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3 RoboCup FIRA, (Angle) Ka (Position) (RunArc) i

4 Global Vision based Motion Parameters Analysis and Basic Actions Improvement for Robot Soccer Systems Abstract With the development of robotics and artificial intelligence, scientists were often faced with issues on cooperation and coordination among different robots in a workspace. This has led to the developments in multi-robot cooperative systems MRCS. As a research model of MRCS, soccer robot is an interdisciplinary area that involves the knowledge of robotics, intelligence control, wireless communication, computer science, sensor technology, image processing, artificial intelligence, and so on. It has become a public experimental platform for universities all over the world to communicate with each other. The first chapter of the paper introduces robot soccer games and the four subsystems of soccer robot systems. In the following chapter, the strategy system and the control scheme of robot soccer system is discussed in detail. Following the discussion of the importance of action designing, the mechanism of basic behavior generation is introduced and the codes of the two original basic behavior functions, which need improving, are shown. The last chapter is the focus of the whole paper. Based on analysis of motion parameters, the velocity parameter Ka of the Angle function is tuned through experiments to get the best results; To overcome the disadvantages of the former Position function, a RunArc function is developed, which can control the robot to run a smooth arc path to a destination; At last, the method of controlling the robot to run to the target by different trajectories is successfully realized, using the feedback information of the global vision subsystem. Key words Soccer robot, Motion analysis, Action design, Global vision ii

5 ...i Abstract... ii... iii RoboCup FIRA RoboCup FIRA (MIROSOT) Angle Ka (Angle) Ka Position Position iii

6 1.1 [1] 50 IBM? Alan Mackworth 1992 On Seeing Robots [2] [3]

7 1.1.1 RoboCup FIRA RoboCup MiroSot FIRA Alan Mackworth RoboCup J (the Robot World Cup Soccer Games) RoboCup[4] IJCAI SONY RoboCup (the Robot World Cup Soccer Games and Conferences) 2000 RoboCup 40 WrightEagle WrightEagle RoboCup TsinghuAeolus Padua RoboCup (RoboCupSoccer) 1 (Simulation) 2 (Small-Size) 3 (Middle-Size) 4 SONY (Legged) 5 (Humanoid) FIRA 1997 [5] FIRA 2

8 1-1 FIRA cm cm NAROSOT S-MIROSOT MIROSOT ROBOSOT KheperaSot HUROSOT SimuroSot 2001 RoboCup RoboCup RoboCup RoboCup RoboCup FIRA RoboCup RoboCup RoboCup Client/Server RoboCup Soccer Server Client Client Soccer Server Soccer Server 100 Soccer Server 1-2 Client Client UDP Socket Server Socket Client Client RoboCup [6][7] FIRA 11 3

Client Socket Client Socket Monitor Client Socket 1-2 Soccer Server Client Server Client Server move( ) turn( ) turn_neck( ) kick( ) dash( ) sense_body( ) say( ) Server Client see( ) hear(

9 Client Socket Client Socket Monitor Client Socket 1-2 Soccer Server Client Server Client Server move( ) turn( ) turn_neck( ) kick( ) dash( ) sense_body( ) say( ) Server Client see( ) hear( ) turn kick dash say say Server 2002 RoboCup TsinghuAeolus Everest 1-3 TsinghuAeolus Everest TsinghuAeolus TsinghuAeolus 7 0 Everest RoboCup 1-3 RoboCup monitor (TsinghuAelous V.S. Everest) 4

1.1.3 FIRA (MIROSOT) FIRA 1 5 150cm 130cm ( ) 1-4 1-4 FIRA (2m) 1.

10 1.1.3 FIRA (MIROSOT) FIRA cm 130cm ( ) FIRA (2m) 1.2 system) (robots) (soccer robot system) [8][9] (vision (host computer) (RF communication system)

11 CPU (Vision-based soccer robot system) (Robot-based soccer robot system) (Remote-brainless vision-based soccer robot system) (Brain on-board vision-based soccer robot system) 1 CPU [10] 2D, : ( ), ( 1-6),,,,,, 6

12 45 1-6,,,,, NTSC 24 RGB RGB HSI 3 3 ( ) ( ) ( ) [11][12] 1-7 7

13 [13] 1-8 Mirosot Specialized intelligence (Basic behaviors) (Move)(Obstacle avoidance) (Basic actions) Angle Position Advanced actions (Shoot) (Position-shoot) (Intercept-ball) (Sweep-ball) (Block) (Central controller) (Velocity controller) 8

14 1-9 MCU CPU 1-10 CPU CPU (1-10) 9

15 (Omnidirectional Robot) [14][15] 1-11 [16] MCU - PI PID MCU PLL PLL [17] 2 10

16 418MHz 433MHz MHz 433MHz Motorola MC2831 RFM HX2000 Ohm T100, MC3356 RX2010 N100 [19] Ri Ri Ri 1.3 RoboCup FIRA Angle Ka Position RunArc RunArc 11

2.1 2-1 2-1 R2 R2 2-1 2-1 R1 R2 R3 ER1 ER2 ER3 Ball X(cm) 75 60 12

17 R2 R R1 R2 R3 ER1 ER2 ER3 Ball X(cm) Y(cm) (deg) , 12

18 ) 4) 5) 6) 2) 3) 2) 3) 1) 4) 5) 6)

19 2-3 Robot1 Robot2 2-3 [18] A1( ) A2 A3 A4 A5 14

20 20ms A3 A1 A4 A5 A

21 d i ( i = 1 2) θ ( i = 1,2) ( i = 1,2) k i i A = + 1 k1d1 k2θ 1 A + 2 = k1d 2 k2θ 2 Y A1<A2? N Y N ( i = 1 2) d i α ( i = 1,2) β w = 2 i 2 w and d < and α β < 15 or α 180 β d < Robot2 2 Robot

22 2.2.2 angle position shoot block sweep pass pass-shoot beat one) crossing-covering (two 2-8 InitRole() AddiShoot(),Attack() My_Strategy() Strategy() MyGoalie(), MyGoaliePosition() RoleSelect(), RoleSelect2(), CheckOp(), AreaPartition() ShootAt(),Kick() PositionEx(),shoot() Position() Auto_Robot_Position(), GoToPosition(), Rush(), Rush2() AvoidFZone(),AvoidSticked() AvoidBound(),AvoidBoundEx() Angle(), AngleEx() Velocity() StopRobot(),WhichRobotStop() StopProc()

23 V V R ICR L L R 2-9 V R + V V = L (2.1) 2 VR VL ω = (2.2) L V ω ( x, y, θ ) V dx = V cosθ (2.3) dt dy = V sinθ (2.4) dt dθ 180 = ω (2.5) dt π (2.1),(2.2) dx 0 dy dt cosθ 1 0 = d dt sinθ πl 2 θ dt cosθ V sinθ V 1 L R V L R (2.6) 18

24 2.3.2 angle position angle V L = V V = V position R L R VL VR 2-10 d d θ (2.7) (2.8) k d V = k d k (2.7) L d aθ V = k d + k (2.8) R d aθ ka (2.7) (2.8) (2.1) (2.2) V = k d d (2.9) ω 2k a θ = (2.10) L d θ d 2-10 d (Angle) whichrobot desired_angle ka AngleOfHomeRobot[whichrobot] Velocity(whichrobot, vl, vr) void Angle(int whichrobot, int desired_angle, double ka) 19

25 { int theta_e = 0, vl, vr; theta_e = desired_angle-angleofhomerobot[whichrobot]; while(theta_e > 180) while(theta_e < -180) theta_e += 360; if (theta_e >= 90) { } theta_e -= 180; if (theta_e < -90) { } theta_e += 180; theta_e -= 360; // // // vl = int(-ka*theta_e); vr = int(ka*theta_e); Velocity(whichrobot, vl, vr); } StopProc(whichrobot); ka ka ka 2 Position whichrobot x, y PositionOfHomeRobot[whichrobot][0] PositionOfHomeRobot[whichrobot][1] void Position(int whichrobot, double x, double y) { int desired_angle=0, theta_e = 0, vl, vr,; double dx, dy, d_e, Ka=0.2,Kd = 1.5 ; // ka, kd dx=x-positionofhomerobot[whichrobot][0]; dy=y-positionofhomerobot[whichrobot][1]; d_e=sqrt(dx*dx+dy*dy); if (d_e > 20.0) // d_e = 20.0; if (d_e < -20.0) d_e = -20.0; desired_angle = (int)(180./m_pi*atan2((double)(dy),(double)(dx))); theta_e = desired_angle - AngleOfHomeRobot[whichrobot]; while(theta_e > 180) theta_e -= 360; 20

26 while(theta_e < -180) theta_e += 360; { } if (theta_e >= 90) theta_e -= 180; d_e = -d_e; if (theta_e < -90) { } theta_e += 180; d_e = -d_e; if(fabs(d_e)<1.0) { d_e=0.0; } if(fabs(theta_e)<=1) { theta_e=0; } // } vl = int(kd*d_e - Ka*theta_e); vr = int(kd*d_e + Ka*theta_e); Velocity(whichrobot, vl, vr); (2.7),(2.8) Ka Kd Position 21

27 3.1 Angle Ka whichrobot desired_angle Ka whichrobot θ 3-1 θe = desired _ angle θ θ e Vl = Ka θ Vr = Vl * e 3-1 Angle 3-1 θ θ -90 e e θ e e

28 3.1.1 (Angle) θ 3-1 θ, θ θ t), 0 = ( t= 0 2k m = L e t k a θ ω = L dθ ω = dt 3.1 dθ 2k = aθ dt L 3.2 dθ 2k = θ L a dt 2k d(ln θ ) = d ( a t) L θ = θ 3.3 a 0 e mt θ (t) θ t 23

29 3.4 e mt θ = θ 0 cos( Ωt) Ka Ka Ka Ka Ka Ka θ e Ka 1 90 Ka=0.1, 0.2, 0.3,, 0.1 Ka Ka 2 90 Ka 90 Ka Ka Ka θ = 90 Ka θ t e e 3-4 Ka=0.1 24

30 3-5 Ka= Ka=0.2 25

31 3-7 Ka= Ka=0.1, 0.2, 0.3 Ka=0.1 Ka=0.2 Ka= T T Ka= y=0, Ka=0.3 Ka= Ka 0.2 θ = 80,70,...,10 Ka=0.2 e θ = 60 θ = θ e = e e 26

32 3-8 θ = 60 θ e t Ka=0.2 e 30 θ e = θ e θ = 30 θ e t Ka=0.2 e θ 5 θ = 80,70,...,10 Ka=0.2 e Angle e Ka

33 3.2 Position Position Position whichrobot x, y whichrobot 3-10 e d θe = desired _ angle θ θ e d Vl = Kd * d Ka* θ e Vr = Kd * d + Ka * θ e 3-10 Position 3-10 θ angle d d d e d d if (d > 20.0) d = 20.0; if (d < -20.0) d = -20.0; d d position 1 Ka Kd Kd 2.9 Ka x, y t t Kd 3 d Ka Kd Ka Kd 28

34 V V L R L R VL = V L R R + 2 R L V V L R V = k d k (3.6) L d aθ V = k d + k (3.7) R d aθ L d k a = k d 3.8 2R θ (3.8) k a d θ t k (3.8) a (3.8) L V L = ( 1 ) kd d 2R (3.10) L V R = ( 1+ ) kd d 2R (3.11) (3.10) (3.11) x2,y2 29 k a

35 x1,y1 20T, α = 2γ + β (3.13) (RunArc) k k = β = 90 (x1,y1)=(108,20) (x2,y2)=(75,65) d 1 x2 y2 d T x2 y2 k d 30

36 3-13 x (Kd=1.963) 3-14 y (Kd=1.963) 31

37 3-15 (Kd=1.963) 2 Position 3-16 Position 3-16 Position 3-17 Position 32

38 3-17 (Kd=1.963) k = 90 d 3-18 β = ( ) ( x, y = 108,2 ) ( ) ( x, y = 75,65 ) x y 3-18 x (Kd=1.571) 33

39 3-19 y (Kd=1.571) k 52T 45T k d d 3.3 RunArc RunArc RunArc RunArc 34

40 3-20 IniPnt (MidPnt) ExpPnt α 3-21 A B C D AO α + β = 90 θ + β = 90 α = θ ABCD α = = (3.14) 35

41 R 3-21 d AC d R = (3.15) AB BC 3 φ 2sinθ (3-14) AO (3-15) R ϕ = φ + 90 (3.16) A ( x y ) x, y ) O, A, A ( O xo xa Rsinϕ = + (3.17) yo y A Rcosϕ φ (3-16) (3-17) ( x y ) O, O

42 k k= ) x, y ( B B ,71, 33,61, 38,56, 48,46, 53,41, 58,36, 63,31, 68, * cm 4cm 7.5cm ( x, y ) = (25,15) ( x, y ) = (75,65) A A C C 37

3-23 * 3-2 3-2 cm (cm) (cm) 1 (20, 66) (64, 79) (56, 20) 2 (84, 25) (66,

43 3-23 * cm (cm) (cm) 1 (20, 66) (64, 79) (56, 20) 2 (84, 25) (66, 82) (28,110) 3 (91, 66) (22, 82) (32, 45) 4 (27, 27) (109, 32) (129, 84) 38

44 * * 39

45 * * 3cm 4cm 7.5cm 40

46 H1 O H1 O H

47 H1 O1 H3 H1 H3 O1 H1 O1 45º 42

48 Mirosot Angle Position Ka Position 1 3 RGB RGB YUV 2 (Action Set) 3 43

49 [1] J.-H. Kim, P. Vadakkepat, Multi-Agent Systems: A Survey from the Robot Soccer Perspective, Int. J. Intelligent Automation and Software Computing, 6:(1)3-17, [2] A. K. Mackworth, On Seeing Robots, in A. Basu and X. Li, editors, Computer Vision: Systems, Theory, and Applications, pp.1-13, World Scientific Press, Singapore, [3] [4] [5] [6] Agent [7] [8] K.-H. Kim, K.-W. Ko, J.-G. Kim, S.-H. Lee, H.-S. Cho, The Development of a Micro Robot System for Robot Soccer Game, Proceedings of the 1997 IEEE International Conference on Robotics and Automation, 1997, pp [9] p1-3. [10] R. Sargent, B. Bailey, C. Witty, A. Wright, The Importance of Fast Vision in Winning the First Micro-Robot World Cup Soccer Tournament, Robotics and Autonomous Systems, Volume 21, 1997, pp [11] [12] [13] [14] J.-H. Kim, H.-S. Shim, H.-S. Kim, M.-J. Jung, I.-H. Choi and J.-O. Kim A Cooperative Multi-Agent System and Its Real-Time Application to Robot Soccer, Proc of IEEE Int. Conf. On Robotics and Automation, New Mexico, April 1997, pp [15] R. L. Williams II, B. E. Carter, P. Gallina, and G. Rosati, Dynamic Model With Slip for Wheeled Omnidirectional Robots, IEEE Transactions On Robotics and Automation, Vol. 18, No. 3, June 2002, pp [16] K. Watanabe, Control of an Omnidirectional Mobile Robot, International Conference on Knowledge-Based Intelligent Electronic Systems, April 1998, pp [17] [18] [19] [20]

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(Pattern Recognition) 1 1. CCD

********************************* ********************************* (Pattern Recognition) 1 1. CCD 2. 3. 4. 1 ABSTRACT KeywordsMachine Vision, Real Time Inspection, Image Processing The purpose of this