## 3 Bio-Inspired Fission-Fusion Control and Planning via Reinforcement Learning Algorithm
### Sub-Swarm Confrontation via Reinforcement Learning
Figure 10 presents the dynamics of communication load in UAV swarms employing different interaction structures. In this study, we compare the communication load of the PSTI structure with various fixed-distance communication structures. The simulation results demonstrate that in the absence of fission-fusion, the probabilistic starling-inspired topological interaction achieves a significant reduction in communication load, ranging from 50% to 85% compared to other structures. Even during the fission-fusion process, our proposed interaction structure continues to exhibit advantages. Conversely, as the fixed distance interaction structure adopts smaller interactive distances, the likelihood of local convergence increases during the swarming process. Figure 10: Communication load comparison results. Figure 9: Precision of stimuli of the UAV swarm. ## 5 Conclusions

In recent years, researchers have shown significant interest in the UAV swarm, particularly its self-organized fission-fusion control methods. However, dealing with dynamic obstacles that possess tracking capabilities remains a challenge in UAV swarm control. As such, we present a bio-inspired fission-fusion control and planning of a UAV swarm system via reinforcement learning algorithm to tackle this issue. In contrast to existing methodologies, our approach is targeted at resolving the interference posed by unknown dynamic obstacles through the utilization of fewer resources. The proposed self-organized fission-fusion framework facilitates autonomous grouping and separation in response to dynamic obstacles while maintaining control over the size of the sub-swarm. Additionally, we introduce sub-swarm confrontation via reinforcement learning to handle the selection of confrontation paths in the presence of unknown disturbance directions. After completing the confrontation, the sub-swarm seamlessly integrates back into the parent swarm through self-organization. Furthermore, we propose a probabilistic starling-inspired topological interaction structure, which effectively mitigates the issue of swarm local convergence encountered by existing seven-nearest-neighbor algorithms. To validate the competitiveness of our approach, we conduct extensive simulations involving different swarm initial ranges and evaluate the communication load as a performance metric. The results demonstrate the effectiveness and feasibility of our proposed BiFRL algorithm, which combines reinforcement learning with UAV swarm fission-fusion control and planning to handle unknown dynamic obstacle disturbances. We believe that our proposed algorithm has a positive effect on improving the efficiency of swarm control in dynamic environments and the ability of clusters to combat dynamic disturbances. This has a positive effect on the research in the field of multi-swarm. However, the only resource terms we considered during the study are limited, and it is clear that we need to consider more resource terms, e.g., electromagnetic interference in the environment, leakage rate during recognition of dynamic obstacles, and other factors. These issues will make the existing dynamic environment more complex but more realistic. We will introduce more AI algorithms to try to solve such complex dynamic problems. This will, in turn, improve the robustness and applicability of the algorithms. We will perform in-depth sensitivity analyses for more complex environments to observe the robustness of the algorithms [39]. In future work, we will further investigate the interference of complex dynamic obstacles within the swarm fission-fusion approach and study the influence of specific parameters on the algorithm in greater depth. Conceptualization, X.Z. and W.D. ; Methodology, Y.W. and W.D. ; Software, X.Z. ; Validation, X.Z., Y.W. and J.J.; Formal analysis, W.D. ; Investigation, Q.W. ; Resources, J.J.; Writing--original draft, Q.W. and Z.Z. ; Writing--review & editing, Y.W. ; Visualization, Z.Z. ; Funding acquisition, W.D. All authors have read and agreed to the published version of the manuscript.