## 3 Bio-Inspired Fission-Fusion Control and Planning via Reinforcement Learning Algorithm
### Sub-Swarm Confrontation via Reinforcement Learning
Figure 6a showcases the whole process of fission-fusion motion of the UAV swarm when encountering dynamic obstacles. This motion is achieved through our proposed BiFRL algorithm. The sub-swarm successfully executes the confrontation movement and seamlessly integrates back into the parent swarm upon completion. The point of origin for the dynamic obstacle is (40, 60, 30). When the swarm perceives the presence of dynamic obstacles, it initiates a self-organizing fission into two swarms, which include a parent swarm and a sub-swarm. The two swarms swiftly synchronize their positions. During the confrontation process, the sub-swarm effectively confronts the interference from dynamic obstacles utilizing the SFCRL algorithm. Finally, following the resolution of the sub-swarm's engagement, it autonomously converges back into the parent swarm, fusion into a stable and cohesive swarm. Figure 6: Self-organized fission–fusion process of UAV swarm. (**a**) indicates the complete UAV swarm fission–fusion movement, while (**b–f**) denotes different steps within the fission–fusion process. Figure 6b illustrates random initial spots of all agents. Figure 6c demonstrates the rapid formation of a stable swarm by intelligent agents under random initial spots. In Figure 6d, the swarm perceives the obstacle, leading to its self-organization and fission into two state-stable swarms. The confrontation motion against dynamic obstacles is executed based on the SFCRL algorithm. Figure 6e exhibits the sub-swarm completing its confrontation movement and initiating its return to the parent swarm after the dynamic obstacle stops tracking. Lastly, Figure 6f illustrates the self-organization of the sub-swarm as it returns to the parent swarm following the conclusion of the antagonistic movement, culminating in a fusion. 3.4 Evaluation of the Bio-Inspired Fission-Fusion Control and Planning via Reinforcement Learning Algorithm

Figure 7 depicts the temporal evolution of the polarization index of the UAV swarm combined with reinforcement learning. The polarization indices in Figure 7 demonstrate that the entire swarm successfully completes the swarming within 10 s. After the fission campaign, both swarms have strong robustness. Upon the sub-swarm's return to the parent swarm, there is a brief decrease in the polarization index, followed by stabilization around 1. Notably, the parent swarm is unaffected by the dynamic obstacle throughout the entire process. Figure 8 illustrates the evolution of the differentiation index in the swarm, integrated with reinforcement learning. The differentiation index showed that 20 randomly generated UAVs rapidly established stable swarms within the first 0-3 s of the initial, which persists until approxi

Figure 8: Differentiation index of the UAV swarm. Figure 7: Polarization index of the UAV swarm. mately 24 s. At around 27 s, the sub-swarm undergoes rapid fission, forming two stable sub-swarms, while maintaining a constant differentiation index of approximately 1. Subsequently, at approximately 92 s, after completing the confrontation and returning to the parent swarm, it seamlessly fuses back into a stable swarm, thereby validating the effectiveness of our proposed algorithm. Figure 9 illustrates the precision of stimuli exhibited by both swarms under dynamic obstacle interference. Both swarms demonstrate commendable precision of response stimuli throughout the fission-fusion process. The precision of stimuli of the sub-swarm, influenced by the SFCRL algorithm, exhibits some variability; however, it consistently demonstrates a high level of accuracy. In contrast, the parent swarm experiences slight fluctuations in response stimuli when dynamic obstacles are detected. These fluctuations primarily arise from the need for the parent swarm to stabilize into a new formation during the fission-fusion process and are not directly caused by the dynamic obstacles themselves. Furthermore, it is observed that the obstacles do not significantly impact the parent swarm during the remaining period duration.