Young honeybees aggregate on optimal temperature zones in the hive using a simple set of behaviors. One of the fascinating examples of cue-based aggregation is the thermotactic behavior of young honeybees. The former being called the self-organized aggregation, and the latter being called the cue-based aggregation. The effectiveness of the proposed formation-containment control algorithm is demonstrated with both numerical simulations and experiments using real robots that utilize the miniature mobile robot, Mona.Īggregation, a widely observed behavior in social insects, is the gathering of individuals on any location or on a cue. In contrast to conventional consensus-based formation control methods, the relative formation reference signal is not required in the real-time data transmission, which provides greater feasibility for implementation on hardware platforms. The followers then converge into the convex hull formed by the leaders simultaneously. A novel control protocol and an implementation algorithm are proposed that enable the leaders to achieve the desired formation via semidefinite programming (SDP) techniques. The swarm system contains two types of robots- leaders and followers. In this paper, formation-containment controller design for single-integrator and double-integrator swarm robotic systems with input saturation is investigated. There are many potential applications of swarm robotic systems in real-world scenarios. According to the results, the proposed method realizes the aggregation behavior in a shorter time than the other method in all systematic simulations. Additionally, the proposed method is compared to a method in the literature in terms of aggregation completion time. The performance of the proposed aggregation method is statistically examined for different arena sizes, different detection limits and different numbers of robots. In the study, with swarm robots that have basic features, the aggregation method is applied in the simulation environment for different arena sizes, different numbers of robots and different detection distances. Each swarm robot can aggregate by decision-making only by itself without needing a control unit by using the proposed aggregation method. In the proposed method, a decision-making structure that determines robot movements for the swarm robots to show aggregation behavior is utilized. This study proposes an aggregation method based on flocking for self-organizing aggregation behavior in swarm robotics. The aggregation behavior shown by swarm robots to establish coordination among each other is a basic behavior that is used in swarm robotics. Also, our results showed that the increasing swarm size significantly improved the performance of the group. Moreover, we analyzed our results using a one-way analysis of variance. The experimental results showed that the aggregation time in the proposed algorithm was significantly reduced by 41% compared to other algorithms in the literature. The execution time of the proposed algorithm was analyzed. In this work, the aggregation algorithm consists of two main phases: the searching phase, and the surrounding phase. This algorithm combined with the wave algorithm to achieve collective navigation and the recruitment strategy. This paper proposed a new aggregation algorithm. These difficulties are as such: this algorithm has to work under the restrictions of no information about positions, no central control, and only local information interaction among robots. Many difficulties are facing the aggregation algorithm. Aggregation is crucial behavior when performing complex tasks in swarm robotics systems. Limited capabilities of sensing and communication by simple mobile robots have been essential inspirations for aggregation tasks. Swarm robotics carries out complex tasks beyond the power of simple individual robots.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |