persistent uav service: an improved scheduling formulation and prototypes of system components

©2013 – James R. Morrison – ICUAS’13 – Atlanta, Georgia, USA – May 28-31, 2013

Persistent UAV Service: An Improved Scheduling Formu-lation 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 – 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



• Motivation









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



• Motivation









UAV Service System Concept

Service station 1

Service station 2

Object 1

Object 2

Object 3Service station 3

UAV 1

UAV 2

UAV 4

UAV 3

UAV 5

Moving objective trajectory

UAV service system

Persistent UAV service

Random arrival of customer in-

formationRandom path and duration

Vision technol-ogy

Heterogeneous UAVs

UAV operation system

Automatic re-plenishment

stationCentral plan-

ning



Service station 1

Service station 2

Object 1

Object 2


UAV 1

UAV 2

UAV 4

UAV 3

UAV 5


UAV service system

Persistent UAV service

Random path and duration

Vision technol-ogy

Heterogeneous UAVs




ning



Service station 1

Service station 2

Object 1

Object 2


UAV 1

UAV 2

UAV 4

UAV 3

UAV 5


UAV service system

Persistent UAV serviceVision technol-

ogy

Heterogeneous UAVs




ning



• Motivation









Comparison with Existing Research

<Automated 1.5 Hour persistent surveillance mission with three autonomous vehicles>

<Automatic landing & recharge><Decentralized task assignment algorithm>

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

[1] 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, 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, Navigation and Control Conference and Exhibit, August 2007



• Motivation









UAV Service System: Components and Prototype

UAV

UAV guid-ance system

Central planning by MILP

UAV schedule

Automaticcontrol

Tracking

Customerinformation

Replenishmentstation

Customer

feedback

Automaticreplenishment

Web or smart phone


Central Planning: Deterministic Customer Paths

UAV



UAV schedule

Automaticcontrol

Tracking

Customerinformation


Customer

feedback


Web or smart phone


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 in-definite 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 1

Service station 2

Object 1

Object 2


UAV 1

UAV 2

UAV 4

UAV 3

UAV 5



Customer Paths

■ To follow a time-space trajectory, the trajectory is divided into pieces (split jobs)

Start

End

Service station 1

Service station 2

1

2

3

104

5

6 7

89

Split Job 1

UAV 1UAV 1

UAV 2

UAV 2

▪ Objective moves- From point (50,250) to (950,350)

- From 13:10 to 13:20

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

7 650,250 750,250 13:16 13:17

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

UAV 1

UAV 2

2 2 2 2( ) ( ) ( ) ( )i j i j j i j iij e s e s ji e s e sd x x y y d x x y y


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


Initial Mathematical Formulation

■ Notationi, 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, yj

s) : Start point of split job j

(xje, yj

e) : 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



■ 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

▪ 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.

▪ Yikr = 1 if UAV k processes split job i during its rth flight; 0, otherwise.



■ Mathematical formulation

1, 1 ( , 1... 1, )JD JD

iskr s ikr R SEi i

X X k K r N s

1 ( , )SE JD

iskrs i

X k K r R

1 ( , )SS JD

sjkrs j

X k K r R

, 1 1 ( )ok

JD

s jkj

X k K

Subject to

1, 1 ( , 1... 1, )skr s kr R SEC C k K r N s

1 ( )A

ijkr Jk K r R i

X j

0 ( , , )A A

ijkr jikr JDj j

X X i k K r R

0 ( , , )JD

iskr SSi

X k K r R s

A A

ij ijkrk K r R i j

D X

Minimize

Recharge station

constraints

Initial recharge station constraints

Split jobassignment constraints

/ (1 )

( , , , )ikr i ij k jkr ijkr

JD SS JD SE

C P D TS C M X

i j k K r R

( , , )JD SE

ijkr ikr Jj

X Y i k K r R

( , , )ikr ikr JM Y C i k K r R

( )ikr i Jk K r R

C E i

/ ( , )A A JD A

ij k ijkr i ijkr ki j i j

D TS X P X q k K r R

, 1, ( , , )sdkr d s kr SSX X k K r R s

0 ( , , )dikr idkr JX X k K r R i

0 ( , , )ikr AC k K r R i

{0,1} ( , , , )ijkr A AX k K r R i j

{0,1} ( , , )ikr AY k K r R i

Start time constraints

Fuel constraints

Dummy job constraints

Decision variables


Reduce Variables and Constraints


1, 1 ( , 1... 1, )JD JD

iskr s ikr R SEi i

X X k K r N s

1 ( , )SE JD

iskrs i

X k K r R

1 ( , )SS JD

sjkrs j

X k K r R

, 1 1 ( )ok

JD

s jkj

X k K

Subject to


1 ( )A

ijkr Jk K r R i

X j

0 ( , , )A A

ijkr jikr JDj j

X X i k K r R

0 ( , , )JD

iskr SSi

X k K r R s

A A

ij ijkrk K r R i j

D X

Minimize

Recharge station

constraints



/ (1 )


JD SS JD SE

C P D TS C M X

i j k K r R

( , , )JD SE

ijkr ikr Jj

X Y i k K r R

( , , )ikr ikr JM Y C i k K r R

( )ikr i Jk K r R

C E i

/ ( , )A A JD A



, 1, ( , , )sdkr d s kr SSX X k K r R s

0 ( , , )dikr idkr JX X k K r R i


{0,1} ( , , , )ijkr A AX k K r R i j

{0,1} ( , , )ikr AY k K r R i


Fuel constraints

Dummy job constraints

Decision variables


Improved Formulation


1, 1 ( , 1... 1, )J SS J SS

iskr s ikr R SEi i

X X k K r N s

1 ( , )SE J SS

iskrs i

X k K r R

1 ( , )SS J SE

sjkrs j

X k K r R

, 1 1 ( )ok

J SE

s jkj

X k K

Subject to


1 ( )A

ijkr Jk K r R i

X j

0 ( , , )A A

ijkr jikr Jj j

X X i k K r R

0 ( , , )J SS

iskr SSi

X k K r R s

A A

ij ijkrk K r R i j

D X

Minimize

/ (1 )


J SS J SE

C P D TS C M X

i j k K r R

( , , )J SE

ijkr ikr J SSj

M X C i k K r R

( )ikr i Jk K r R

C E i

/ ( , )A A J A




{0,1} ( , , , )ijkr A AX k K r R i j

Recharge station

constraints




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 contin-uous

decision variableNUAV∙NR∙ (NJ+2∙NSTA+1) NUAV∙NR∙(NJ+2∙NSTA) NUAV∙NR

Total # ofdecision 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)

+ NUAV∙NR{( NJ+NSTA+1)2

+2NJ+4NSTA+5}

NUAV{2(NR-1)∙NSTA+ NR∙ NSTA +1}+ NJ(NUAV∙NR+2)

+ NUAV∙NR{( NJ+NSTA)2

+2NJ+3NSTA+3}

NUAV∙NR{( NJ+NSTA+1)2

-( NJ+NSTA)2+NSTA+2}+2∙NJ∙ (NUAV∙NR)-NUAV∙ (NR∙ NSTA )


Computational Results

Kim et al. (2012) Improved formulationCPU Time Re-duc-tion

NJ NSTA NUAV# ofD.V

# ofconst

CPUTime

Obj.Value

# ofD.V

# ofconst

CPUTime

Obj.Value

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 -

■ Comparison of computational result


UAV Guidance System

UAV



UAV schedule

Automaticcontrol

Tracking

Customerinformation


Customer

feedback


Web or smart phone


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.


UAV Guidance System

1280 720 pixel front camera

320 240 pixel belly camera

< AR drone 2.0 >

< Ipad 3>

■ System components

< WIFI >


UAV Guidance System

Descriptions

① : Front(Bottom) Camera Video

② : Start Procedure Interface

③ : Color Filtered Video

④ : Control Gain Adjustment Sliders

⑤ : Emergency Landing Button

1. The color video from the camera is acquired via TCP port and processed using OpenCV framework.

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.


UAV Guidance System

■ P-D gain controller block diagram


Automatic Replenishment Station

UAV



UAV schedule

Automaticcontrol

Tracking

Customerinformation


Customer

feedback


Web or smart phone


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



• Motivation









System Demonstration: Layout

UAV Startstation

Assignedjob

Endstation

Service starttime

Service end time

1 1 1,2,3,4 2 2 10

2 2 5,6,7,8 3 10 18

■ Demonstration description

Station 1Station 2

Station 3

UAV 1

UAV 2

Hand-off

Split job 1

Split job 2

Split job 8

Split job 7

∙ ∙ ∙

< Schedule by MILP >

< Demonstration layout >

5m


System Demonstration: Video

■ Demonstration video



• Motivation









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


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.

▪ 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.

▪ Yikr = 1 if UAV k processes split job i during its rth flight; 0, otherwise.

Cjkr

=0>0

Yjkr

=0=1


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

1 ( , )SS JD

sjkrs j

X k K r R

stations

Dummy job

0, , ij ssd i j dummy job

1 ( , )SE JD

iskrs i

X k K r R

1 ( , )SE J SS

iskrs i

X k K r R

1 ( , )SS J SE

sjkrs j

X k K r R


Literature Review• Scheduling methods without a distance or time restriction

– 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

• 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– 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 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

persistent uav service: an improved scheduling formulation and prototypes of system components

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