Multi-Satellite Scheduling Using Genetic Algorithms - Semantic Scholar

Multi-Satellite scheduling using Genetic Algorithms P.Soma, S.Venkateswarlu, S.Santhalakshmi, Tapan Bagchi*,Sanjay Kumar* ISTRAC/ISRO, Peenya Industrial Estate, Bangalore-560058 *Formerly I.I.T., Kanpur, India – 208 016 e-mail: [email protected] Abstract ISRO Telemetry Tracking and Command Network (ISTRAC) operate a fleet of Indian remote Sensing Satellites (IRS) in the low earth polar sun-synchronous orbits. Satellite Operations Downloaded by 117.211.61.21 on December 5, 2013 | http://arc.aiaa.org | DOI: 10.2514/6.2004-743-515

Scheduling of ground stations is one of the important tasks performed at Spacecraft Control Centre (SCC). In the prevailing multiple-satellite operations scenario, scheduling becomes complex, because of satellite-specific constraints, ground station configuration, satellite priorities and priorities of payload and special operations. Further, radio visibility conflicts are to be taken in to consideration, while generating the weekly operations schedule. Resolving the visibility clash at a ground station and allocating Telemetry, Tracking and Command (TTC) resources optimally for multiple satellites meeting the requirements of each mission are the key aspects of scheduling. Optimal resolution of visibility clashes is performed using Genetic Algorithms. The software, “Intelligent Multi-Objective Priority Activated Task Optimizer (IMPACT)” uses Artificial Intelligence and constraint directed search to generate weekly optimal schedule of spacecraft support. IMPACT is designed to resolve spacecraft controller clashes as well, thus enabling a single controller to perform multiple spacecraft operations. This software also ensures that the operations load is distributed uniformly to all the ground stations in the TTC network. This paper presents the multi-satellite operations scenario of Indian Remote Sensing satellites at ISTRAC and the details of operations scheduling scheme adopted. The variants of visibility clash topologies, the optimization scheme based on genetic algorithm including the mathematical model for gain function, scheduling software architecture and its salient features are described. Important design features of this operational software system are highlighted with examples. 1 Introduction: Genetic algorithms use meta heuristics approach, that models natural evolution process. Genetic algorithms are theoretically and empirically proven to provide good search in complex spaces. Having been established as a valid approach to problems requiring efficient search, Genetic algorithms are now finding more applications in business, scientific and engineering circles.

Genetic algorithms are used successfully in scheduling spacecraft

operations in a multi-satellite environment on a weekly basis. This paper provides the problem description and the solution obtained using Genetic Algorithms.

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2 The problem of Spacecraft operations scheduling: ISRO Telemetry, Tracking and Command network (ISTRAC) has the responsibility of tracking, health monitoring and commanding low earth orbit satellites. There are 8 remote-sensing satellites in orbit as on now, with 6 of them actively servicing payload requirements from various customers. operations scheduling

Spacecraft

is one of the important tasks performed at ISTRAC. Scheduling the

network resources for Telemetry and Tele command operations involves visibility clash resolution over the ground stations and allocation of network operations for different spacecrafts based on the mission constraints and station capabilities. This work becomes more complex in a multi-

Downloaded by 117.211.61.21 on December 5, 2013 | http://arc.aiaa.org | DOI: 10.2514/6.2004-743-515

satellite environment. Table-1 gives details of satellites in the orbit as of now. Table: 1 - CURRENT SPACECRAFTS IN ORBIT SPACE-CRAFT

ORBIT

LAUNCH DATE

LOCAL TIME

IRS – 1C

808 / 825

28-12-1995

9:48

IRS – 1D

736/821

29-09-1997

10:23

IRS – P3

807 / 825

21-03-1996

8:53

IRS- P4

711/728

26-05-199

11:57

IRS – P6

808 / 825

17 – 10 –2004

10:34

TES

523 / 585

22 – 10 –2001

10:52

PAY LOADS PAN, WIFS LISS – 3 PAN, WIFS LISS – 3 XRAY, MOS CBT OCM, MSMR LISS – 4, AWIFS, LISS – 3,SSR PAN

Typical spacecraft operations vary depending upon the individual mission requirements. Telemetry (TM) and Tele command (TC) supports are essential supports from a ground station for any given spacecraft. Other than these, the mission as and when it is needed will inform other operations that are to be scheduled. Thus the spacecraft operations scheduling has to take care of the following tasks: 3 Visibility clash resolution: Over a ground station, if two or more spacecrafts pass simultaneously, or one after the other, such that the time difference between the loss of signal (LOS) of first spacecraft and the acquisition of signal (AOS) of the second spacecraft, is less than the time required for the station to get ready to support the second spacecraft, then the two spacecrafts are said to have a visibility clash. These visibility clashes over various stations are to be resolved effectively, and operations are to be allocated for different ground stations by taking in to account the requirements of various spacecrafts and the capabilities and constraints of the supporting elements. The operations are to be allocated over various ground stations for all the

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satellites in such a way that, every satellite gets adequate TTC support and all the ground stations services are utilized effectively. 4 Network of ground stations:

The network of ground stations that are supporting the

operations are as in Table-2: Table-2: NETWORK STATIONS


Station

Capability

Duration

Bangalore (2 chains)

All operations Reconfiguration time 3 minutes

Throughout mission life (All 24 hours)

Lucknow (2 chains)



Mauritius (1 Chain)


Throughout mission life (04:00 U T to 10:00 U T)

Bears Lake (2 chains)



Biak (1 chain)

TM, TC

Throughout mission life (22:00 UT to 05:00 UT & 11:45 UT to 14:15 UT)

The spacecraft operations scheduling is the problem of mapping tasks to resources. Here the tasks are the operations that are to be scheduled. Resources are the network stations and the spacecraft controllers. There are many constraints that are to be considered such as, ¾

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Mission requirements o

Type and Frequency of operations to be scheduled

o

Mutual dependency of operations

o

Minimum support required from the stations

o

Payload related requirements

o

Maximum support gap that can be tolerated for any spacecraft

o

Maneuver for orbit maintenance

o

Minimum number of operations to be scheduled in a day

o

Spacecraft priority

o

Support requirements for contingency phase/launch phase

Station capabilities o

Number of chains available

o

Minimum support elevation

o

Operations handling capability

3

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o

Station reconfiguration time needed

o

Station working hours

Controller position o

Controller for a single spacecraft or multiple spacecrafts


5 Typical visibility clash and support schedule can be expressed as in Figure-1:

F ig 1 .V is u a liz a tio n o f C la sh in g V is ib ilitie s o v e r a G r o u n d S ta tio n T o ta l v is ib ility o f a ll th re e s ate llite s V isib ility o f S a tellite 1

a1

b1

V is ib ility o f S a te llite 2 b2 V is ib ility o f S a te llite 3

a2

s 1 = S ta rt o f S u p p o r t o f S a te llite 1

M in im u m re c o n fig u ra tio n tim e = 6 0 0 s e c s

a3

s 2 = S ta r t o f S u p p o rt o f S a te llite 2

S u p p o rt= x 1

s 3 = S ta rt o f S u p p o rt o f S a te llite 3

S u p p o rt= x 2

e 1 = E n d o f S u p p o rt o f S a te llite 1

e 2 = E n d o f S u p p o rt o f S a te llite 2

b3

S u p p o rt= x 3 e 3 = E n d o f S u p p o rt o f S a te llite 3

In the above scenario satellite 1, satellite 2, and satellite 3 have visibility clash. By adjusting the timings at AOS & LOS all the three satellites are supported. 6 Genetic Algorithms (GAs): GAs work with the coding of the parameter set, not the parameters themselves. The solution to the problem is cast in to a chromosome like structure (binary coding). The initial set of solutions form the first generation, often called the initial population. Further generations are derived based on the survival of fittest, similar to natural evolution. 7 Genetic Operations: Typical genetic operations are crossover and mutation, which causes the parent strings to produce offspring. Crossover creates progeny by exchanging information among selected parent strings resident in the mating pool. Example Single-Point Crossover Parent 1

:

XX|XXXXX

Parent 2

:

YY|YYYYY

Offspring 1

:

XXYYYYY

Offspring 2

:

YYXXXXX

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Crossover is usually the primary operator with mutation serving only as a mechanism to introduce diversity in the population. Mutation facilitates local search. The produced offspring are checked for fitness and the fittest ones form the next mating pool. This process continues till optimum solution is arrived at (More often decided by experience and stopped after a specified number of generations).

8 Genetic algorithm applied to scheduling problem: Input

- Visibility information for all satellites and payload requests. - Configuration file for tuning satellite and station details and priority.


Output - Clash resolved, operations allocated schedules for one week. The pass duration for each set of clashing records serve as the input to be optimized. The decision variables are the duration of support of the clashing slots of various satellites. Binary coding is done for these decision variables to form the chromosomes. For a combination of support duration of the given set, the profit or return value is computed which varies for every combination of support duration. The initial population with their fitness forms the initial mating pool. GA operation is performed on this population and the next generation is checked for return value. The procedure continues for 500 generations.

9 Mathematical formulation P = f(x1,x2,x3,…..xn), the utility or value function to be maximized xi = Support duration given to the satellite when it passes over a station Maximize P = f(x1,x2,x3,….xn) = Σ(Ci*xi), where i varies from 1 to number of satellites and C is the profit contributed to P per unit time when satellite

‘i’

is supported subject to the following

constraints: xi = ei – si where ei – end of pass and si - the start of pass xi = 0 or xi >= minimum duration xi < maximum duration si,ei>=0; xi>=0 In general, the profit or value function P=f(x1,x2,x3,….xi) may be non linear and may involve multiple local optima. The control factor used for deciding the optimal support duration for each satellite is derived from various factors governing the satellite and station capabilities and priorities. Thus combining the soft constraints and driving the objective function with the value of the combined constraints gives a good control over the search process.

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10 Evaluation function for TTC support value generation For TTC operations the return per unit support time is not uniform or constant. Rather, it depends on the duration of support already provided. The following points are derived based on experience: TTC operations require a minimum support of 8 minutes, whenever support is

-

provided. Almost all operations can be done in this 8 minutes span. Thus the returns obtained

-

in supporting a satellite for its minimum support time are high. A support of more than 8 minutes is not necessary, but if some additional visibility

-


time is available, support may be extended beyond 8 minutes. The marginal utility of this extra support gradually diminishes with extra support provided. Thus, providing more than 8 minutes support is desirable, when possible, but not required. Thus, the objective function is crafted in such a way that there is no return if a satellite is supported for a duration less than 8 minutes. But the return per unit time is high if support is exactly 8 minutes. The marginal rate of return diminishes after 8 minutes. To implement this, the exponentially diminishing return function was adopted. A simple exponentially diminishing function has only one parameter λ and can be represented as f = λe-λt Where, t is the time measured from the origin. At t = 0, f = λ, thus λ is the ordinate of the exponential curve at t = 0. 11 The exponentially decaying return function 12 . . . . . 4 2 f 0

1

5

9 …. …. …. …. …. …. …. …. …. …. …. …. … … … …. …. …. 53 Time

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To mould this diminishing function to our requirement, we introduce one more parameter B. f = λe-λBt

Here B is a factor that will control the actual decay rate of this exponential curve as support time t increases beyond 8 minutes. When t = 8 minutes, f = 1% of λ At t = 0, f = λ At t = 480, f = 1% of λ Hence at 8 minutes, B = 0.00958/λ. Integrating this, we get total value of support which is (1-eλtB

)/B


Total return = 0 for t < 8 minutes Total return = A+(1-e-λtB)/B for t >= 8 minutes Where A = 480 (8minutes in seconds) * λ, B = 0.00958/λ and t = (Duration of total support – 480). λ is a function of all the controlling factors, λ = Max(Factorexclusive,Factorcyclic,Factortimer,Factorsupportgap) +Factor satellite+Factorelevation. Tuning the input files appropriately controls all these factors. 12 Scheduling system:

Special tilt requests

User requests

Payload request file.

Payload process Module

Payload steering information

Spacecraft visibilities Visibilities

Station/Satellite priorities Genetic Algorithm

Input configuration files Clash resolved optimized schedule

Processed Schedule for one week. Fig 2. Scheduling System

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Customer requests for payload data are processed for special tilt requirement, sensor selection etc and feasibility of scheduling those passes are studied. These processed requirements, and the visibilities of all the spacecrafts, over all the stations, serve as input for optimization. The configuration files serve as the controlling factor for operation allocation. Genetic algorithm is applied at this level to optimally allocate the operations for a duration of one week by resolving visibility clashes and controller clashes. All these modules are automated and regularly used for schedule generation on a weekly basis. A sample output of schedules generated is given below:


The clashing passes over Mauritius ground station are highlighted for easy reference. In this, IRS-1C, IRS-1D, IRS-T1 and IRS-P4 are clashing and the passes are allocated for IRS-1C and IRS-T1. DATE

S/C STN ORBIT Max

AOS

LOS

OPERATIONS

ele hh:mm:ss hh:mm:ss 2004 03 14 I1D SN1 33773 19.18 06:10:43 06:16:20 PL,P85(TILT=-2.07),G3 2004 03 14 I1D BLE 33773 17.99 06:11:56 06:24:40 TM,TC,TR 2004 03 14 I1C LK1 42612 7.29 06:13:19 06:22:26 TM,TC$ 2004 03 14 I1C SN2 42612 5.31 06:17:20 06:24:10 NO SUPPORT 2004 03 14 I1C BLW 42612 4.38 06:18:19 06:26:29 TM,TC 2004 03 14 I1D MAU 33773 70.78 06:21:14 06:35:51 NO SUPPORT 2004 03 14 IT1 DLH 13111 31.35 06:24:45 06:36:03 NO SUPPORT 2004 03 14 IP4 LK1 25434 52.26 06:27:26 06:37:55 TM,TC# 2004 03 14 IT1 LK2 13111 19.94 06:25:20 06:35:59 TM,TC,TR,PLDW,DSS 2004 03 14 I1C MAU 42612 24.90 06:26:07 06:35:38 TM,TC,TR$ 2004 03 14 IP4 BLE 25434 48.46 06:27:47 06:41:46 TM,TC,TR,PB,PYS 2004 03 14 IP4 SN1 25434 47.77 06:28:00 06:37:00 OCM_RT,P10(TILT=-20.0) 2004 03 14 IT1 SN1 13111 19.29 06:28:03 06:38:39 NO SUPPORT 2004 03 14 IT1 BLW 13111 18.38 06:29:29 06:39:41 TM,TC#$ 2004 03 14 IP4 MAU 25434 12.84 06:39:22 06:50:42 NO SUPPORT 2004 03 14 IT1 MAU 13111 66.89 06:40:38 06:50:09 TM,TC,TR,PB# 2004 03 14 IP6 BR1 2119 15.82 06:54:08 07:07:12 TM,TC,PB 2004 03 14 IP6 LK2 2119 7.43 07:01:28 07:11:29 TM,TC 2004 03 14 IP6 SN3 2119 5.41 07:04:40 07:11:52 PL,P76,T02.27,RT,L4L3AW 2004 03 14 IP6 BLW 2119 4.46 07:06:28 07:14:42 TM,TC

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13 Software features: -

Windows platform with VC++ allows easy portability.

-

CPU time 250 seconds for generation of one week’s schedule.

-

Optimum support allocation for all satellites.

-

Permits inclusion of more than two chains per ground station.

-

Ease of input parameter tuning.

-

Resolves controller clash (Single controller for multiple satellites)


14 Performance study The software was tested and evaluated by a good team of scientists and implemented for multisatellite schedules generation on a regular basis. The software provides a good improved support allocation from various network stations. A comparative study between the schedules generated using heuristics and that generated using this software is shown below.

Number of passes scheduled Satellite

Heuristics approach

GA method

IRS-1C

126

136

IRS-1D

125

132

IRS-P4

119

122

IRS-T1

107

106

IRS-P3

144

149

IRS-1B

38

36

IRS-P2

22

27

IRS-P6

126

139

Total

807

847

Right tuning of the input parameters may fetch better results. This is now carried out from time to time based on the requirements and feedback.

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15 Conclusion: ¾

Genetic algorithm is applied effectively for multi satellite scheduling by allocating resources optimally to meet the spacecraft requirements.

¾

Visibility clash resolution and allocation of ground station for different spacecraft support was carried out effectively by Genetic algorithm.

¾

6% Increase in support allocation was observed over a week when compared to


heuristic approach of scheduling.

Acknowledgement We thank Mr.S.K.Shivakumar, Director, ISTRAC for his constant support and encouragement. We also acknowledge the contributions from the members of Mission Operations and Health Analysis Group.

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