1. 1 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 and angle. The former 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 and

2. 2 Motion Fuzzy Controller Structure(2/7) We set the values of variable,,,,,,, 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: Then (1) Then (2)

3. 3 Motion Fuzzy Controller Structure(3/7) Table 1. Fuzzy rule base of the left- wheel velocity fuzzy controller Table 2. Fuzzy rule base of the right- wheel velocity controller

4. 4 Motion Fuzzy Controller Structure(4/7) The following term sets were used to describe the fuzzy sets of each input and output fuzzy variables: (3) (4)

5. 5 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. (a) (b) Figure 4. Membership function: (a) the fuzzy sets for ; (b) the fuzzy sets for.

6. 6 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) Where and were determined according to Equations (7) and (8). (5)(6) (7)(8)

7. 7 Motion Fuzzy Controller Structure(7/7) When the input data of and are given, and can be determined by using Equations (5) and (6) Thus, the left-wheel velocity and the right-wheel velocity can be obtained.

8. 8 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 and the group optimal value ， it will use the robot to avoid obstacles function, the schematic diagram of and below.

9. 9 Particle Swarm Optimization algorithm (2/3) (9) (10) According to the above function, determining the velocity and position, the maximum speed limit for each particle, and the maximum distance limit ， When the speed limit and greater limit than distance, The speed and distance will be defined as or 。

10. 10 Particle Swarm Optimization algorithm (3/3) Figure 5. Particle velocity and position graph

11. 11 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

12. 12 Simulation Results(2/2) (a) (b) Figure 7(a)(b). While planning a path ahead to avoid obstacles in the movement

13. 13 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.

14. 14 Thanks for your attention !