Persistent UAV Service: An Improved Scheduling
Formulation and Prototypes of System Components
Byung Duk Song, Jonghoe Kim, Jeongwoon Kim, Hyorin Park,
James R. Morrison* and David Hyunchul Shim
Department of Industrial and Systems Engineering
Department of Aerospace Engineering
KAIST, South Korea
Friday, May 31, 2013
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013
Presentation Overview
• Motivation
• UAV service system concept
• Comparison with existing research
• UAV service system: Components and prototype
– Central planning: Deterministic customer paths
– UAV guidance system
– Automatic replenishment station
• System demonstration
• Concluding remarks
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 2
Presentation Overview
• Motivation
• UAV service system concept
• Comparison with existing research
• UAV service system: Components and prototype
– Central planning: Deterministic customer paths
– UAV guidance system
– Automatic replenishment station
• System demonstration
• Concluding remarks
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 3
Motivation
•
Large expensive UAVs
– Usually military purpose
– Operate for many hours
– Travel long distances
•
Small inexpensive UAVs
– A lot of application area such as tracking, communication relay,
environmental / fire / national boundary monitoring, cartography, disaster
relief and so on.
– Limited duration of mission
– Limited distance
•
Methods to ensure persistent operation can increase effectiveness of small UAVs
– Collection of UAVs, refueling stations, automatic guidance
– Algorithms to orchestrate the system operations
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 4
Presentation Overview
• Motivation
• UAV service system concept
• Comparison with existing research
• UAV service system: Components and prototype
– Central planning: Deterministic customer paths
– UAV guidance system
– Automatic replenishment station
• System demonstration
• Concluding remarks
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 5
UAV Service System Concept
Random arrival
of customer
information
Heterogeneous
UAVs
Random path
and duration
UAV service
system
Persistent UAV service
Vision
technology
Service station 2
UAV 1
UAV operation
system
Moving
objective
trajectory
Object 2
UAV 3
Service station 3
Object 3
Object 1
UAV 4
UAV 2
Central
planning
Automatic
replenishment
station
Service station 1
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 6
UAV 5
UAV Service System Concept
Random path
and duration
Heterogeneous
UAVs
UAV service
system
Persistent UAV service
Vision
technology
Service station 2
UAV 1
UAV operation
system
Moving
objective
trajectory
Object 2
UAV 3
Service station 3
Object 3
Object 1
UAV 4
UAV 2
Central
planning
Automatic
replenishment
station
Service station 1
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 7
UAV 5
UAV Service System Concept
Heterogeneous
UAVs
UAV service
system
Persistent UAV service
Vision
technology
Service station 2
UAV 1
UAV operation
system
Moving
objective
trajectory
Object 2
UAV 3
Service station 3
Object 3
Object 1
UAV 4
UAV 2
Central
planning
Automatic
replenishment
station
Service station 1
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 8
UAV 5
Presentation Overview
• Motivation
• UAV service system concept
• Comparison with existing research
• UAV service system: Components and prototype
– Central planning: Deterministic customer paths
– UAV guidance system
– Automatic replenishment station
• System demonstration
• Concluding remarks
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 9
Comparison with Existing Research
<Decentralized task assignment algorithm>
<Automatic landing & recharge>
1. Persistent path following with multiple shared
service stations distributed across the field of
operations
2. Prototype components for a system seeking to
provide a persistent UAV security escort service
<Automated 1.5 Hour persistent surveillance mission
with three autonomous vehicles>
[1] M. Alighanbari and J. P. How, “Decentralized task assignment for unmanned aerial vehicle”, Proceedings of the 44 th IEEE Conference on Decision and Control, and the European Control Conference,
December 2005
[2] M. Valenti, D. Dale, J. P. How and D. P. de Farias, “Mission health management for 24/7 persistent surveillance operations”, AIAA Guidance, Navigation10
and Control Conference and Exhibit, August
2007
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 –
Presentation Overview
• Motivation
• UAV service system concept
• Comparison with existing research
• UAV service system: Components and prototype
– Central planning: Deterministic customer paths
– UAV guidance system
– Automatic replenishment station
• System demonstration
• Concluding remarks
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 11
UAV Service System: Components and Prototype
Customer
information
UAV
schedule
Central planning
by MILP
Web or
smart phone
UAV
guidance
system
Automatic
control
feedback
Tracking
Customer
UAV
Automatic
replenishment
Replenishment
station
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 12
Central Planning: Deterministic Customer Paths
Customer
information
UAV
schedule
Central planning
by MILP
Web or
smart phone
UAV
guidance
system
Automatic
control
feedback
Tracking
Customer
UAV
Automatic
replenishment
Replenishment
station
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 13
Persistent UAV Service
■ Persistent UAV service system with heterogeneous UAVs and multiple service stations
- A system of UAVs that is supported by automated replacement systems can support long term or even
indefinite duration missions in a near autonomous mode with multiple service stations
- The UAVs can return to any service station, replenish their resources and resume their duties
Service station 2
UAV 1
Moving
objective
trajectory
Object 2
UAV 3
Service station 3
Object 3
Object 1
UAV 4
UAV 2
Service station 1
UAV 5
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 14
Customer Paths
■ To follow a time-space trajectory, the trajectory is divided into pieces (split jobs)
Service station 2
UAV 2
Start
5
1
UAV 2
8
9
6
4
2
7
10
UAV 1
End
UAV 1
3
Split
job
Start
point
End
point
Start
time
End
time
1
50,250
150,250
13:10
13:11
2
150,250
250,250
13:11
13:12
3
250,250
350,250
13:12
13:13
4
350,250
450,250
13:13
13:14
5
450,250
550,250
13:14
13:15
6
550,250
650,250
13:15
13:16
- From point (50,250) to (950,350)
7
650,250
750,250
13:16
13:17
- From 13:10 to 13:20
8
750,250
850,250
13:17
13:18
9
850,250
950,250
13:18
13:19
10
950,250
950,350
13:19
13:20
Service station 1
dij  ( xei  xsj )2  ( yei  ysj )2  d ji  ( xej  xsi )2  ( yej  ysi )2
▪ Objective moves
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 15
UAV 1
UAV 2
Assumptions
■ Assumptions
1. Moving target’s path and location at specific times are known.
2. UAVs start its travel from a recharge station
3. Recharge time for a UAV is constant
4. Initially all UAV batteries or fuel tanks are empty
5. UAV travel speed is constant
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 16
Initial Mathematical Formulation
■ Notation
i, j
:
Indices for jobs
s
:
Index for stations
k
:
Index for UAVs
r
:
Index of a UAV’s rth flight
NJ
:
Number of split jobs
NUAV
:
Number of UAVs in the system
NSTA
:
Number of recharge stations
NR
:
Maximum number of flight per UAV during the time horizon
M
:
Large positive number
(xjs, yjs)
:
Start point of split job j
(xje, yje)
:
End point of split job j
Dij
:
Distance from split job ith finish point to split job jth start point, Dij ≠ Dji
Ei
:
Start time of split job i
Pi
:
Processing time or split job i
qk
:
Maximum traveling time of UAV k
Sok
:
Initial location(station) of UAV k
TSk
:
Travel speed of UAV k
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 17
Initial Mathematical Formulation
■ Notation
ΩJ
:
= {1, …, NJ}, Set of split jobs
ΩJD
:
= {1, …, NJ+1}, Set of split jobs and dummy jobs
ΩSS
:
= {NJ+2, NJ+4, …, NJ+2∙ NSTA}, set of UAV flight start station
ΩSE
:
= {NJ+3, NJ+5, …, NJ +2∙ NSTA+1}, set of UAV flight end station
ΩA
:
= (ΩJD U ΩSS U ΩSE) = {1,…, NJ+2∙NSTA+1}, set of all jobs and recharge stations
■ Decision Variables
▪ Xijkr = 1 if UAV k processes split job j or recharges at station j after processing split job i or
recharging at station i during the rth flight; 0, otherwise
▪ Yikr = 1 if UAV k processes split job i during its rth flight; 0, otherwise.
▪ Cikr is job i’s start time by UAV k during its rth flight or UAV k’s recharge start time at station i;
otherwise its value is 0.
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 18
Initial Mathematical Formulation
■ Mathematical formulation
Minimize
   D
kK rR i A j A
ij
 X ijkr
Subject to
Initial recharge
station constraints

j JD
X sok , jk 1  1 (k  K )
(i  JD  SS , j  JD  SE , k  K , r  R)
 
X sjkr  1 (k  K , r  R)
sSS jJD
Recharge
station
constraints
Cikr  Pi  Dij / TSk  C jkr  M (1  X ijkr )
 X
sSE i JD
X
i JD
iskr
iskr
 1 (k  K , r  R)
X

i JD
s 1,ikr 1
(k  K , r  1... N R  1, s SE )
  X
kK rR i A
X
j A
ijkr

ijkr
 1 ( j J )
X
j A
X ijkr  Yikr (i  J , k  K , r  R)
jikr
 0 (i  JD , k  K , r  R)
 C
kK rR
ikr
i A j A
ij
 Ei (i  J )
/ TSk  X ijkr 
  PX
i JD j A
i
ijkr
 qk (k  K , r  R)
X sdkr  X d ,s 1,kr (k  K , r  R, s   SS )
X dikr  X idkr  0 (k  K , r  R, i  J )
X
iskr
 0 (k  K , r  R, s SS )
Fuel
constraints
Dummy job
constraints
Cikr  0 (k  K , r  R, i  A )
X ijkr  {0,1} (k  K , r  R, i   A , j   A )
iJD
Start time
constraints
M  Yikr  Cikr (i J , k  K , r  R)
 D
Cskr  Cs 1,kr 1 (k  K , r  1... N R  1, s   SE )
Split job
assignment
constraints

j JD SE
Yikr {0,1} (k  K , r  R, i  A )
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 19
Decision
variables
Reduce Variables and Constraints
■ Mathematical formulation
Minimize
   D
kK rR i A j A
ij
 X ijkr
Subject to
Initial recharge
station constraints

j JD
 
 X
sSE i JD
X
i JD
(i  JD  SS , j  JD  SE , k  K , r  R)
X sjkr  1 (k  K , r  R)
sSS jJD
Recharge
station
constraints
Cikr  Pi  Dij / TSk  C jkr  M (1  X ijkr )
X sok , jk 1  1 (k  K )
iskr
iskr
 1 (k  K , r  R)
X

i JD
s 1,ikr 1
(k  K , r  1... N R  1, s SE )
kK rR i A
Split job
assignment
constraints
X
j A
ijkr

ijkr
 1 ( j J )
X
j A
X ijkr  Yikr (i  J , k  K , r  R)
jikr
 0 (i  JD , k  K , r  R)
 C
kK rR
ikr
i A j A
ij
 Ei (i  J )
/ TSk  X ijkr 
  PX
i JD j A
i
ijkr
 qk (k  K , r  R)
X sdkr  X d ,s 1,kr (k  K , r  R, s   SS )
X dikr  X idkr  0 (k  K , r  R, i  J )
X
iskr
 0 (k  K , r  R, s SS )
Fuel
constraints
Dummy job
constraints
Cikr  0 (k  K , r  R, i  A )
X ijkr  {0,1} (k  K , r  R, i   A , j   A )
iJD
Start time
constraints
M  Yikr  Cikr (i J , k  K , r  R)
 D
Cskr  Cs 1,kr 1 (k  K , r  1... N R  1, s   SE )
  X

j JD SE
Yikr {0,1} (k  K , r  R, i  A )
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 20
Decision
variables
Improved Formulation
■ Mathematical formulation
Minimize
   D
kK rR i A j A
ij
 X ijkr
Subject to
Initial recharge
station constraints

j J SE
Cikr  Pi  Dij / TSk  C jkr  M (1  X ijkr )
X sok , jk1  1 (k  K )
(i  J  SS , j  J  SE , k  K , r  R)
 
sSS jJ SE
X sjkr  1 (k  K , r  R)
M

j J SE
Recharge
station
constraints
 
X iskr  1 (k  K , r  R)
sSE iJ SS
 C
kK rR

iJ SS
X iskr 

iJ SS
X s1,ikr 1
(k  K , r  1... N R  1, s SE )
Cskr  Cs 1,kr 1 (k  K , r  1... N R  1, s   SE )
  X
kK rR i A
Split job
assignment
constraints

j A

X ijkr 
i J SS
ijkr

j A
ij
/ TSk  X ijkr 
  PX
iJ j A
i
ijkr
 qk (k  K , r  R)
Cikr  0 (k  K , r  R, i  A )
 1 ( j J )
X jikr  0 (i  J , k  K , r  R)
Start time
constraints
 Ei (i  J )
ikr
 D
i A j A
X ijkr  Cikr (i  J  SS , k  K , r  R)
X ijkr  {0,1} (k  K , r  R, i   A , j   A )
X iskr  0 (k  K , r  R, s SS )
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 21
Fuel
constraints
Decision
variables
Improved Formulation
■ Complexity : Number of decision variables and constraints
Kim et al. (2012)
Improved formulation
Difference
Total # of binary
decision variable
NUAV∙NR∙{(NJ+2∙NSTA+1)2
+(NJ+2∙NSTA+1)}
NUAV∙NR∙(NJ+2∙NSTA)2
(NJ+2∙NSTA+1)2
+(NJ+2∙NSTA+1)
-(NJ+2∙NSTA)2
Total # of continu
ous
decision variable
NUAV∙NR∙ (NJ+2∙NSTA+1)
NUAV∙NR∙(NJ+2∙NSTA)
NUAV∙NR
Total # of
decision variable
NUAV∙NR∙{(NJ+2∙NSTA+1)2
+2∙(NJ+2∙NSTA+1)}
NUAV∙NR∙{(NJ+2∙NSTA)2
+NJ+2∙NSTA}
(NJ+2∙NSTA+1)2
+NJ+2∙NSTA+2
-(NJ+2∙NSTA)2
Total # of
constraints
NUAV{2(NR-1)∙NSTA+1}
+ NJ(3NUAV∙NR+2)
2
+ NUAV∙NR{( NJ+NSTA+1)
+2NJ+4NSTA+5}
NUAV{2(NR-1)∙NSTA+ NR∙ NSTA +1}
+ NJ(NUAV∙NR+2)
2
+ NUAV∙NR{( NJ+NSTA)
+2NJ+3NSTA+3}
NUAV∙NR{( NJ+NSTA+1)2
-( NJ+NSTA)2+NSTA+2}
+2∙NJ∙ (NUAV∙NR)
-NUAV∙ (NR∙ NSTA )
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 22
Computational Results
■ Comparison of computational result
NJ
NSTA
NUAV
# of
D.V
# of
const
CPU
Time
Obj.
Value
# of
D.V
# of
const
CPU
Time
Obj.
Value
CPU
Time
Redu
ction
8
2
2
780
722
3.00
2048
624
566
1.84
2048
1.6x
14
3
6
5796
5002
15.84
1846
5040
4222
4.36
1846
3.6x
15
3
6
6336
5508
220.3
724
5544
4680
35.78
724
6.2x
20
4
8
14384
12048
N/A
N/A
12992
10592
348.97
2894
-
Kim et al. (2012)
Improved formulation
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 23
UAV Guidance System
Customer
information
UAV
schedule
Central planning
by MILP
Web or
smart phone
UAV
guidance
system
Automatic
control
feedback
Tracking
Customer
UAV
Automatic
replenishment
Replenishment
station
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 24
UAV Guidance System
■ Roles of UAV guidance system
1. Receive and implement the schedule from the MILP.
2. Convert the video from the UAV cameras into usable information for directing the motion of
the UAVs
3. Enable a human overseer to monitor the UAV progress via video and adjust feedback control
gain values for various situations
4. Allows for a human overseer to initiate emergency actions such as immediate landing.
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 25
UAV Guidance System
■ System components
< AR drone 2.0 >
1280  720 pixel front camera
320  240 pixel belly camera
< WIFI >
< Ipad 3>
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 26
UAV Guidance System
1. The color video from the camera is acquired via
TCP port and processed using OpenCV framework.
Descriptions
① : Front(Bottom) Camera Video
② : Start Procedure Interface
③ : Color Filtered Video
④ : Control Gain Adjustment Sliders
⑤ : Emergency Landing Button
2. The image is separated into three RGB channels.
These three images are used to determine the color
of the targeted image.
3. Control inputs including the longitudinal-lateral tilt
angles, height and yaw angular velocity are calculated
from the number and mean coordinate of target pixels
in the processed image.
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 27
UAV Guidance System
■ P-D gain controller block diagram
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 28
Automatic Replenishment Station
Customer
information
UAV
schedule
Central planning
by MILP
Web or
smart phone
UAV
guidance
system
Automatic
control
feedback
Tracking
Customer
UAV
Automatic
replenishment
Replenishment
station
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 29
Automatic Replenishment Station
▪ Each AR Drone 2.0 uses a three cell lithium
polymer battery
▪ four copper leads (three for each terminal and
one for the ground terminal) were threaded from
the battery inside the UAV to the four feet of the
drone
▪ The service station consists of four pads, one for
each foot of the drone.
▪ Each such pad connects to the UAV battery via
the leads on the drone feet
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 30
Presentation Overview
• Motivation
• UAV service system concept
• Comparison with existing research
• UAV service system: Components and prototype
– Central planning: Deterministic customer paths
– UAV guidance system
– Automatic replenishment station
• System demonstration
• Concluding remarks
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 31
System Demonstration: Layout
■ Demonstration description
< Demonstration layout >
Split
job 8
Station 3
< Schedule by MILP >
Split
job 7
UAV 2
∙∙∙
Split
job 2
Split
job 1
UAV
Start
station
Assigned
job
End
station
Service
start
time
Service
end
time
1
1
1,2,3,4
2
2
10
2
2
5,6,7,8
3
10
18
Hand-off
5m
UAV 1
Station 2
Station 1
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 32
System Demonstration: Video
■ Demonstration video
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 33
Presentation Overview
• Motivation
• UAV service system concept
• Comparison with existing research
• UAV service system: Components and prototype
– Central planning: Deterministic customer paths
– UAV guidance system
– Automatic replenishment station
• System demonstration
• Concluding remarks
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 34
Concluding Remarks
• Towards a persistent UAV service
• Components of such a system
– System orchestration MILP (deterministic customer paths)
• Improved formulation
• Reduced computational time
– UAV guidance system
• Vision for UAV localization relative to customer, location flags and platforms
• Feedback control for UAV via iPad controller
– Automatic replenishment stations (battery recharge)
• Demonstration of proposed UAV service system
• Future directions
–
–
–
–
Real time customer requests
Random customer behavior during service
Implementation outdoors
Improved UAV localization algorithms
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 35
Back up materials
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013
Improvement : Efficient formulation
• To enhance the cplex computational power, efficient mathematical
formulation was developed
• Delete unnecessary decision variable and dummy job concepts
- Delete Yjkr decision variable because Cjkr decision variable can replace it.
▪ Yikr = 1 if UAV k processes split job i during its rth flight; 0, otherwise.
▪ Cikr is job i’s start time by UAV k during its rth flight or UAV k’s recharge start time at station i;
otherwise its value is 0.
Cjkr
Yjkr
=0
=0
>0
=1
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 37
Improvement : Efficient formulation
• Delete the concept of dummy job which is used for idle UAVs by allowing
direct flight from start(end)station to end(start) station
 
sSS jJD
X sjkr  1 (k  K , r  R)
 X
sSE i JD
iskr
 1 (k  K , r  R)
Dummy job
d ij  0, i   ss , j  dummy job
stations
 
sSS jJ SE
X sjkr  1 (k  K , r  R)
 
sSE iJ SS
X iskr  1 (k  K , r  R)
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 38
Literature Review
•
Scheduling methods without a distance or time restriction
–
–
•
Scheduling methods for limited flight duration
–
–
–
–
•
A. L. Weinstein and C. Schumacher, “UAV scheduling via the vehicle routing problem with time windows,” In Proc. AIAA
Infotech@Aerospace 2007 Conference and Exhibit, Rohnert Park, California, 2007
T. Shima, S. Rasmussen and D. Gross, “Assigning micro UAVs to task tours in an urban terrain,” IEEE Transactions on Control Systems
Technology, Vol. 15, No. 4, 2007, pp. 601 – 612
Y.S. Kim, D.W. Gu and I. Postlethwaite, “Real-time optimal mission scheduling and flight path selection, IEEE Transactions on
Automatic Control, Vol. 52, No. 6, 2007, pp. 1119-1123.
B. Alidaee, H. Wang, and F. Landram, “A note on integer programming formulations of the real-time optimal scheduling and flight
selection of UAVS,” IEEE Transactions of Control Systems Technology, Vol. 17, No. 4, 2009, pp.839-843
Scheduling method for persistent UAV operation
–
•
T. Shima and C. Schumacher, “Assignment of cooperating UAVs to simultaneous tasks using genetic algorithm,” In Proc. AIAA
Guidance, Navigation, and Control Conference and Exhibit, San Francisco, 2005
J. Zeng, X. Yang L. Yang and G. Shen, “Modeling for UAV resource scheduling under mission synchronization,” Journal of Systems
Engineering and Electronics, Vol. 21, No. 5, 2010, pp. 821-826
M. Alighanbari and J. P. How, “Decentralized task assignment for unmanned aerial vehicle”, Proceedings of the 44th IEEE Conference
on Decision and Control, and the European Control Conference 2005 seville, spain, december 12-15, 2005
Battery recharge/exchange methods
–
–
–
–
–
J. How, thesis papers at MIT, 2005, 2007
A.S. Kurt, B.H. Clarence, R.R. Johnhenri, D.W. Richardson, Z.H. White, Q. Elizabeth and G. Anouck, “Autonomous Battery Swapping
System for Small-scale Helicopters”, 2010 IEEE International Conference on Robotics and Automation
R. Godzdanker, M. J. Rutherford and K. P. Valavanis, “ISLANDS: A self-leveling platform for autonomous miniature UAVs”, 2011
IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp 170-175
A.O.S. Koji, K.F. Paulo and James R. Morrison, “Automatic battery replacement system for UAVs: Analysis and design” Journal of
Intelligent and Robotic Systems, Special Issue on Unmanned Aerial Vehicles (Springer), a Special Volume on Selected Papers from
ICUAS’11, Vol. 65, No. 1, pp. 563-586, January 2012. First published online September 9, 2011
M. Valenti, D. Dale, J. P. How and D. P. de Farias, “Mission health management for 24/7 persistent surveillance operations”, AIAA
Guidance, Navigation and Control Conference and Exhibit, 20-23 August 2007, Hilton Head, South Carolina
©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013 – 39