###### Abstract
Typical swarm controllers incorporate fixed-neighbor distance interactions for fission-fusion motions [1; 22; 29; 30; 31]. However, this interaction manner imposes a communication load that increases with the swarm size, making it unfeasible for large-scale swarms [25; 29; 30; 31]. In practice, large-scale swarms are usually achieved through limited interactions among a subset of individuals, enabling the formation of expansive swarms [32; 33; 34]. Notably, studies have shown that each starling in a flock interacts with only six to seven of its closest neighbors, allowing for the swarm formation comprising thousands of individuals through topological interaction structures [32]. As such, numerous researchers have investigated the starling topology and its utilization in swarms [35; 36; 37]. However, in the context of UAV swarms, the reliance on a seven-nearest-neighbor topological interaction model often leads to local convergence, as shown in Figure 1. It still remains a problem of the local convergence to maintain swarm integrity while reducing the communication load among UAV swarm members. Based on the above discussion, this paper focuses on investigating the bio-inspired fission-fusion control and planning of UAV swarm systems by enhancing the starling topology and incorporating a reinforcement learning algorithm. The proposed approach, referred to as the Bio-inspired Fission-Fusion Control and Planning via Reinforcement Learning (BiFRL), encompasses several key contributions. Firstly, we introduce a probabilistic starling-inspired topological interaction (PSTI) structure, which deviates from the traditional fixed-range neighbor interactions in swarm motion while effectively reducing the communication load and likelihood of local convergence. Next, a self-organized fission-fusion control framework and a fission decision algorithm are developed, which enables the swarm to autonomously divide into sub-swarms when encountering dynamic obstacles. It allows for precise control of sub-swarm units. Finally, we propose a sub-swarm confrontation via reinforcement learning (SCRL), which can efficiently plan confrontation movement with minimal energy loss through reinforcement learning techniques. This characteristic facilitates the original objectives of the parent swarm remaining unaffected by dynamic obstacles, while the sub-swarms can seamlessly reintegrate into the parent swarm once the interference ceases. Finally, the effectiveness and robustness of the proposed BiFRL algorithm for UAV swarm are validated through simulation models that consider dynamic obstacles approaching from different directions. The present study provides the following contributions:  
* We introduced a probabilistic starling-inspired topological interaction structure, which effectively reduces the communication load and likelihood of local convergence. * A self-organized fission-fusion control framework for UAV swarms is presented that extends the existing social force model, where a fission decision algorithm is designed to manipulate the composition of sub-swarms and target the dynamic obstacles. * We developed a reinforcement learning sub-swarm confrontation algorithm to achieve a self-organized sub-swarm against dynamic obstacles in unknown environments, which significantly improves the adaptability of UAV swarms. * The proposed method's feasibility and validity are demonstrated through extensive numerical simulations, accompanied by the development of several numerical evaluation indicators. The subsequent sections of this paper are structured as follows: In Section 2, we present the formulation of the problems and review of existing models pertaining to fission-fusion control and planning in UAV swarms. Section 3 presents the proposed BiFRL framework and its components in detail. We then conduct the simulation experiments to analyze the proposed method in Section 4. Finally, the findings and conclusions of this study are presented in Section 5.