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Southern Taiwan University PSO-based Fuzzy Controller Design for Robot Soccer Department of Electrical Engineering, Southern Taiwan University, Tainan, R.O.C Juing-Shian Chiou, Chi-Jo Wang, Shih-Wen Cheng, Kuo-Yang Wang, and Yu-Chia Hu 1 Southern Taiwan University Outline Abstract Introduction Motion Fuzzy Controller Structure Particle Swarm Optimization algorithm Simulation Results Conclusion 2 Southern Taiwan University Abstract This paper will use the algorithm based on fuzzy to combine with particle swarm algorithm, applying to the mobile robot’s obstacle avoidance, determine the fuzzy algorithm and Particle Swarm Optimization (PSO) to design the optimal route and speed. In this paper, we will use this algorithm in five-versus-five simulation platform, it knows whether the combination of these algorithms can be quickly and accurately to achieve our objectives. 3 Southern Taiwan University Introduction(1/3) we propose the application of this algorithm - fuzzy particle swarm algorithm, the advantage of PSO, convergence time is quicker than others and easy to modify, the above features are very special in the algorithm. In this experimental platform, we choose the robot Football Association 5-vs-5 simulation platform in Figure 1 Figure 1. The Five-versus-Five simulation platform 4 Southern Taiwan University Introduction(2/3) Using to cluster features for the particle swarm, to find out how to avoid obstacles in the move, at the same time moving towards the destination path planning, and this focus on how to quickly take the lead particles are individual optimal solution, also obtained group optimal solution, show in Figure 2. 5 Southern Taiwan University Introduction(3/3) Set of fuzzy rule. Fuzzy controller of robot wheels Speed. Evaluate each particle's fitness function. Records of individual particles and groups of the best memories. Update the particle position and velocity. No Terminating condition. Figure 2. System structure. Yes END 6 Southern Taiwan University Motion Fuzzy Controller Structure(1/7) In this part, we start design the fuzzy logic controller aimed at producing the velocities of the robot right and left wheel. We set two input parameters of the fuzzy logic controller are distance d and angle . The former d is the distance between the robot and the goal. The latter is the direction of with on the straight line path to the goal. Both are shown in Figure 3. Figure 3. the relation of d and 7 Southern Taiwan University Motion Fuzzy Controller Structure(2/7) We set the values of variable e1 , e2 , e3 , e4 , v1 , v2 , y1 , y 2 and design two fuzzy controllers to control the velocity of the right and left wheels to move the robot. The fuzzy rules on which were based these fuzzy controllers are described in tables 1 and 2, and can be described according to the following equations: Ry1 j1 , j2 : IF e1 is A1, j1 And e2 is A 2, j2 Then y1 is y1 j1 , j2 j1, j2 3, 2, 1,0,1,2,3 (1) Ry2 j3 , j4 : IF e3 is 3, j1 And e4 is 4, j2 A A Then y2 is y2 j3 , j4 j3 , j4 3, 2, 1,0,1,2,3 (2) 8 Southern Taiwan University Motion Fuzzy Controller Structure(3/7) Table 1. Fuzzy rule base of the left- Table 2. Fuzzy rule base of the right- wheel velocity fuzzy controller wheel velocity controller 9 Southern Taiwan University Motion Fuzzy Controller Structure(4/7) The following term sets were used to describe the fuzzy sets of each input and output fuzzy variables: T ei NB, NM , NS , Z , PS , PM , PB , i 1,2,3,4 Ai ,3 , Ai ,2 , Ai ,1 , Ai ,0 , Ai ,1 , Ai ,2 , Ai ,3 , (3) T ym NB, NM , NS , Z , PS , PM , PB , m 1,2 y m,3 , y m,2 , y m,1 , y m,0 , y m,1 , y m,2 , y m,3 , (4) 10 Southern Taiwan University Motion Fuzzy Controller Structure(5/7) As show in figure 4, the triangle membership function and the singleton membership function are used to describe the fuzzy sets of input variables and output variables. NB NM NS Z PS PM PB NB NM NS Z PS PM PB 0 10 20 30 40 50 60 (inch) 90 60 30 0 30 60 90 (a) (b) Figure 4. Membership function: (a) the fuzzy sets for ei ; (b) the fuzzy sets for ym . 11 Southern Taiwan University Motion Fuzzy Controller Structure(6/7) Based on the weighted average method, the final output of these fuzzy controllers can be described by means of equation (5) and (6) 3 3 3 3 y1 w j1 3 j2 3 j1 , j2 y1 j1 , j2 (5) y2 w j3 3 j4 3 j3 , j4 y1 j3 , j4 (6) Where w j1 , j2 and w j3 , j4 were determined according to Equations (7) and (8). w j1 , j2 min u A1, j e1 , u A2, j e2 1 2 (7) w j3 , j4 min u A3, j e3 , u A 4, j e4 3 4 (8) min u e , u e2 min u e3 , u A e4 3 3 3 3 A1, j 1 A 2, j A 3, j 4, j4 1 2 3 j1 3 j2 3 j1 3 j2 3 12 Southern Taiwan University Motion Fuzzy Controller Structure(7/7) When the input data of e1 , e2 , e3 and e4 are given, y1 and y2 can be determined by using Equations (5) and (6) Thus, the left-wheel velocity vl and the right-wheel velocity vr can be obtained. 13 Southern Taiwan University Particle Swarm Optimization algorithm (1/3) Initially the group is based on the flock-based mobile way, there are based on particles, the first, particles will be randomly distributed in space, each particle has own optimal solution, also to the group's information to determine the optimal location of the next movement and speed, the optimal by different individuals, repeat implementation to find the overall optimization, after iterative calculation method, to achieve the optimization goal. In the particle swarm system in the simultaneous existence of individual optimal value pbesti and the group optimal value gbesti ， it will use the robot to avoid obstacles function, the schematic diagram of pbesti and gbesti below. 14 Southern Taiwan University Particle Swarm Optimization algorithm (2/3) v wv c rand () ( gbest s ) k 1 i k i 1 k* i k i (9) c rand () ( pbest s ) # k k 2 i i (10) s k 1 i s vk i k 1 i According to the above function, determining the velocity and position, the maximum speed limit vmax for each particle, and the maximum distance limit smax ，When the speed limit and greater limit than distance, The speed and distance will be defined as vmax or smax 。 15 Southern Taiwan University Particle Swarm Optimization algorithm (3/3) Figure 5. Particle velocity and position graph 16 Southern Taiwan University Simulation Results(1/2) We use the particle swarm algorithm to simulate the path and the avoidance function. (a) (b) Figure 6(a)(b). Use the PSO to modify the fuzzy rule, the robot to achieve faster 17 Southern Taiwan University Simulation Results(2/2) (a) (b) Figure 7(a)(b). While planning a path ahead to avoid obstacles in the movement 18 Southern Taiwan University Conclusion In this experiment, we use particle swarm algorithm to avoid obstacles, at the same time toward the destination, and through the particle swarm faster convergence to obtain the optimal solution, can achieve the path planning objects quickly, at the same time as change with the environment, and immediately change its pre-determined parameters, PSO is easy to change, the platform is also very easy to operate. 19 Southern Taiwan University Thanks for your attention ! 20

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posted: | 3/31/2013 |

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