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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.
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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