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THESIS COLLOQUIUM Collision avoidance and coalition formation of multiple unmanned aerial vehicles in high density traffic environments Joel George M 1

THESIS COLLOQUIUM

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THESIS COLLOQUIUM. Collision avoidance and coalition formation of multiple unmanned aerial vehicles in high density traffic environments. Joel George M. - PowerPoint PPT Presentation

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THESIS COLLOQUIUM

Collision avoidance and coalition formation of multiple unmanned aerial vehicles in high density traffic environments

Joel George M

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“… it was nevertheless - the first time in the history of the world in which a machine carrying a man had raised itself by its own power into the air in full flight, had sailed forward without reduction of speed, and had finally landed at a point as high as that from which it started.”

Details of first flight:

Speed = 6.8 miles/hour

Range = 120 feet

Altitude = 10 feet

Orville Wright

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Faster, Farther, Higher (and Safer)

Slogan of aircraft design industry

Boundaries of speed, altitude, range, and endurance have been pushedfurther and further

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Aircraft kept the tag “machine carrying a man”

Presence of man in aircraft was always an important design consideration

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“Elimination of pilot from a manned combat aircraft removes many of the conventional design constraints …

This at once throws open the design parameter space and dramatic improvements in performance measures like increased speed, range, maneuverability, and payload can be achieved.”

Late Dr. S Pradeep

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Dull, Dirty, and Dangerous missions

In some missions, human presence ‘need not’ be there

In some other missions, human presence ‘should not’ be there

Unmanned Aerial Vehicles find applications in

Why Unmanned Aerial Vehicles (UAVs)?

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Why UAVs?

Factors compelling the use of Unmanned Aerial Vehicles (UAVs)

Design freedom (mission specific designs)

Dull, dirty, and dangerous missions

Low cost, portability, absence to human risk, …

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Why autonomous UAVs?

UAVs can be remotely piloted

However, desirable to make UAVs autonomous

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Why multiple UAVs?

Use of multiple UAVs leads to coordination problems

UAVs are often small

Collision avoidance, coalition formation, formation flying, …

Some missions are more effectively done by multiple UAVs

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This thesis addresses the problems of

Collision avoidance,

Coalition formation, and

Mission involving collision avoidance and coalition formation

of multiple UAVs in high density traffic environments

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OUTLINE

CHAPTER 1

Introduction

CHAPTER 2

Collision avoidance among multiple UAVs

CHAPTER 3

Collision avoidance with realistic UAV models

CHAPTER 4

Coalition formation with global communication

CHAPTER 5

Coalition formation with limited communication

CHAPTER 6

Coalition formation and collision avoidance in multiple UAV missions

CHAPTER 7

Conclusions

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CHAPTER 1Introduction

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Collision avoidance

Using information of positions and velocities of UAVs in the sensor range, a UAV needs to find an efficient safe path to destination

A safe path means that no UAV should come within each others safety zones during any time of flight

Efficiency less deviation from nominal path

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Have been looked at from the robotics and air traffic management points of view

Ground based robots can stop to finish the calculations

Collision avoidance algorithms addressing air traffic management problems consider only a few aircraft

Collision avoidance literature

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A situation requiring three dimensional collision avoidance

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Coalition formation

Multiple UAVs with limited sensor ranges search for targets

A target found needs to be prosecuted

A UAV that detected the target may not have sufficient resources

‘Need to talk’ to other UAVs to form a coalition for target prosecution

Objective: To find and prosecute all targets as quickly as possible

The algorithm should be scalable

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Multi-agent coalition formation

Can share resourcesExtensive communication

Multi-robot coalition formation

Resources do not deplete

Multi-UAV coalition formation

Resources deplete with useNeed quick coalition formation algorithms

Coalition formation literature

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Multi-UAV rendezvous with collision avoidance

Coalition formation with collision avoidance

Collision avoidance and coalition formation in multiple UAV missions

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CHAPTER 2Collision avoidance among multiple UAVs

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UAV kinematic model

Limited sensor range

Assumptions

Constant speedMinimum radius of turn

Further assumption

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It suffices, in case of a multiple UAV conflict, for a UAV to avoid the most imminent near miss to obtain a good collision avoidance performance.

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22/85Lesser the deviation (higher efficiency), better the collision avoidance algorithm

Objective is to reduce the number of near misses, as in a high density traffic case, it may not be possible to avoid near misses

Lesser the number of near misses, better the collision avoidance algorithm

Two UAVs within each others safety zones results in a ‘near miss’

DeviIdea

atedl path l

path leength

ngthEfficiency =

Aircraft deviates from its nominal path due to collision avoidance maneuver.

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Reduce multiple conflicts to an ‘effective’ one-one conflict by findingthe ‘most threatening’ UAV from among the ones in sensor range

UAVs encounter multiple conflicts

Most threatening UAV: A UAV U2 is the most threatening UAV for U1 at an instant of time, if

1) U2 is in the sensor range of U1

2) Predicted miss distance between U1 and U2 suggests the occurrence of a near miss

3) Out of all the UAVs in the sensor range of U1 with which U1 has a predicted near miss, the near miss with U2 is the earliest to occur

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For collision avoidance, a UAV does a maneuver to increase the LOS rate

Collision avoidance maneuver

Each UAV does a maneuver to avoid collision with the most threatening neighbor

A necessary condition for collision between two aircraft to occur is that the Line of Sight (LOS) Rate between them be zero

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Two Dimensional Reactive Collision Avoidance: RCA-2D

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Simple head-on collisions

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High density traffic

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Random flight test

Aircraft fly from random points on outer circle to random points on inner circle

Velocity: 500 miles per hourTurn rate: 5 degrees per second

Radius of outer circle 120 milesRadius of inner circle 100 miles

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Number ofAircraft

SGT RCA-2D

Near Misses Efficiency Near Misses Efficiency

20 1.95 99.45 1.35 99.0940 7.05 97.94 3.75 97.6460 17.85 94.38 12.65 96.39

Since the test case involves random flights, the simulations are run 20 times for each case, and the values presented are averaged over the results obtained from these runs

Number of Aircraft

Computation Time (sec)

SGT RCA-2D

20 638 33

40 1611 100

60 2819 206

Archibald, J. K., Hill, J. C., Jepsen, N. A., Strirling, W. C., & Frost, R. L. (2008). A satisficing approach to aircraft conflict resolution. IEEE Transactions on System, Man, and Cybernetics - Part C: Applications and Reviews, 38, 510–521.

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Std. Dev.Of Noise(miles)

Near Misses Efficiency

SGT RCA-2D SGT RCA-2D

0 1.95 1.35 99.45 99.09

0.1 8.65 1.35 99.58 98.99

0.2 12.5 1.55 99.79 99.08

0.3 14.1 1.95 99.44 99.02

Effect of noise in position measurement

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Collision plane

RCA-3D-I

RCA-3D-O

Three dimensional engagement

Three dimensional collision avoidance algorithms

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Number of Aircraft

Near Misses Efficiency

RCA-2D RCA-3D-O RCA-2D RCA-3D-O

20 1.35 0.3 99.09 99.35

40 3.75 1.0 97.64 98.06

60 12.65 2.6 96.39 96.82

Comparison of the performance 2D and 3D algorithms for random flights

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Case 1: h = 20 miles,rin = 100 miles, and rout = 120 miles

Case 2: h = 60 miles,rin = 55 miles, and rout = 70 miles

Case 3: h = 100 miles, rin = 40 miles, and rout = 50 miles

CaseNear Misses Efficiency (%)

RCA-3D-I RCA-3D-O RCA-3D-I RCA-3D-O

1 2.6 2.4 98.62 98.84

2 8.3 4.6 97.30 96.96

3 11.8 6.1 96.17 96.17

Modified random flights (three dimensional)

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Summary of Chapter 2

Developed conceptually simple collision avoidance algorithms

For two and three dimensional conflicts

For high density traffic environments

Analyzed the performance of these algorithms

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CHAPTER 3Collision avoidance with realistic UAV models

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Realistic UAV Model

Stability and control derivatives from Aviones

A UAV flight simulator developed by the Brigham Young University (an open source software)

Available: http://aviones.sourceforge.net/

The Zagi Aircraftwww.zagi.comSpan = 1.5 mMean Chord = 0.33 mWeight = 1.5 kgPicture courtesy: www.zagi.com

UAV of span 1.4224 m, weighing 1.56 kg

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PI controllers with parameters tuned manually

Controllers designed through successive loop closure

Separate controllers for holding altitude, attitude, and speed

UAV control system

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Controller design

Altitude hold controller

Similar controllers for attitude and speed holds are designed

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Implementing the guidance commands

Look-up graph for bank angle that will provide required turn rate

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Test of collision avoidance

A example of collision avoidance of 5 UAVs

The test case is set-up such that the avoidance of one conflict will lead into another

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Random flights test case

inner circle radius 400 m

outer circle radius 500 m

velocity 12 m/s

maximum turn rate 10 deg/sec.

Any approach of two UAVs within 10 m is considered a near miss

An approach within 2 m is a collision.

Test case of random flights for dense traffic

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No. of UAVswithout collision avoidance with collision avoidanceNear Misses Efficiency Near Misses Efficiency

204060

218.1899.1

2027.9

100100100

0.11.61.4

96.1589.1789.11

Results of the random flight test case

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Implementation of 3D collision avoidance algorithm

Realization of pitch and turn rate commands

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Pitch rate guidance and control loops

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No. of UAVs Near Misses Efficiency204060

0.41.22.7

99.9299.8699.79

Results of the random flight test case

No. of UAVs Near Misses Efficiency204060

0.61.52.3

99.9099.8299.75

for heterogeneous UAVs

for homogeneous UAVs

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Collision avoidance in presence of non-cooperating UAVs

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Summary of Chapter 3

Implemented collision avoidance algorithms on 6 DoF UAV models

Simulations with heterogeneous and non-cooperating UAVs

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CHAPTER 4Coalition formation with global communication

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Destroy the target is minimum time

Coalition should have minimum number of UAVs

Rendezvous on target to inflict maximum damage

Search targets and destroy them

The targets may have different requirements

Objectives:

Coalition formation for search and prosecute mission

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Limited sensor radius

Target locations are not know a priori

Limited resources that deplete with use

Stationary targets

Global communication

Assumptions

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Theorem: The minimum time minimum member coalition formation for a single target is NP-hard

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Coalition leader initiates the coalition formation process

UAV that detects the target – Coalition leader

Deadlocks are handled by rules/protocols

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Communication protocol for coalition formation process

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Stage IIFind a minimum member coalition

Two stage algorithm for coalition formation

Stage IFind a minimum time coalition

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Stage I: Minimum time coalition

Theorem: Finding minimum member coalition is NP-hard

Recruit members to coalition in the ascending order of their ETA to target

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Theorem: Stage I gives a minimum time coalition

Theorem: Stage I has polynomial time complexity

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Stage II

‘Prune’ the coalition formed in stage I to form a reduced member coalition

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Coalition formation examples

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Solution using Particle Swarm Optimization (PSO)

Global solution of the search and prosecute problem using PSO

Target locations known a priori

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Comparison of solutions

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Summary of Chapter 4

Coalition formation algorithm for search and prosecute mission

Two stage polynomial time algorithm

Efficacy of the algorithm demonstrated via simulations

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CHAPTER 5Coalition formation with limited communication

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Dynamic network over which coalition formations should take place

UAVs have limited communication ranges

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Log of messages kept to avoid duplication

Every UAV acts as a relay node

Each hop of message has an associated lag

Time-to-live for a message

Network properties

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Works well as coalition formation period is much shorter than the time scale in which network connection varies

Coalition formation over dynamic network

Find a sub static coalition formation period

A UAV accepts to be a relay node only if sub-network that is over the UAV it is in communication range for the entire coalition formation period

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Example of stationary and constant velocity target

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A coalition member prosecutes the target and continues to track it until the target is within the sensor range of the next coalition member

Prosecution sequence for maneuvering target

Rendezvous at a maneuvering target is difficult sequential prosecution

Coalition leader tracks the maneuvering target and broadcast this information until the target is in the sensor range of one of the coalition members

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Example

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Performance of coalition formation algorithm with increase in number of UAVs

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Performance of coalition formation algorithm with increase in communication range

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Performance of coalition formation algorithm with increase in communication delay

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Summary of Chapter 5

Coalition formation of UAVs with limited communication ranges

Prosecution of stationary, constant velocity, and maneuvering targets

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CHAPTER 6Collision avoidance and coalition formation in multiple UAV missions

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Rendezvous – meeting at a pre-planned time and place

Rendezvous of multiple UAVs

For simultaneous deployment of resources

To exchange resources or critical information

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Multiple UAV Rendezvous

Uses a consensus on Estimated Time of Arrival (ETA) at target

Rendezvous under collision avoidance

Rendezvous of multiple UAVs when some of the UAVs have to do collision avoidance maneuvers en route

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Multiple UAV Rendezvous Algorithm

If velocity hits lower bound, then ‘wander away’ from the rendezvous point

Consensus in ETA achieved using

Velocity control within boundsWandering maneuver

Change in velocity proportional to (average ETA – ETA)

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Solution Approach

In principle, any consensus protocol can be used.

Average consensus protocol is used for the purpose of illustration

Linear average consensus

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Rendezvous: Simulation Results

Rendezvous of 5 UAVs (3 of them do collision avoidance on the way)

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Target tracking

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Coalition formation with collision avoidance

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Summary of Chapter 6

Multiple UAV rendezvous with collision avoidance

Coalition formation with collision avoidance

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CHAPTER 7Conclusions

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Algorithms for collision avoidance and coalition formation and their applications

Algorithms are

conceptually ‘simple’

scalable

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Better controller implementations possible for collision avoidance

Better communication protocols possible for coalition formation

Possible extensions of present work

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