## 3 Bio-Inspired Fission-Fusion Control and Planning via Reinforcement Learning Algorithm
### Sub-Swarm Confrontation via Reinforcement Learning
When \(\Lambda=0\), it signifies a lack of interference as the motion directions are dissimilar. Conversely, when \(\Lambda=1\), it indicates severe interference, as the motion directions are entirely congruent. The definition is as follows:

\[\Lambda=\frac{A-A_{0}}{1-A_{0}}\ \ \ \ \ A_{0}=\frac{1+r_{o}r_{att}}{2}A=\frac{1}{N _{i}^{\omega}}\sum_{i=1}^{N^{\omega}}\frac{1+r_{i}r_{att}}{2} \tag{23}\]

where \(r_{i}\), \(r_{att}\) and \(r_{o}\), respectively signify the unit velocity directions of unmanned aerial vehicle \(i\), the swarm, and the dynamic obstacles. Communication Load. This term represents the average communication cost incurred by the UAV fleet, where a lower communication load indicates a reduced average communication cost. It is defined as follows:

\[N_{cl}=\frac{1}{N}||\sum_{i=1}^{N}{\sum_{j=1}^{N_{pl}^{\omega}}d_{ij}^{\omega}}|| \tag{24}\]where \(N_{il}^{\omega}\) represents the number of unmanned aerial vehicles interacting with unmanned aerial vehicle \(i\). ### Detailed Simulation Parameters

Tables 1 and 2 show the detailed simulation parameters of the experiments and RL-based path planning of the sub-swarm, respectively. It is worth noting that the purpose of the parameter list we have provided is in the hope that researchers will be able to reproduce the methodology of this study more quickly. In fact, the parameters in the table can be modified according to different research situations, which does not affect the validity of the proposed algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Numerical Value** \\ \hline \(\gamma^{ine}\) & Inertia coefficient & 0.75 \\ \(\xi\) & Environmental damping factor & 0.006 \\ \(\zeta\) & Random noise factor & 0.003 \\ \(\Gamma^{pos}\) & Position cooperation factor & 1 \\ \(\Gamma^{oels}\) & Velocity alignment factor & 1 \\ \(l_{c_{0}}^{lo}\) & Decay coefficient & 0.2 \\ \(l_{a}^{lo}\) & Desired spacing & 0.2 \\ \(\alpha_{\varphi}\) & Self-driving instrument conrol parameters & 3.15 \\ \(\alpha_{k}\) & & 0.4 \\ \(\alpha_{i}\) & & 1 \\ \(\mathrm{v_{min}}\) & Min horizontal speed & 0.02 \\ \(\tilde{h}_{\mathrm{max}}\) & Max height change rate & 2.1 \\ \(\varphi_{\mathrm{max}}\) & Max lateral overload & 10 \\ \(\mathrm{v_{max}}\) & Max horizontal speed & 2 \\ \(\mathrm{g}\) & Gravitational acceleration & 9.8 \\ \(\tilde{h}_{\mathrm{min}}\) & Minimum height change rate & \(-2.2\) \\ \(R_{Radius}\) & Perception radius & 70 \\ \(\varphi_{flock}\) & Polarization index threshold & 0.85 \\ \(\mathit{ion}\) & Lead-in distance & 70–65 \\ \(\mathit{ina}\) & Safe confrontation range & 3–5 \\ \(N_{top}\) & Maximum number of neighbors & 7 \\ \(\mathrm{Y_{rad}}\) & Random number & 0–1 \\ \(\mathrm{e_{rad}}\) & Probability thresholds & 1.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Swarm detailed simulation parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Numerical Value** \\ \hline \(\mathrm{W}\) & Simulation region size & 100 \\ \(\mathcal{T}\) & Maximal simulation steps & 28 \\ \(d_{\delta 2e}^{min}\) & Minimal distance between parent swarm and obstacle & 5 \\ \(d_{\delta 2e}^{satfe}\) & Safe distance between the sub-swarm and obstacle & 15 \\ \(d_{\delta 2e}^{capture}\) & Capture distance between the sub-swarm and & 10 \\ \(c_{\delta 2e}^{max}\) & dynamic obstacle & 10 \\ \(v_{sub}^{max}\) & Max speed of sub-swarm & 2 \\ \(v_{p}^{max}\) & Max speed of parent swarm & 1 \\ \(v_{e}^{max}\) & Max speed of dynamic obstacle & 1.5 \\ \(\mathcal{D}\) & & 10 \\ \(\sigma\) & & 2 \\ \(\alpha\) & & 5 \\ \(\beta\) & Discrete actions & 0.1 \\ \(\gamma\) & & 5 \\ \(c_{1}\) & & 5 \\ \(c_{2}\) & & \(-10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sub-swarm simulation parameter setting. ### Simulation Results Analysis

In this section, we assess the ability of the UAV swarm to achieve fission-fusion and perform confrontation movements, ensuring that the parent swarm movement remains unaffected during anti-confrontation scenarios.