Swarm robot formation control research has been implemented in various fields. One of the applications is monitoring and mapping the environmental pollution level by using a remote sensing system. In localizing pollution sources, swarm robots must have ability to cooperate among them. In their collaboration, they must be able to communicate and be able to avoid obstacles and collisions. Therefore, for controlling the swarm robot movement, it is needed to set the formation, so that the performance can be improved.
To improve performance in the durability and efficiency of formation control on a swarm robot, it is necessary to choose the right formation strategy. The swarm robot must have ability to coordinate and cooperate. The choice of strategy type in controlling the swarm robot formation using basic behavior (behavior-based), depends on some factors, such as: (i) reaction behavior between the robot and the environment; (ii) behavior among robots; and (iii) robot behavior with obstacles. In swarm robot control with basic behavioral strategies, the ability that must be possessed by individual robots is to avoid obstacles between robots and obstacles, and to maintain formation during movement.
Maintaining formation control during avoiding obstacles and avoiding collisions between the robots when they perform the tasks in unknown and uncertain environments are difficult to achieve. Therefore, a soft computational technique is needed. The Interval Type-2 Fuzzy Logic System (IT2FLS) algorithm is used. This algorithm can be used to deal with uncertainty problems that arise as long as the robot completes its tasks in the real environment.
The problem that often occurs in the use of IT2FLS algorithm is the need of large memory requirement. This need will affect the overall performance of the swarm robot. However, in this dissertation, the individual specifications of the swarm robot used are simple robots with limited abilities, however, the swarm robot can control the formation during its movement. All of this can be achieved by modifying the IT2FLS algorithm that is embedded in individual robots and by choosing the right strategy that can improve the formation performance.
The above statement is the contribution of this dissertation. In this dissertation, the formation control on the swarm robot is carried out using a behavior based strategy. the swarm robot used in this study consists of simple individuals with low-cost microcontrollers, and has low power as well. However, researchers were able to embed the IT2FLS controller algorithm into the individual of robot swarm. From the experiments, it can be proved that it can maintain swarm robot formation.
The individual mobile robots produced in this study have good performance and endurance capabilities in maintaining the formation. The swarm robot is able to maintain the stability of the formation control by determining the direction of rotation and speed of each individual robot. This proves that the swarm robot formation control can run well by using behavior based strategies or basic robot behavior.
The formation control testing in order to see the performance of swarm robots that work based on behavior based strategies, was conducted by comparing the movement of the robot using the T1FLS embedded controller with the movement of the robot using the IT2FLS embedded controller, either in the indoor or outdoor. The test was carried out for several conditions, such as: (i) indppr environmental conditions with obstacles and without obstacles; (ii) outdoor environmental conditions with obstacles and without obstacles, and (iii) environments with obstacles in motion. The test results show that the IT2FLS controller produces better performance than T1FLS, i.e., with shorter track length, and more stable formation control.
Study Program Of Doctoral Engineering, Electrical Department, Sriwijaya University