Dynamic Mission Control for UAV Swarm via Task Stimulus Approach

Transcription

1 Amercan Journal of Intellgent Systems 2012, 2(7): DOI: /.as Dynamc Msson Control for UAV Swarm va Task Stmulus Approach Haoyang Cheng *, John Page, John Olsen School of Mechancal and Manufacturng Engneerng, Unversty of New South Wales, Sydney, 2052, Australa Abstract Interest n self-organzed (SO), mult-robotc systems s ncreasng because of ther flexblty, robustness, and scalablty n performng complex tasks. Ths paper descrbes a decentralzed task allocaton model based on both task stmulus ntensty and a respondng threshold. The response threshold method was developed through observatons of socal nsects. It allows a swarm of nsects wth a relatvely low-level of ntellgence to perform complex tasks. In ths work, an agent based smulaton envronment s developed ncorporatng these deas. The msson scenaro smulated n ths study s a wde area search and destroy msson n an ntally unknown envronment. The msson obectves are to effectvely allocate a UAV swarm to both optmze coverage of the search space and attack a target. Rule based behavours were used to create UAV formatons. Two sets of smulatons wth dfferent swarm sze and target numbers were performed. The smulaton results show that wth task stmulus ntensty and a respondng threshold, the UAV swarm demonstrates emergent behavour and ndvdual vehcles respond adaptvely to the changng envronment. Keywords UAV, Swarm Intellgence, Self-Organzaton, Smulaton 1. Introducton Wth recent technologcal advances n autonomous control and communcaton, mult-robotc systems are recevng a great deal of attenton due to ther ncreased ablty to carry out complex tasks n a superor manner when compared to sngle-robotc systems. Multple autonomous agents workng n-group exceed the sum of the performance of the ndvduals. Currently, the human factor assocated wth the UAV (unmanned aeral vehcle) operators workload s one of the key lmtatons to ncreasng future UAS (unmanned aeral system) effectveness[1]. Such a requrement not only ncreases the expense of UAV operatons, but also makes coordnaton among UAVs more complex. For these reasons, research s requred to nvestgate methods of ncreasng UAV autonomy and nteroperablty, whle reducng global communcaton and human operator relance. The cooperatve control of UAVs s a complex problem that s domnated by uncertanty, lmted nformaton, and task couplng. Due to the complextes of the nherent problems, centralzed decson and control algorthms are tradtonally adapted to optmse tmng and task constrants. One of the msson scenaros consdered n the lterature s cooperatve movng target engagement. Kngston & * Correspondng author: haoyang.cheng@student.unsw.edu.au (Haoyang Cheng) Publshed onlne at Copyrght 2012 Scentfc & Academc Publshng. All Rghts Reserved Schumacher n ther paper solved ths problem wth a mxed nteger lnear program that addressed task tmng constrants and agent dynamc constrants to generate a flyable path[2]. A genetc algorthm (GA) s used to effcently search the space of possble solutons and provde the cooperatve assgnment[3]. Schumacher and Chandler also addressed the problem of task allocaton for a wde area search muntons scenaro[4]. A network flow optmzaton model s used to develop a lnear program for optmal allocaton of powered muntons to perform several tasks, such as search, classfy, attack and fnally damage assessment of potental targets. Cooperatve task assgnment wthn an adversaral envronment was addressed n[5][6]. In ther work, a cooperatve task assgnment was computed wth the addtonal knowledge of the future mplcatons of a UAV s actons. Ths was done to mprove the expected performance of the other UAVs. In order to mplement a centralzed controller, consensus has to be reached under communcaton constrants. False nformaton and communcaton delay strongly negates the benefts of cooperatve control[7]. Alghanbar n hs thess[8] developed the unbased dstrbuted Kalman consensus algorthm whch he proved converged to an unbased estmate for both statc and dynamc communcaton networks. Despte ts benefts, the use of a centralzed controller lacks sgnfcant robustness, s computatonally complex and depends on hgh degree of global nformaton. Such requrements make the mplementaton of centralzed approach ntractable n real-lfe mssons. In contrast, decentralzed decson and control algorthms,

2 178 Haoyang Cheng et al.: Dynamc Msson Control for UAV Swarm va Task Stmulus Approach trade optmalty and predctablty, wth robustness and adaptaton to envronmental changes. A self-organzed (SO) system, or swarm, s typcally a decentralzed control system made up of autonomous agents that are dstrbuted n the envronment and follow stmulus response behavours[9]. Examples from socal nsects, such as foragng and the dvson of labour show that SO systems can generate useful emergent behavours at the system level. Self-organsed, swarm based systems do not requre a centralsed plan or delberate acton from ndvduals. They therefore have the potental to reduce msson plannng and therefore the amount of ntellgence requred n control systems desgn. Moreover, self-organsed, swarm based systems demonstrate robustness and scalablty. Ths means that addng or subtractng agents to or from the system may not sgnfcantly affect the overall performance and emergent behavours of the system. Sgnfcant research effort has been nvested n recent years nto the desgn and smulaton of ntellgent swarm systems[10]. Intellgent swarm systems can generally be defned as decentralzed systems, comprsed of relatvely smple agents whch are equpped wth the lmted communcatonal, computatonal and sensng abltes requred to accomplsh a gven task[11]. Gaudano, et al. n ther studes, tred to apply quanttatve methodologes to evaluate the performance of UAV swarms under a varety of condtons[12]. In Prce s research, ten self-organzaton rules were mplemented whose weght factors were collected nto a sngle ftness functon. Ths functon was further refned usng a genetc algorthm wthn the smulaton[13]. Another wdely adopted mechansm s dgtal pheromone maps that mtate the foragng behavour of ants. Dgtal pheromones are modelled on the pheromone felds of the ndvdual vehcles. By synchronzng ther maps the UAVs coordnate to avod redundant searches[14]. Hauert, et al. had been nvestgatng the potental of usng a swarm of UAVs to establsh a wreless communcaton network[15]. They appled artfcal evoluton to develop neuronal controllers for the swarm of homogenous agents. Ths study desgns a swarm controller to enable a swarm of UAVs to search for and attack targets. The concept of employng swarms of weapons was explored n[16]. In ths concept, the ndvdual of the swarm may be less capable than conventonal weapons, but through cooperaton across the swarm, the swarm exhbts behavours and capabltes that can exceed those demonstrated by conventonal systems that do not employ cooperaton. To mplement a swarm system, they desgned a rule set of dscrete behavours, whch was governed by nteractve subsumptve logc. Buldng upon ther research, we adopted an adaptve task controller based on task stmulus to optmze the performance of the swarm. In ther desgn, once the targets were dscovered, the weapons would home n on them. Through communcaton, other weapons had knowledge of how many weapons had been commtted to a gven target. Smple rules prevent excessve numbers of weapons from beng expended on a sngle target. Our approach also takes nto consderaton the value of the targets and the tme left n the msson. Thus the weapons would not be largely expended n the early stage of the msson, especally on low value targets, whch leaves an nadequate number of UAVs to search for the remanng targets. The remander of ths paper s structured as follows. Secton II descrbes the msson n detal and the smulaton tools used n ths study. In secton III, we present the task controller. The results of the smulaton are gven n secton IV whle secton V concludes the paper. 2. Smulaton Envronment In ths secton, we present the smulaton tool and the overvew of the smulated msson scenaro Smulaton Tool Agent-based smulatons of complex adaptve systems are becomng an ncreasng popular tool n the artfcal lfe communty. The applcaton of agent-based smulatons n combat modellng had been explored by Ilachnsk[17]. He argued that agent-based models are most useful when they are appled to complex systems that can be nether wholly descrbed nor can be bult by conventonal models based on dfferental equatons. Agent-based models are desgned to allow users to explore evolvng patterns of system-level behavour that derve collectvely from low-level nteractons among ther agents. In ths study, the SWARM smulator, developed by the Santa Fe Insttute was used to construct the smulaton envronment[18]. It provdes a conceptual framework for desgnng, descrbng, and conductng experments on agent-based models. Ths framework allows ndependent agents to nteract va a schedule of dscrete events. Bharathy et al. provded an archtectural frame for generc agents, whch comprsed three external domans wthn an agent to nteract wth the agent s envronment: percepton doman, acton doman and communcaton doman[19]. Wthn the SWARM framework, the agents communcate both wth each other and wth ther envronment va messages. SWARM supples a basc system lbrary that manages a dynamc lst of obects and handles message passng between obects. In our desgn, two dfferent obects were modelled, namely the UAVs and ther targets. The nternal functons of the obects, whch descrbe the rule set of behavours, handle the messages that are sent to the obect and modfy ther nternal states Msson Descrpton In applyng these theoretcal concepts to a UAV swarm applcaton, we desgned the followng wde area search and attack scenaro. Ths scenaro ams to search for targets wthn the defned area and destroy or degrade targets. The followng s a lst of assumptons and functonaltes we use n our smulaton:

3 Amercan Journal of Intellgent Systems 2012, 2(7): The vehcles consdered n ths study are relatvely smple platforms wth the smallest effectve payload and wth a mnmal set of onboard detecton devces to sense the envronment and one another other. One concept of such platform s called the Low Cost Autonomous Attack System (LOCAAS), whch has a range of 100 nautcal mles as well as laser radar wth automatc target recognton to dentfy potental targets[20]. The msson area s defned as a rectangular regon. Each UAV s aware of the terran boundares and wll turn as t approaches a boundary to reman wthn the target area. The UAV has only enough fuel to fly over the msson area once. The UAV s were modelled as dentcal agents n the smulator. Each UAV s equpped wth a global postonng system (GPS) navgaton system for autonomous waypont navgaton, a crcular sensor to detect the poston of other UAVs wthn a specfed radus and a lmted range communcaton system. The UAV s fly at constant alttude but at a varable speeds. Ther vehcle dynamcs wll be dscussed n next secton. The UAVs can change modes between search and attack. They can only change mode from search to attack once. The UAVs are themselves weapons whch are destroyed n the attack process. The attack wll have 80 percent chance of beng successful. The targets are statonary and randomly dstrbuted wthn target area. The potental targets could be relocatable ar defence stes or vehcles n columns. The targets may be of hgh or low value. The stmulus of hgh value targets s set to be twce that of the low value targets. The onboard target recognton software has the ablty to recognze the target type. The locaton and the nature of the targets are unknown to each UAV. Each target s assumed to be an array of ground vehcles or ar defence unts. In ths smulaton, each target array must be ht by 8 UAVs to be destroyed. The status of the target wll degrade as the number of UAV attacks ncrease, so that the stmul that attract UAV attacks wll decrease. The msson control strategy based on task stmulus wll be dscussed n secton III. The msson was started wth two groups of UAVs enterng the msson area from the left sde (Fgure 1). The UAVs were assumed to be deployed from a cargo arcraft, though ths could have been a shp or ground transport. The reason for deployng them n two groups was to encourage them dstrbute themselves evenly across the space durng the msson. In[21], the feasblty of usng cargo arcraft for the mass delvery of standoff muntons s nvestgated. The UAV s spread out mmedately after release and form a loose formaton usng a sx rule set (dscussed n subsecton B below) to hunt for targets (Fgure 2). As shown n the fgure, the targes wthn a crcle were the ones that had been detected. The UAV s shown n green began the attack. Through communcaton, other UAVs that met certan proxmty requrements became aware of the dentfed targets. However, the onboard task controller would decde whether to attack the targets or proceed to search for other targets. Fgure 1. Msson Start Fgure 2. Search and Attack 3. Dynamc Msson Control In ths secton, the rule set of behavours and task controller s dscussed Rule sets of Movement The behavours of the UAVs are bult upon rule sets descrbng formaton mantenance and target nteracton. The mathematcal defnton of the rules s nspred from prevous research[13]. Ths study extended the prevous research by ntroducng two behavor logc modes on top of the rules, whch are the search mode and target engagement mode. The swtch between behavor modes s controlled by the task controller, whch wll be dscussed n detal n next secton. In ths study, sx rules are used to govern the movement of the vehcle: coheson, algnment, separaton, collson avodance, target avodance and goal seekng. When the UAVs are n the search mode, every rule s actve, causng the vehcles to spread out whle preservng ntervehcle communcaton. Once the target engagement mode s trggered, the UAVs are commtted to the attack phase and only collson avodance and goal seekng rules are actve. Each of these rules s mathematcally defned below. 1) Rule 1: Coheson The coheson rule makes UAVs attract each other by orentatng ther acceleraton vectors n the drecton of the local flock center provded that the dstance between them s greater than some set value.

4 180 Haoyang Cheng et al.: Dynamc Msson Control for UAV Swarm va Task Stmulus Approach P NB a1 norm P N { } U U (1) NB U P P < r, (2) where P P means the dstance between U and U.. N denotes the number of neghbours of UAV. 2) Rule 2: Algnment The algnment rule enables a UAV to algn ts headng and match ts speed wth ts neghbours. V NB P P a2 norm (3) N, 1) Rule 3: Separaton The separaton rule enables each UAV to mantan a dstance between tself and ts neghbours to prevent sensor overlap. It apples a repulson vector that ponts away from neghbourng UAVs. The magntude of the repulson vectors are based upon a UAV s dstance from ts neghbors. P P a3 norm ( P P) 1 f ( x ) NB d s 1, P P ds f ( x) (4) 0, P P > ds. 2) Rule 4: Collson Avodance The collson avodance rule works n a smlar manner to separaton rule but wth a lower separaton dstance. 1 a4 norm ( P P) f ( x) NB 2 P P d c 1, P P dc f ( x) (5) 0, P P > dc. 3) Rule 5: Target Avodance The target avodance rule keeps a UAV from flyng over detected targets. It works n a smlar manner to the separaton rule n that t provdes a repulson vector away from a known target. P P a5 norm ( P P) 1 f ( x ) NB d t 1, P P dt f ( x) (6) 0, P P > dt. 4) Rule 6: Goal Seek The goal seek rule gudes a UAV to fly toward the next waypont or target poston f t s n attack mode. a6 norm ( P w P ). (7) The actons of the rules are weghted and summed to gve a desred acceleraton vector aa for each UAV. a. (8) 6 k 1 wf k a The desred acceleraton s lmted by maxmum lnear and radal acceleraton. The calculaton of velocty and poston use the followng equatons: V ( t + t) V ( t) + a t (9) P t + t P t + V. (10) k ( ) ( ) t 3.2. Task Controller In Bonabeau, et al[10], a task allocaton model based on a response threshold was developed through the observaton of socal nsects. The response threshold refers to the lkelhood of reactng to a task assocated stmulus. A response threshold θ, expressed n the unts of stmulus ntensty, s an nternal varable that determnes the tendency of an ndvdual to respond to the ntensty of the stmulus S and perform the task. A response functon T θ (S) whch s the probablty of performng the task as a functon of stmulus ntensty ss, s gven by: n S Tθ, (11) n n S + θ where n > 1determnes the steepness of the threshold. As we can see from ths functon: for S << θ, the probablty of engagng a task s close to 0, and for S >> θ, ths probablty s close to unty. The msson obectves nvestgated n ths paper focus on the effectve allocaton of weapons to maxmse the coverage of the search space and attacks on targets. Use of flockng rules dscussed above equally dstrbuted the swarm n the search space and then re-adusted ts formaton after an attack. One of the most challengng aspects that mght affect the effcency of the msson s how to allocate the swarm assets to each task. The tasks nclude search and the selecton of the target to attack. An attack on a target causes the number of searchng UAVs be reduced. An attack n the early stage of the msson wll leave an nadequate number of UAV s to search and destroy the remanng targets. In order to use fewer vehcles to cover a greater target area, we desgned a target area wth a lmted sze. As a consequence, the UAVs fly over the search area once and so attackng targets after searchng the whole area was not an opton. It was mportant to make the UAVs decde when to attack and whch target to attack. Ths study attempted to address these ssues utlsng the way self-organsng and collectve behavour emerge from nteractons among ndvduals and local envronmental changes. We desgned an adaptve task allocaton model based on task stmulus ntensty and respondng threshold to maxmse the performance of the swarm.

5 Amercan Journal of Intellgent Systems 2012, 2(7): Ths task control algorthm runs n each UAV at each tme nterval and calculates the probablty that an ndvdual should change ts mode to attack. It s assumed that target array T takes N UAVs to attack successfully so as to be totally destroyed after t s detected. The dscrete tme dynamcs of the stmulus ntensty related to T s updated by: S( t) N act + δ V, N act < N S ( t + 1) (12) 0, N act N, where N act s the total number of UAVs that are assgned to attack target T, N act s the number of UAVs that attack T and make a successful mpact n the last tme step, δ s the ncrease n stmulus ntensty per unt of tme and V s the value of the target. If N act s equal to N, whch means the target has already been totally destroyed, ts stmulus becomes zero. The stmulus of hgh value targets wll grow faster than low value targets once they are detected. The probablty P that the ndvdual U attacks a target T s gven by: 2 S P β 1 e 2 2 S + α θ d γ (13), where α, β and γ are coeffcents that control the nfluences of θ. d denotes the dstance between U and T. The last term of equaton (13) forces the UAVs to be more lkely to attack the targets that are nearest to them f multple targets are dscovered. Each UAV at every tme nterval wll decde whether to attack a target or proceed to search for other targets based on ths probablty. Ths controller wll make a UAV choose a target wth hgh stmulus ntensty and spatally closer. Because the swarm self-organzes, the msson plannng requrements are sgnfcant relaxed. Msson plannng can be accomplshed as an emergent behavour, rather than as decsons sent to ndvdual vehcles. The proposed task control algorthm s shown n fgure 3. For each control cycle True Change to target engagement mode. Update stmulus ntensty related to each target. Calculate the probablty P that the agent U attacks a target T. Generate random number R unformly dstrbuted between 0 and 1. Fgure 3. Task control flow chart 4. Smulaton Results R > P False Keep searchng for new targets Rato of actural ponts to maxmum ponts Attack Rato Target Detect Rato θ 0, β 0 θ 5, α 1, β 0.5 θ 10, α 1, β 0.5 θ 15, α 1, β 0.5 θ 20, α 1, β 0.5 only attack hgh value target Fgure 4. Smulaton result wth 40 UAVs and 8 targets

6 182 Haoyang Cheng et al.: Dynamc Msson Control for UAV Swarm va Task Stmulus Approach 0.90 Rato of actural ponts to maxmum ponts Attack Rato Target Detect Rato θ 0, β 0 θ 5, α 1, β 0.5 θ 10, α 1, β 0.5 θ 15, α 1, β 0.5 θ 20, α 1, β 0.5 only attack hgh value target Fgure 5. Smulaton result wth 50 UAVs and 10 targets Two sets of smulatons wth dfferent swarm sze and target numbers were performed to test the msson control algorthm, namely: (1) 40 UAVs and 8 targets and (2) 50 UAV s and 10 targets. The smulatons were run n batch mode 30 tmes for each settng. The smulaton results are shown n fgures 4 and 5. In the smulatons, half of the targets were of hgh value whle half were of low value. The poston and status of the targets were unknown to the smulated UAVs before beng detected. Each target was assumed to take potentally 8 UAVs to be totally destroyed. Each attack had an 80 percent chance of beng successful. To evaluate the msson performance, we assumed that an attack on a hgh value target receved 2 ht ponts, whle an attack on low value target receved 1 ht pont. The best stuaton was that all UAVs would attack a hgh value target and thus receve the most ht ponts. The attack rato s actual ponts receved n a msson dvded by maxmum ponts. The target detecton rato s the rato of detected targets to total targets. The smulaton results demonstrate the change of msson performance on attack and search as the value of θ n equaton (13) s vared. As s evdent from the smulaton results, both experments wth dfferent swarm sze and target numbers show the same pattern. We use θ 0, β 0 as the baselne case. Because when θ 0, β 0, the stmulus ntensty of the target has no effect, so the swarm wll attack every target mmedately after t s detected untl the target s totally destroyed. Ths s smlar to the swarm behavour used n prevous research[13][16]. In ths case, the msson performance of the swarm s not effcent because the UAVs attack any detected targets regardless of the value of the target and ths may leave an nadequate number of UAVs to search for new target. As can be seen from the results, the target detecton raton s extremely low. Wth θ ncreasng, the UAVs become less lkely to attack a target wth low stmulus ntensty and pay more attenton to hgh value targets. However, the target stmulus ntensty wll ncrease for each tme step, the UAVs wll stll choose to attack low value target before runnng out of fuel f no more hgh value targets are detected. After θ passes a certan pont, the attack rato wll fall because searchng becomes more attractve than attackng. The last strategy we used for comparson was to let the UAVs only attack hgh value targets and gnore low value targets. As s shown, ths strategy s not as effcent as the one wth the optmum θ settng, because t runs the rsk of wastng sgnfcant UAV resource f not enough hgh value targets are detected. 5. Conclusons We have presented a formulaton for a swarm of UAVs engaged n a wde area search and destroy msson n an unknown envronment. We have used flockng behavour to control the movement of the UAVs whle searchng the target area, as the rule based behavour helps the vehcles to quckly dsperse after deployment and readust the formaton as requred. We have developed a decentralzed msson control mechansm based on task stmulus ntensty and a respondng threshold, whch can optmze the performance of the swarm. The smulaton results show that, by usng ths mechansm, a swarm of UAVs demonstrates emergent behavour through self-organsaton, and ndvdual vehcles respond adaptvely to the changng envronment. The smulaton scenaro presented here s very smplfed n comparson to a real msson. One weakness beng the msson control mechansm only allocates UAVs to a certan target. A more sophstcated cooperatve control algorthm s needed to control UAVs attackng multple targets, especally when possble adversary acton of the target s taken nto consderaton. Nomenclature a acceleraton vector W waypont NB set of neghbours of UAV P poston vector of agent V velocty vector of agent r nteracton range between UAVs d c dstance below whch the collson avodance rule take effect

7 Amercan Journal of Intellgent Systems 2012, 2(7): d s separaton dstance from other UAVs d t separaton dstance from target norm(v) normalze vector V wf weght factor for each rule S stmulus ntensty θ response threshold P probablty that U attack T REFERENCES [1] K. Heffner and F. Hassane, "Towards Intellgent Operator Interfaces n Support of Autonomous UVS Operatons," n 16th Internatonal Command and Control Research and Technology Symposum, Quebuc, Canada, [2] D. B. Kngston and C. J. Schumacher, "Tme-dependent cooperatve assgnment," n Amercan Control Conference, Proceedngs of the 2005, 2005, pp vol. 6. [3] T. Shma and C. Schumachery, "Assgnng Cooperatng UAVs to Smultaneous Tasks on Consecutve Targets usng Genetc Algorthms," presented at the AIAA Gudance, Navgaton, and Control Conference and Exhbt, San Francsco, Calforna, [4] C. Schumacher, et al., "Task allocaton for wde area search muntons wth varable path length," n Amercan Control Conference, Proceedngs of the 2003, 2003, pp vol.4. [5] J. S. Bellngham, et al., "Cooperatve path plannng for multple UAVs n dynamc and uncertan envronments," n Decson and Control, 2002, Proceedngs of the 41st IEEE Conference on, 2002, pp vol.3. [6] M. Alghanbar and J. P. How, "Cooperatve task assgnment of unmanned aeral vehcles n adversaral envronments," n Amercan Control Conference, Proceedngs of the 2005, 2005, pp vol. 7. [7] T. Shma and S. Rasmussen, UAV Cooperatve Decson and Control: Challenges and Practcal Approaches: SIAM, Socety for Industral and Appled Mathemcs, [8] M. Alghanbar, "Robust and Decentralzed Task Assgnment Algorthms for UAVs," Doctor of Phlosophy, Department of Aeronautcs and Astronautcs, Massachusetts Insttute of Technology, [9] S. Garner, et al., "The bologcal prncples of swarm ntellgence," Swarm Intellgence, vol. 1, pp. 3-31, [10] E. Bonabeau, et al., Swarm Intellgence- From Natural to Artfcal Systems: Oxford Unversty Press, [11] Y. Altshuler, et al., "Swarm Intellgence-Searchers, Cleaners and Hunters," n Swarm Intellgent Systems, ed, 2006, pp [12] P. Gaudano, et al., "Control of UAV Swarms: What The Bugs Can Teach Us," presented at the 2nd AIAA "Unmanned Unlmted" Systems, Technologes, and Operatons, San Dego, Calforna, [13] I. C. Prce, "Evolvng self-organzed behavor for homogeneous and heterogeneous UAV or UCAV swarms," Master of Scence, Department of Electrcal and Computer Engneerng, Ar Force Insttute of Technology, [14] C. A. Ergnac, "An Exhaustve Swarmng Search Strategy based on Dstrbuted Pheromone Maps," presented at the AIAA Infotech@Aerospace 2007 Conference and Exhbt, Rohnert Park, Calforna, [15] S. Hauert, et al., "Evolved swarmng wthout postonng nformaton: an applcaton n aeral communcaton relay," Autonomous Robots, vol. 26, p. 11, [16] D. R. Frelnger, et al., Prolferated Autonomous Weapons: An Example of Cooperatve Behavor. Santa Monca, CA: RAND Corporaton, [17] A. Ilachnsk, Artfcal War: Multagent-Based Smulaton of Combat: {World Scentfc Publshng Company}, [18] (June 6). Swarm man page. Avalable: org/ndex.php/swarm_man_page [19] G. K. Bharathy, et al., "Agent Drected Smulaton for Combat Modelng and Dstrbuted Smulaton," n Engneerng Prncples of Combat Modelng and Dstrbuted Smulaton, ed: John Wley & Sons, Inc., 2012, pp [20] (June 6). Low Cost Autonomous Attack System (LOCAAS). Avalable: [21] M. E. Franke and G. Ar, "Use of Cargo Arcraft for Launchng Precson-Guded Muntons," presented at the 42nd AIAA Aerospace Scences Meetng and Exhbt, Reno, Nevada, 2004.