The Multitasking System of Swarm Robot based on Null-Space-Behavioral Control Combined with Fuzzy Logic
2. Building the Function of the Suction/Thrust Ford Based on Fuzzy Logic
- First step
- The input signal is , assume that the value domain of u is [αb, βb] ∈ R, divide this domain into 2Nf + 1 in the range Bk as shown in Figure 1.
- The output signal is A with the value domain [αa, βa], divide this value domain into 2Nf + 1 in the range Ak as shown in Figure 2 (k = 1, 2, …, 2Nf + 1). is the focus of the fuzzy range Ak:
- The second step: establishing 2Nf + 1 rule IF-THEN with the form: IF: , THEN:
- The third step: defuzzifier using the central area method, we have control laws as follows :
- Upper and lower limits:
- The equation of a part linearization:
3. The Stability of the System
- If increases, the thrust increases, the restricted area of the swarm robot increases.
- If increases, the restricted area of the swarm robot decreases.
- If the size of the swarm (N) is bigger, the restricted area is lower.
4. Multitasking-Control System of Swarm Robot
- The first task is avoiding obstacles
- The second task is moving to the destination.
- The third task is maintaining the swarm: Avoiding collisions among individuals in the swarm, but not splitting the group.
4.1. Determining the Speed Component Avoiding Obstacles
4.2. Determining the Speed Component Moving to the Target
4.3. Determining the Maintained-Swarm Speed Component
4.4. The Algorithm of Swarm Robot Control for Performing Multiple-Task
- The first step
- Enter the number (N) of robots in the swarm.
- Enter the number (M) of obstacles in the moving space.
- Initially the position of individual robots in n-dimensional space:
- Placement of M obstacles and the destination (g) in n-dimensional space:
- Enter the safe distance between the individual robot and obstacle , the safe distance among robot individuals
- Enter the coefficients and
- Enter the number of steps to calculate (K).
- The second step
- Calculating the distance between each robot (i) and each obstacle , the distance between each robot and target, the distance between robot (i) and robot (j).
- Calculating the suction/thrust force according to Equation (3), satisfying Condition (4).
- The third step
- Comparing the actual distance and safe distance from the robot (i) to the obstacle (m):
- If , the robot (i) does not need to avoid the obstacle (o), it means .
- If , the robot (i) needs to avoid the obstacle (o), calculating by Formula (26). Calculating , , .
- Comparing the actual distance and the desired distance from the robot (i) to the target:
- If , the robot (i) reached the target g, .
- If , the robot (i) has not reached the target, calculating by the Formula (24).
- Calculating ,,, calculating: , , .
- Comparing the actual distance and the desired distance from the robot (i) to the robot (j):
- If , the robot (i) and the robot (j) move towards each other by the suction force .
- If , the robot (i) and the robot (j) move away from each other by the thrust force .
- If , the robot (i) and the robot (j) keep their route because of .
- Calculating , , .
- The fourth step
- The speed of the individual (i) at the step k (k = 0 ÷ K − 1) is determined by the formula:
- The distance moved of the robot (i) in a step time():
- The new position of the robot (i) after k + 1 steps:
5. Simulation Results and Analysis
- If the size of the swarm (N) increases, the convergence radius decreases;
- If the safe distance increases, the convergence radius increases;
- The actual convergent radius (R) is always smaller than the calculated value (σ).
- If the coefficient is larger, the individual movement to the target is faster.
- If we want to increase the coefficient but not let the robot collide with obstacles, we must reduce the coefficient . This means, if is more positive, must be more negative.
- If the number of obstacles (M) is bigger, the avoid-obstacle-coefficient ( must be more negative. If the coefficient is more negative, the ability of the robots to avoid obstacles is better, but the moving time to the destination will be longer.
Conflicts of Interest
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Le Thi Thuy, N.; Nguyen Trong, T. The Multitasking System of Swarm Robot based on Null-Space-Behavioral Control Combined with Fuzzy Logic. Micromachines 2017, 8, 357. https://doi.org/10.3390/mi8120357
Le Thi Thuy N, Nguyen Trong T. The Multitasking System of Swarm Robot based on Null-Space-Behavioral Control Combined with Fuzzy Logic. Micromachines. 2017; 8(12):357. https://doi.org/10.3390/mi8120357Chicago/Turabian Style
Le Thi Thuy, Nga, and Thang Nguyen Trong. 2017. "The Multitasking System of Swarm Robot based on Null-Space-Behavioral Control Combined with Fuzzy Logic" Micromachines 8, no. 12: 357. https://doi.org/10.3390/mi8120357