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

Joel George M

“… 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.”

Orville Wright

Details of first flight:

Speed = 6.8 miles/hour

Range = 120 feet

Altitude = 10 feet

Slogan of aircraft design industry

Faster, Farther, Higher (and Safer)

Boundaries of speed, altitude, range, and endurance have been pushed

further and further

Aircraft kept the tag “machine carrying a man”

Presence of man in aircraft was always an important design consideration

“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

Why Unmanned Aerial Vehicles (UAVs)?

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

Dull, Dirty, and Dangerous missions

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, …

Why autonomous UAVs?

UAVs can be remotely piloted

However, desirable to make UAVs autonomous

Why multiple UAVs?

UAVs are often small

Some missions are more effectively done by multiple UAVs

Use of multiple UAVs leads to coordination problems

Collision avoidance, coalition formation, formation flying, …

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





Collision avoidance among multiple UAVs


Collision avoidance with realistic UAV models


Coalition formation with global communication


Coalition formation with limited communication


Coalition formation and collision avoidance in multiple UAV missions





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

Collision avoidance literature

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

A situation requiring three dimensional collision avoidance

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

Coalition formation literature

  • Multi-agent coalition formation

    • Can share resources

    • Extensive communication

  • Multi-robot coalition formation

    • Resources do not deplete

  • Multi-UAV coalition formation

    • Resources deplete with use

    • Need quick coalition formation algorithms

Collision avoidance and coalition formation in multiple UAV missions

Multi-UAV rendezvous with collision avoidance

Coalition formation with collision avoidance


Collision avoidance among multiple UAVs


UAV kinematic model

Constant speed

Minimum radius of turn

Further assumption

Limited sensor range

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.

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

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

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

Efficiency =

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

UAVs encounter multiple conflicts

Reduce multiple conflicts to an ‘effective’ one-one conflict by finding

the ‘most threatening’ UAV from among the ones in sensor range

Most threatening UAV:

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

U2 is in the sensor range of U1

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

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

Collision avoidance maneuver

A necessary condition for collision between two aircraft to occur is that

the Line of Sight (LOS) Rate between them be zero

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

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

Two Dimensional Reactive Collision Avoidance: RCA-2D

Simple head-on collisions

High density traffic

Random flight test

Aircraft fly from random points on outer circle to

random points on inner circle

Velocity: 500 miles per hour

Turn rate: 5 degrees per second

Radius of outer circle 120 miles

Radius of inner circle 100 miles

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

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

Effect of noise in position measurement

Three dimensional engagement

Collision plane


Three dimensional collision avoidance algorithms


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

Modified random flights (three dimensional)

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

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


Collision avoidance with realistic UAV models

Realistic UAV Model

UAV of span 1.4224 m, weighing 1.56 kg

  • Stability and control derivatives from Aviones

  • A UAV flight simulator developed by the Brigham Young University

  • (an open source software)

  • Available:

The Zagi Aircraft

Span = 1.5 m

Mean Chord = 0.33 m

Weight = 1.5 kg

Picture courtesy:

UAV control system

Controllers designed through successive loop closure

Separate controllers for holding altitude, attitude, and speed

PI controllers with parameters tuned manually

Controller design

Altitude hold controller

Similar controllers for attitude and speed holds are designed

Implementing the guidance commands

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

Test of collision avoidance

A example of collision avoidance of 5 UAVs

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

Test case of random flights for dense traffic

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.

Results of the random flight test case

Implementation of 3D collision avoidance algorithm

Realization of pitch and turn rate commands

Pitch rate guidance and control loops

Results of the random flight test case

for homogeneous UAVs

for heterogeneous UAVs

Collision avoidance in presence of non-cooperating UAVs

Summary of Chapter 3

Implemented collision avoidance algorithms on 6 DoF UAV models

Simulations with heterogeneous and non-cooperating UAVs


Coalition formation with global communication

Coalition formation for search and prosecute mission

Search targets and destroy them

The targets may have different requirements


  • Destroy the target is minimum time

  • Coalition should have minimum number of UAVs

  • Rendezvous on target to inflict maximum damage


Limited sensor radius

Target locations are not know a priori

Limited resources that deplete with use

Stationary targets

Global communication

Theorem: The minimum time minimum member coalition formation

for a single target is NP-hard

UAV that detects the target – Coalition leader

Coalition leader initiates the coalition formation process

Deadlocks are handled by rules/protocols

Communication protocol for coalition formation process

Two stage algorithm for coalition formation

Stage I

Find a minimum time coalition

Stage II

Find a minimum member coalition

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

Theorem: Stage I gives a minimum time coalition

Theorem: Stage I has polynomial time complexity

Stage II

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

Coalition formation examples

Solution using Particle Swarm Optimization (PSO)

Global solution of the search and prosecute problem using PSO

Target locations known a priori

Comparison of solutions

Summary of Chapter 4

Coalition formation algorithm for search and prosecute mission

Two stage polynomial time algorithm

Efficacy of the algorithm demonstrated via simulations


Coalition formation with limited communication

UAVs have limited communication ranges

Dynamic network over which coalition formations should take place

Network properties

Every UAV acts as a relay node

Each hop of message has an associated lag

Log of messages kept to avoid duplication

Time-to-live for a message

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

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

Example of stationary and constant velocity target

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

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


Performance of coalition formation algorithm with increase in number of UAVs

Performance of coalition formation algorithm with increase in communication range

Performance of coalition formation algorithm with increase in communication delay

Summary of Chapter 5

Coalition formation of UAVs with limited communication ranges

Prosecution of stationary, constant velocity, and maneuvering targets


Collision avoidance and coalition formation in multiple UAV missions

Rendezvous – meeting at a pre-planned time and place

Rendezvous of multiple UAVs

For simultaneous deployment of resources

To exchange resources or critical information

Multiple UAV Rendezvous

Rendezvous under collision avoidance

Rendezvous of multiple UAVs when some of the UAVs have to do

collision avoidance maneuvers en route

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

Multiple UAV Rendezvous Algorithm

Consensus in ETA achieved using

Velocity control within bounds

Wandering maneuver

Change in velocity proportional to

(average ETA – ETA)

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

Solution Approach

Linear average consensus

  • In principle, any consensus protocol can be used.

  • Average consensus protocol is used for the purpose of illustration

Rendezvous: Simulation Results

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

Target tracking

Coalition formation with collision avoidance

Summary of Chapter 6

Multiple UAV rendezvous with collision avoidance

Coalition formation with collision avoidance



Algorithms for collision avoidance and coalition formation and their applications

Algorithms are

conceptually ‘simple’


Possible extensions of present work

Better controller implementations possible for collision avoidance

Better communication protocols possible for coalition formation

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