) demonstrates how this is accomplished. For the meta-path, there are j points with x, y,

and z components. The meta-path point is pmeta-path. The individual path points are pi where i is ranges from 1 to 5 for

the 5 paths.

(11)

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The radius of the meta-path at a bend location is defined as the average separation, in three dimensions, between

the meta-path point and the five individual waypoints. Using th is radius value, the meta-path occupies roughly the

same volume visually as the five individual paths. This calculation is shown in Eq. (12):

(12)

The radius is a value calcu lated at each waypoint, j, of the paths. Again, it is necessary to average the calculation

over all 5 individual paths, i. The square root term is a distance calculation in three dimensions. As before, pmeta-path

is the meta-path point and pi is the path point. The xj, yj, and zj are the x, y, and z components of the position of the

jth

point. A larger radius value means there is more physical distance between the path points at a given waypoint

and results in a larger meta-path. The reverse is also true. Since the beginning and ending points are the same for

all paths, each meta-path comes to a point at both ends.

C. Path Selection

The second aspect of path planning is the decision making process performed by the operator. Meta -paths are

designed to give this individual a wider base of options to consider when tasking the UAV. In doing this, another

layer was added to the decision making process, which is shown in Fig. 7. The operator must select a meta -path, an

individual path, and then confirm that selection for the alternate path to replace the current path. It is possible to

navigate freely between the indiv idual path mode and meta-path mode so the operator can investigate all 15

possibilit ies if that is necessary to find a suitable solution. The operator also reserves the ability to quit at any time

and use the original path tasked to the UAV.

Figure 7. Flowchart for path selection process

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IV. Implementation of PSO Path Planner into Virtual Battlespace

The purpose of an immersive command and control station is to permit the operator to focus on the overall

mission status. As the number of aircraft under an operator’s control increases, it becomes impossible to constantly

monitor and manage every aircraft. To facilitate this, an alert subsystem was developed as part of the Virtual

Battlespace to alert the operator to any issues. The alert subsystem plays a vital ro le in reassuring the operator that

when UAVs run into situations that require user input, the operator will be made aware of them.

The path planning process begins when a threat is detected by the alert subsystem of Virtual Battlespace, which

prompts the controller for a decision on the next action. The controller can either ignore the alert or choose to

inspect it. The process ends here and does not execute the path planner if the controller decides to ignore the alert.

The alert subsystem, seen in the right image of Fig. 8, notifies the operator of the presence of an alert and when

the operator chooses to examine an alert posted by a threatened UAV, the operator will see a variety of

automatically generated path options. These path options will appear at a distance corresponding to a default value

of 30 seconds ahead of the UAV’s current position and reengage with the path when in a safe region. This lead -time

can be adjusted by an operator. These points on the old path are used as the start and end points of the path planner.

All relevant threats, reconnaissance targets, and the start and end points are passed to the path planner to calculate

new candidate paths.

Figure 8. Illustration of a path in the Virtual Battlespace environment (left ) and a threat alert d isplay (right).

Several distinct scenarios were used to test the developed path planner. For each scenario, three alternate sets of

paths were generated. For the purpose of this paper the parameter settings used were those in Table 1. However, an

operator can adjust these weights if additional paths for rev iew are desired. The first alternate path has preference

towards reconnaissance locations. This is significant when the importance of a UAV’s current mission or future

mission may demand that the UAV stay as close as possible to its original waypoints . The second path is weighted

towards threat avoidance, and the last path makes a preference for fuel efficiency (minimal fuel expense for an

alternate path). The operator also has the option to vary these parameters to fit the mission objective.

The generated paths are represented in different colors with unique labels for easy identification and inspection,

and are represented as follows:

Table 2. Color and label representations of generated alternate paths.

Color Label

Reconnaissance Blue A

Threat Avoidance Green X

Fuel Efficiency White Y

A. Simulated Test Scenario #1

The first scenario test is one where three threats are situated in close proximity to each other and are in the way

of the UAV’s original path, as can be seen in the right view of Fig. 9. The threats are represented as red spheres and

their corresponding threat zone is represented as a set of red rings. After running the PSO path plann er, the resulting

meta-paths were generated and displayed as shown in Fig. 9.

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Figure 9. Right and top meta-path views for the first scenario.

The meta-paths in Fig. 9 reveal some general trends of the individual paths used to create them. For instance, the

larger spread of the threat avoidance meta-path suggests its paths are more varied than the reconnaissance or fuel

efficiency paths. Its paths also tend to stay farther away from the threats while the reconnaissance paths stay closer

to the original path and the fuel efficiency path tries to take the shortest route. The operator has the option to

investigate any of the meta-paths or use the return functionality to look at all three groups of paths.

a) Reconnaissance alternate paths

c) Fuel Efficiency alternate paths b) Threat avoidance alternate paths

Figure 10. Alternate paths displayed when respective meta-path is selected

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Each of the meta-paths breaks down into its five individual paths when selected. Fig. 10 shows the alternate

paths for each of the choices. In each case, the best path is initially highlighted. The highlighted path is the currently

selected path and its relative fitness value is displayed on the screen. Relat ive fitness provides a numerical means for

the operator to evaluate the alternate paths in addition to the visual inspection. The optimum value is 100 and less -

than-optimal solutions have larger values. Thus, the operator can weigh the optimality of the solutions against their

physical layout to make an informed decision about how to reroute the UAV.

Table 3 shows all the relative fitness values for each of the paths in this test case. As mentioned, the first path is

the basis for the comparison and will always have a value of 100. It is apparent that the reconnaissance paths have a

much s maller range of values than the threat avoidance paths. This is due in large part to the problem formulation

within the PSO algorithm. Reconnaissance paths are constrained to move in a relatively s mall area since they need

to get close to the waypoints. Threat avoidance paths simply want to stay away from the threats and so have a much

wider range of possibilities and therefore a wider range of values. Fuel efficiency paths are most affected by path

length and since all paths begin and end at the same spot and have the same number of waypoints, large differences

are not expected in this group.

Table 3. Relative fitness values for all 15 paths returned by the planning algorithm

Path Relative Fitness Path Relative Fitness Path Relative Fitness

1 100.0 1 100.0 1 100.0

2 100.7 2 106.5 2 103.3

3 100.9 3 118.2 3 103.5

4 101.1 4 125.9 4 104.0

5 101.3 5 142.7 5 104.8

Reconnaissance Threat Avoidance Fuel Efficiency

The threat avoidance case was selected to investigate in more detail in this scenario. The optimal path, on the

left in Fig. 11, travels around the upper left side o f the threat dome. A second interesting goes essentially straight

over the top of the threat dome as shown in the middle of Fig. 11. Its cost is 125% of that for the optimal path. This

option would be attractive for keeping the UAV over the initially created waypoints. A third interesting path curves

through the upper middle of the thread dome. This path avoids the very steep climb and descent of the middle path

in Fig. 11 but has a higher relative fitness since it passes closer to the threats. These are characteristic of the

decisions and tradeoffs presented to the UAV operator. With the additional paths, it is possible to find a path that

better fits what the operator would like the UAV to do. Fig. 12 presents the finalized path, which was the best path

from this group.

Figure 11. Three of the alternate paths for threat avoidance

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Figure 12. UAV path updated with chosen alternate path

B. Simulated Test Scenario #2 and #3

Figure 13. Test Scenario #2

a) Threats and meta-paths for the scenario b) Optimum fuel efficiency path highlighted

c) A lternate fuel efficiency path selected d) UAV path updated with selected path

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The second scenario path features two threats positioned together on one side of a bend in the UAV’s path. The

threats can be seen in Fig. 13 along with the meta-paths. The meta-path for reconnaissance cuts closer to the threats

to stay close to the original path. The threat avoidance and fuel efficiency take almost identical paths, bulging away

from the threats, though perhaps not moving out as far as would be expected as these paths are still well within the

threat dome.

For this scenario, the fuel efficiency paths were investigated. As seen in Fig. 13, these paths take a similar

trajectory up and to the right of the original path to avoid the threats. The main difference between the paths is their

separation from the threat. The optimal path in this case is the one that is positioned furthest to the right. In this case,

a solution from the middle of the group was chosen. It had a slightly higher fitness value but deviated less from the

original path. This path, the optimal path, and the final UAV course are shown in Fig. 13.

The third scenario includes two threats closely spaced to one side of an essentially straight path as shown in Fig.

14. The meta-paths exhibit expected behavior in this situation. The threat avoidance paths lead around the left side

of the threat dome. Fuel efficiency and reconnaissance paths go straight to take the shortest path with a moderate

increase in elevation.

The reconnaissance paths are investigated in this scenario. As seen in Fig. 14, these paths essentially stack on

top of each other. Selecting between these paths becomes a matter of how much space should be maintained

between the threats and the UAV. In this case, the middle of the five paths was chosen and is pictured in Fig. 14.

Figure 14. Test scenario #3

a) Front view of meta-paths b) Right view of meta-paths and threat dome

c) Reconnaissance alternate paths d) UAV path updated with selected path

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V. Conclusion

A three-dimensional path planner was developed to intelligently generate a set of alternate paths to be s elected

by an operator of a UAV. Based on the top generated paths for each preference, a meta -path is generated. Meta-

paths allow for effect ive visualization of mult iple paths for decision making purposes. From the test cases using

different simulated scenarios, the three-dimensional PSO path planner successfully generated alternate paths

satisfying its respective objective as set in the component weight parameters. The three objectives to either maintain

as much of the original path as possible for reconnaissance purposes, to ensure maximum safety, or to maintain

maximum fuel efficiency were successfully satisfied. Most importantly, these paths were generated in real time to

allow for efficient decision making by the UAV controller.

By performing the path planning in three-dimensional space, the solution paths presented are more realistic to

what UAVs are actually capable of performing. The option of selecting a particular path from a set of solutions

ensures that the human factor is still part of the decision making process. With multiple views to evaluate the

generated alternate paths allows the operator to make informed decisions based on the current mission objective.

The feedback that has been received from experts within the field of UAV control indicat es that this is a relevant

and interesting concept that warrants further investigation.

Pending implementations to the existing path planner involve the addition of the functionality to be added is to

develop a path planner that is dynamic in nature to incorporate time as a variable when alternate paths are being

generated, since the position of threats could change in the future of the alternate path. Another implementation in

progress is the inclusion of terrain informat ion in the path planning process.

Acknowledgments

This research was supported by the Air Force Office of Special Research Labs.

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