###### Abstract  
Swarm control of unmanned aerial vehicles (UAV) has emerged as a challenging research area, primarily attributed to the presence of conflicting behaviors among individual UAVs and the influence of external movement disturbances of UAV swarms. However, limited attention has been drawn to addressing the fission-fusion motion of UAV swarms for unknown dynamic obstacles, as opposed to static ones. A Bio-inspired Fission-Fusion control and planning via Reinforcement Learning (BiFRL) algorithm for the UAV swarm system is presented, which tackles the problem of fission-fusion behavior in the presence of dynamic obstacles with homing capabilities. Firstly, we found the kinematics models for the UAV and swarm controller, and then we proposed a probabilistic starling-inspired topological interaction that achieves reduced overhead communication and faster local convergence. Next, we develop a self-organized fission-fusion control framework and a fission decision algorithm. When dealing with various situations, the swarm can autonomously re-configure itself by fissioning an optimal number of agents to fulfill the corresponding tasks. Finally, we design a sub-swarm confrontation algorithm for path planning optimized by reinforcement learning, where the sub-swarm can engage in encounters with dynamic obstacles while minimizing energy expenditure. Simulation experiments demonstrate the capability of the UAV swarm system to accomplish self-organized fission-fusion control and planning under different interference scenarios. Moreover, the proposed BiFRL algorithm successfully handles adversarial motion with dynamic obstacles and effectively safeguards the parent swarm. U  
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accomplish planning objectives, such as task allocation and obstacle avoidance [1; 16; 17]. For example, [PERSON] et al. [13] incorporate the fission-fusion motion into the formation controller of an underwater vehicle, [PERSON] et al. [15] study the population resource search with the fission-fusion concept, whereas [PERSON] et al. [16] integrate the fission-fusion movements observed in ant colonies into the task assignment algorithm for robot swarms. As such, the investigation of fission-fusion behavior in swarm systems holds substantial theoretical research and practical application values. Swarm fission-fusion behavior involves the ability of individuals within a certain range to gather into a cohesive group with synchronized movement through specific interactions. [PERSON] et al. [18] propose three fundamental behavioral rules to address this issue: collision avoidance, speed consistency, and mutual aggregation. Subsequently, extensive research has been conducted based on these rules for implementing swarm controls [19; 20; 21] and exploring swarm fission-fusion dynamics [17; 22; 23; 24]. Nevertheless, most of these studies primarily focus on collision avoidance for static obstacles within a single swarm or during the fission-fusion process in a known environment with complete knowledge. There has been limited research on swarm planning in the presence of unknown dynamic obstacles, particularly of those with tracking capabilities. This is primarily because existing path planning strategies for static or dynamic obstacles often rely on global environment information [25; 26; 27; 28]. For instance, [PERSON] et al. [26] perform path planning through a sampling-based algorithm, while [PERSON] et al. [27] propose to address the continuous space subproblems and integrate the discrete and continuous aspects of the search process to achieve path planning objectives. In the real world, however, many dynamic obstacles remain unknown or possess tracking capabilities, continually intruding upon and pursuing the targeted swarm to disrupt its progress. Previous methods [22; 23; 24; 25] for swarm control and planning may be significantly affected by limited success rate and resource utilization in reaching the intended destination.