Self‐Organized Fission‐Fusion Control Algorithm for Flocking Systems Based on Intermittent Selective Interaction

Abstract

In nature, gregarious animals, insects, or bacteria usually exhibit paradoxical behaviors in the form of group fission and fusion, which exerts an important influence on group's pattern formation, information transfer, and epidemiology. However, the fissionfusion dynamics have received little attention compared to other flocking behavior. In this paper, an intermittent selective interaction based control algorithm for the self-organized fission-fusion behavior of flocking system is proposed, which bridges the gap between the two conflicting behaviors in a unified fashion. Specifically, a hybrid velocity coordination strategy that includes both the egalitarian and selective interactions is proposed, where the egalitarian interaction is to maintain the flock's order and achieve the fusion behavior while the selective interaction strategy is for the response to external stimulus information and generates the fission behavior. Numerical simulations demonstrate that the proposed control algorithm can realize the self-organized fissionfusion behavior of flocking system under a unified framework. The influences of the main control parameters on the performance of the fission-fusion behavior are also discussed. In particular, the trade-off parameter balances the exploration (fission) and exploitation (fusion) behaviors of flocking system and significantly enhances its movement flexibility and environmental adaptivity.

Bio-Inspired Cooperative Control Scheme of Obstacle Avoidance for UUV Swarm

“…In nature, biological collectives can exhibit rather complex functionalities through simple self-organizing behaviors [33]. Based on this understanding, a bio-inspired control strategy is proposed.…”

“…The self-organized fission/fusion method (SFF) [33] method and single-informedbased distributed consensus (SDC) [19] method were selected for obstacle avoidance simulation verification. The SFF method predicts the future state of neighbors by utilizing historical information and realizes the split and aggregation of the swarm by designing the clustering and splitting terms.…”

Bio-Inspired Fission–Fusion Control and Planning of Unmanned Aerial Vehicles Swarm Systems via Reinforcement Learning

“…Recently, researchers from diverse fields have increasingly recognized the significance of fission-fusion behaviors. They have successfully emulated these behaviors to achieve robot-controlled motion [12][13][14], enhance the efficiency of swarm resource search [15], and 2 of 20 accomplish planning objectives, such as task allocation and obstacle avoidance [1,16,17]. For example, Wang et al [13] incorporate the fission-fusion motion into the formation controller of an underwater vehicle, Nauta et al [15] study the population resource search with the fission-fusion concept, whereas Reséndiz-Benhumea et al [16] integrate the fissionfusion movements observed in ant colonies into the task assignment algorithm for robot swarms.…”

Formation Control Algorithm of Multi-UAVs Based on Alliance

“…Reference [23] designed control laws for two types of heterogeneous agents with different perceptual ranges, desired distances, and goals to achieve the purpose of the subgroup, but the method still needs to divide the group in advance. References [24][25][26] proposed the self-organized swarm control method based on the information coupling degree, but this type of method requires Drones 2022, 6, 431 2 of 16 individuals with certain memory capacity and only achieves the subgrouping behavior under symmetric external stimuli.…”