Dynamic Route Planning and Obstacle Avoidance Model for Unmanned Aerial Vehicles

Transcription

1 Volume 116 No , ISSN: (prined version); ISSN: (on-line version) url: hp:// ijpam.eu Dynamic Roue Planning and Obsacle Avoidance Model for Unmanned Aerial Vehicles 1 K. Prahyusha, 2 A.S.C.S. Sasry and 3 M. Chaianya Suman 1 Deparmen of E.C.E, K.L. Universiy, Vaddeswaram, Gunur, A.P, India. 2 Deparmen of E.C.E, K.L. Universiy, Vaddeswaram, Gunur, A.P, India. 3 Deparmen of E.C.E, Vignan s Nirula Insiue of Technology and Science for Women, Gunur, A.P, India. Absrac Unmanned Aerial Vehicles (UAVs) are he aerial vehicles which operae and move wihou human conroller. Prior o he developmen of UAVs, Manned Aerial Vehicles (MAVs) were in use. Laer o overcome he demeris of MAVs, UAVs were inroduced. Some significan advanages of UAVs over MAVs are i is less expensive, secure and movable. Two major issues in UAVs are: - collisions and obsacles. Obsacle avoidance models can be eiher saic or dynamic and collision occurs due o variable shaped obsacles. Tradiional saic UAV models are no efficien agains polygon obsacle shapes wih dynamic obsacles. In his proposed model, a novel dynamic obsacle avoidance model wih dynamic rouing algorihm is proposed. The whole process of obsacle deecion and avoidance model can be divided ino wo sub-phases, hose are: - 1) Sensing and deecion phase, 2) Avoidance phase. In he sensing and deecion phase, he sysem gahers imporan informaion abou he environmen and deecs wheher an obsacle is presen in he pah or no. Tradiional roue planning and obsacle/collision avoidance models such as Opimized rajecory model, Geomeric model, Force field model and Bearing angle-based model have limiaions, hose are:-. Limied node capaciy, saic configuraion of each pah, saic pah planning mehod, difficul o deec polygon shaped obsacles. In his paper, we have proposed a new dynamic rouing 315

2 algorihm for dynamic obsacle avoidance and pah planning was implemened for UAVs. Experimenal resuls show ha he proposed model has high compuaional efficiency in erms of dynamic obsacle deecion, rouing consrucion, pah planning ime and search space compared o radiional UAV obsacle deecion models. Key Words:UAVs, Pah planning, search space, obsacle deecion. 316

3 1. Inroducion Unmanned Aerial Vehicles (UAVs) can be defined as aerial vehicles which operae wihou he direc involvemen of human conroller. UAVs are mos frequenly implemened in he area of civil and miliary services. Many researchers idenified his necessiy and various works have been carried ou since las wo decades o propose an efficien algorihm for collision and obsacle avoidance. Till dae here is no such opimal algorihm developed. In his paper, we have developed a new dynamic pah planning and obsacle avoidance scheme for UAVs. Besides, we have also analyzed and presened a comparaive summary of all previously developed models for UAVs rouing and collision avoidance. Advanages and disadvanages of every model are also discussed. In UAVs, he prime objecive of collision avoidance sysem is o preven and deec collisions and obsacles (boh saic and dynamic obsacles). The collision and obsacle avoidance algorihm a firs senses he whole environmen and collecs objec informaion. Afer ha, his algorihm deecs wheher obsacles are presen or no. This phase is very imporan in securiy poin of view. There are four basic models inroduced for roue planning and obsacle/collision avoidance, hose are:- Geomeric model, Opimized rajecory model, Bearing angle-based model and Force field model. Geomeric Model Geomeric model was inroduced o deec obsacles (mosly dynamic) in UAVs environmens. The basic requiremens of he algorihm are:- locaion, velociy and direcion of UAV as well as obsacles. ADS-B is no applicable for nonaircraf obsacles. Thus, o overcome his demeri geomeric model is proposed. Collision is deeced by evaluaing rajecory and disance. To avoid collision his shores disance is expanded. ADS-B model is used o sense and rerieve he obsacles informaion. There are some limiaions of his approach, which is resolved by he exension of his model. This model can be exended by considering he wors case scenario i.e., according o he obsacle s (assume a dynamic obsacle such as aacking aircraf) maximum urn rae a hrea region is formed. The original rouing plans can be improved in fuure so ha, UAVs will be able o reach a hrea regions as soon as possible. Opimized Trajecory Model Opimized rajecory model is a modified version of geomeric model. This approach also evaluaes rajecories based on a geomerical way. The mos vial feaure of his model is, he rajecory formed is he opimized rajecory. This approach no only avoids all obsacles and prevens collision, bu also mainain he disance wih arges. The obsacles considered here are mosly saic in naure. A firs, UAV mus sense and gahers informaion of he saic obsacle, 317

4 like locaion, size, and so on. Two major limiaion are:- excessive use of processing power and shorage of ime prior o collision. The proposed algorihm can ake ime parameers as inpus and produces probable fuure coordinaes of UAV. I is compued by using he curren coordinaes and a cos funcion. Collision deecion scheme is almos similar o rajecory calculaion and disance esimaion algorihms. If he prediced fuure coordinaes fails, anoher se of coordinaes are generaed by his algorihm. The cos funcion is re-compued over and over again. This basic model is enhanced laer which is described as follows. A firs he obsacles locaion, size and shape are recorded. Then he whole 2D space is spli ino grids and i is denoed as a weighed graph. Then A* algorihm is applied o generae he pah free from collision. Bearing Angle-Based Model The presened model i.e., bearing angle-based model is a newly proposed model based on visual sensor. I is responsible for deecing he relaive angle of obsacles wih UAV. The visual sensor iniially senses he environmen and capures a picure of he surrounding and sores i. The mos frequen field of applicaion of his approach is spiral flighs. Spiral flighs have consan velociy and a consan relaive bearing. The model generaes an equi-angular spiral rajecory. The issues of visual sensor influence he whole model wih a grea exen. Force Field Model Force field model was inroduced a firs and used mos widely. This mehod is responsible for evaluaing good rajecory where here exis no chances of collisions. I also considers pah complexiy, velociy, locaion ec. I requires five basic measures for he operaion of he algorihm, hose are:- disance beween UAV and obsacle, closeness o he pah, velociy aleraion, unavailabiliy of insan maneuvers and more complex maneuvers. When he compued poenial is small, UAV is moving in he correc pah. Thus, gradien of poenial is considered. The algorihm evaluaes he curren and fuure locaions of UAVs and obsacles. The algorihm execues successfully when, maximum value of poenial gradien is less han hreshold value. Finally afer housands of ieraions, opimized resul is generaed. 2. Lieraure Survey Rapidly exploring Random Trees (RRTs) are responsible for consrucion of rajecories. These rajecories are used for aerial vehicles moving in environmen along wih obsacles. The auhors adoped he concep of Pyhagorean Hodograph in order o join nodes of ree. The major advanage of his developed approach is:- I can achieve faser convergence wih collision resisance. These advanages are applicable for boh saic and dynamic obsacles. [1]. A UAVs formaion conrol sysem wih obsacle avoidance in dynamic 3D 318

5 environmen [2]. A poenial field is developed in beween UAV leader and arge. UAV leader can conrol his arge by using arificial poenial approach and roaional vecors echnique. The collisions can be avoided by hese repulsive forces and he whole force is disribued on a spherical surface. For avoidance of obsacles and collisions, an opimal rajecory is obained. Opimal rajecory no only avoids obsacles bu also reconfigure he formaion process. may be implemened o ge improved reliabiliy. A vision-based navigaion and obsacle avoidance scheme for UAVs [3] idenifies he necessiy of fully auomaed UAVs. GPS sysems are commonly used for navigaion. A camera uni is inegraed along wih he GPS sysem in order o achieve beer precision and obsacle predicion. For efficien pah planning UAV is divided ino four levels. A Level 1, he problem of scheduling he pah can be solved using any of hese hree caegories: Holonomic, Nonholonomic, Kinodynamic[4]. A level 2, implemenaion planning pahs are caegorized ino wo caegories: Environmenal Modeling + Opimum Pah / feasible search and opimal / feasible pah search. Level 3 is divided ino wo online, offline modes. Level 4 uses a mahemaical programming approach o solve he pah planning. These are wo ypes: deerminisic and probabilisic. The auhors emphasized on Dubins pah [5] approach wih saic obsacles. They simulaed heir concep by dividing he obsacles ino polygons ha is responsible for decreasing compuaion ime. Minimum urning radius is aken as inpu o he Dubins pah algorihm and gives rise o Dubin pahs. Dubins pahs are denoed by sraigh lines and angen arcs. The pars of Dubins pah which are parially inerseced wih obsacles are discarded. They used MATLAB o compue he disance and creae weighed marix. Laer Dijksra shores pah algorihm is implemened by he auhors in order o ge he shores pah. 3. Proposed Model The purpose of his model is o find an opimal navigaion pah agains single or muliple dynamics obsacles using he mahemaical objecive funcion. As in he radiional models, as he number of saic conrol poins increases, he navigaion pah becomes more difficul and compuaionally complex o consruc an opimal feasible pah. In his proposed model, we opimized he dynamic obsacle avoidance mechanism o overcome he pah navigaional issues in UAV pah planning and selecion as shown in figure1. The model has following assumpions. All he UAVs have muliple arges in parallel. Each source saion consiss of a leas one ask in parallel mode. All he UAVs have speed v wih he differen aliude. All he UAVs have he maximum fligh ime. 319

6 Boundary Consrains Each UAV can be conrolled using he coordinae locaion(x,y) and he urning angle, i is represened in riple form as (x,y, ). The conrolled node properies of each UAV is represened as x v.cos( ) ---(1) y v.sin( ) ---(2) u.v / ---(3) Where u is he scaling parameer <1,v is he velociy which is normalized o 1 and is he exended minimum urning radius of he polygon obsacle. Consrain 1 Figure 1: UAV Saring and Targe Posiion Obsacle Consrain-1 Here, beween he UAV sar posiion and arge posiion, here exis single polygon obsacles as shown in Figure 1. UAV sar posiion may navigae hrough he lef and righ angen of he single polygon obsacle of UAV navigaion pah. Figure 2: Single Obsacle in a Single and Muliple Direcions 320

7 Consrain 2 Figure 3: Muliple Obsacles in he Pah Direcion Here, beween he UAV sar posiion and arge posiion, here exis muliple polygon obsacles as shown in Figure 3. UAV sar posiion may navigae hrough he lef and righ angen of he single polygon obsacle of UAV navigaion pah. Here, lef side angen enry poin and righ side angen enry poin o he polygon obsacle are denoed by a and a. l o r o Figure 4: Single Shaped Polygon Srucure In figure 4, P R is he polygon maximum radius and P ex represens he exended widh for he arbiary shape obsacle. Figure 5: Muliple Obsacles Disance Measure 321

8 Muliple Obsacles Disance Measure Le (xsar y sar, sar ) denoes he sar UAV locaion and and denoes he muliple parallel arge locaions.(i=1,2 n) Consrain 3: Enry Condiion (x y, ) i i i arge arge arge Exended radius of he pah can be checked agains wo inequaions a he source end as shown below: ( x.cos( R ) x ) (y.cos( R ) y ) ( P ) 0 ---(4) j 2 j 2 2 sar sar oi sar sar oi Ri (x +τ.cos(θ -R )-x ) +(y +τ.cos(θ -R )-y ) -(τ+p ) <0 ---(5) j 2 j 2 2 sar sar θ oi sar sar θ oi Ri If boh condiions are saisfied hen no pah exiss beween he source and desinaion when obsacle occurs in beween he pah as shown in figure 6. Where j j x o, i oi Figure 6: Enry Condiion for Obsacle Posiions y denoes he saring he obsacle posiions of jh arge and R 0 0 denoes he urning angle wih 45 R 90. Consrain 4: Exi Condiion Exended radius of he pah can be checked agains wo equaions a he desinaion end as shown below: ( x.cos( R ) x ) (y.cos( R ) y ) ( P ) 0 ---(6) j j 2 j 2 2 arg e arg e oi arg e arg e oi Ri ( x.cos( R ) x ) (y.cos( R ) y ) ( P ) 0 ---(7) j j j arg e arg e o arg e arg e o R i i i Figure 7: Exi Condiion for Obsacle Posiions Opimizaion Funcion C u : Toal number of UAVs. C : Number of arges. 322

9 C s :Numberof parallel arges from he source saion. B j i : Boolean flag which indicaes he link from saion i o j is visied by UAV or no (0: no visied, 1: visied). D (i,j) : Disance beween i h saion o j h saion. TP(j) : Trajecory posiion of arge j. T max : Maximum fligh ime. T o : UAV ime o visi all nodes in he original rajecory. T n : UAV ime o visi all nodes in he new direcion o he arge. T e : T o - T n. F p : Fuure pah nodes. Prob((j, i )/ F p ( i )) : Probabiliy of he curren node j o he node in he fuure pah lis. The muli-objecive model for he parallel obsacles avoidance procedure is given below: i MinMax Pr ob((j, i ) / F p( i )).B( j, ).D( j, ) / vi s. i j, C ( x.cos( R ) x ) (y.cos( R ) y ) ( P ) 0; j j 2 j 2 2 arg e arg e oi arg e arg e oi Ri j j 2 arg e arg e o i arg e arg e j 2 2 ( x.cos( R ) x ) (y.cos( R ) y ) ( P ) 0; i B { 01, } ( j, ) Pr ob((j, ) / F ( )) 0 B j ( j, ) 0 i p i UAV Dynamic Opimal Pah Navigaion Inpu i i i (x y, ) denoes he sar UAV locaion and and (x y, ) sar sar sar denoes he muliple parallel arge locaions.(i=1,2 n) Node acual cos funcion and prediced cos funcion : F(i, j) D(i, j) H(i, j) Prob((j, ) / F ( )) ShoresDis(i, j) i p i oi Ri arge arge arge Procedure 1) Iniialize all nodes in he UAV model wih 3-dimensional parameers as 2) The conrolled node of each UAV is represened as x v.cos() y v.sin() u.v / 3) Selec he source and arge nodes. 4) Sar model a he source node. 5) Search nex node using A* search mehod. 6) Compue cos funcions F(i,j) and H(i,j). 323

10 F(i, j) D(i, j) H(i, j) Prob((j, ) / F ( )) Manha an Dis(i, j) i p i 7) Check he boundary consrains 1,2,3,4. 8) For each pah in he search mehod Opimize he pah using he opimizaion model wih minimum disance and maximum obsacles. Check wheher he curren node is he arge node or no. If no break he pah. Oherwise Check for anoher pah. Check wheher he ime period exceed he max imer T max, if exceeds sop he model, oherwise prin he pah saisics End for 4. Experimenal Resuls & Performance Analysis In his secion, we presen he experimenal resuls behind our dynamic pah planning UAV model for polygon obsacle avoidance. Firs, we represen he experimen resuls, hen we show he performance analysis. The proposed resuls are all simulaed in Java IDE, on an Inel Core i5-4210u wih 2.40GHz, 8GB RAM. Saic Obsacle Avoidance Soluion Figure 8: Saic Pah Consrucion Problem 324

11 Proposed Soluions Soluion-1: Dynamic Pah Consrucion and Terminaion Figure 9: Dynamic Pah Consrucion and Terminaion Soluion-2: Dynamic Obsacle Consrucion wih Variable Size and Locaion a) Iniial Seup of Dynamic UAV b) Pah Planning and Navigaion 325

12 Time(ms) Inernaional Journal of Pure and Applied Mahemaics c) Dynamic Polygon Obsacles and Dynamic Roue Planning Compared o (b) 5. Performance Analysis Table 1: Performance of Proposed Model in Terms of Number of Obsacles, Mean Time Per Ieraion and Pah Planning Mean Time MeanimeIeraion PahPlanningMeanTi me 0 #5 #10 #15 #20 #25 Obsacles Figure 10: Performance of Proposed Model in Terms of Number of Obsacles, Mean Time Per Ieraion and Pah Planning Mean Time 326

13 Time(ms) Inernaional Journal of Pure and Applied Mahemaics Toalimeobsacles PlanningTime Models Figure 11: Performance of Proposed Model o he Tradiional Models in Terms of Number of Obsacles and Pah Planning Mean Time 6. Summary and Conclusion In his paper, a novel dynamic pah planning and polygon obsacle avoidance model for UAVs are proposed. The key advanage of our model is o find he dynamic polygon obsacles and finds he new fuure pah o reach he desinaion. In his paper, we have proposed a new dynamic rouing algorihm for dynamic obsacle avoidance and pah planning was implemened for UAVs. The fac ha polygon angen lines and dynamic pah are combined o form a fuure opimal pah for UAV, which makes his model very effecive. Finally, we have compared our model o he pre-exising models for UAVs rouing and collision avoidance in erms of obsacle deecion ime and pah planning ime. In fuure, his model can be exended o classify differen shapes of obsacles in he dynamic environmen. References [1] Neo A.A., Machare D.G., Campos M.F., On he generaion of rajecories for muliple UAVs in environmens wih obsacles, In Seleced papers from he 2nd Inernaional Symposium on UAVs, Reno, Nevada, USA, Springer Neherlands (2009), [2] Chang K., Xia Y., Huang K., UAV formaion conrol design wih obsacle avoidance in dynamic hree-dimensional environmen, Springer Plus 5 (1) (2016), [3] Jian L., Xiao-min L., Vision-based navigaion and obsacle deecion for UAV, IEEE Inernaional Conference on Elecronics, Communicaions and Conrol (ICECC) (2011),

14 [4] Prahyusha K., Sasry A.S.C.S., Sreenivasa Ravi K., UAV shores pah planning and collision free pah A Review, Journal of Advanced Research in Dynamical and Conrol Sysems (2017), [5] Yang C., Liu L., Wu J., Pah planning algorihm for small UAV based on dubins pah, IEEE Inernaional Conference on Aircraf Uiliy Sysems (AUS) (2016), [6] Lakshmi K., Surendar A., Verificaion of axiproocol using sysem Verilog, Inernaional Journal of Mechanical Engineering and Technology 8 (5) (2017), [7] Surendar A., Kaviha M., Secure paien daa ransmission in sensor neworks, Journal of Pharmaceuical Sciences and Research 9 (2) (2017), [8] Surendar A., FPGA based parallel compuaion echniques for bioinformaics applicaions, Inernaional Journal of Research in Pharmaceuical Sciences 8 (2) (2017), [9] Surendar A., Evoluion of gai biomeric sysem and algorihms- A review, Journal of Biomedical and Pharmacology 10 (1) (2017),

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