Abstract
Robotics control system with leader-follower approach has a weakness in the case of formation failure if the leader robot fails. To overcome such problem, this paper proposes the formation control using Interval Type-2-Fuzzy Logic controller (IT2FLC). To validate the performance of the controller, simulations were performed with various environmental systems such as open spaces, complexes, circles and ovals with several parameters. The performance of IT2FLC will be compared with Type-1 Fuzzy Logic (T1FL) and Proportional Integral and Derivative (PID) controller. As the results found using IT2FLC has advantages in environmental uncertainty, sensor imprecision and inaccurate actuator. Moreover, IT2FLC produce good performance compared to T1FLC and PID controller in the above environments, in terms of small data generated in the fuzzy process, the rapid response of the leader robot to avoid collisions and stable movements of the follower robot to follow the leader's posture to reach the target without a crash. Especially in some situations when a leader robot crashes or stops due to hardware failure, the follower robot still continue move to the target without a collision.
Highlights
Formation control of multiple autonomous mobile robots a has been studied extensively and it became a challenging topic among multi-robot research issues [1]
Formation control is defined as the coordination of the group of robots that maintain a formation within specified geometrical shapes as an obstacle
The formation control has been expected to be employed in various applications such as manipulation of large objects [4], [5], intelligent highway systems [6], [7], surface vehicle formation [8], flight formation systems [9], [10], formation of multiple spacecraft [11] and surveillance systems [12], [13], machine vision [14]
Summary
Formation control of multiple autonomous mobile robots a has been studied extensively and it became a challenging topic among multi-robot research issues [1]. This is because there are many potential advantages of such systems over a single robot, including greater flexibility, adaptability to unknown environments and robustness [2], [3]. By behavior-based approach, several desired behaviors are prescribed for each robot, and the final action of each robot is derived by weighting the relative importance of each behavior [15], [16] The limitation of such approach, it is difficult to analyze mathematically, it is hard to guarantee a precise formation control. The formation control problem can be seen as a natural extension of the traditional trajectory-tracking problem
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