# Cooperative Multi-UAV Collision Avoidance Based on a Complex Network
## 4 Simulation and Results
### Further Investigation  
This section discusses the primary results obtained using the CN algorithm based on two different key node selecting algorithms, as well as those obtained by the randomly chosen key node. It also reports the statistical comparison between those methods.  
To validate the performance of the CN, a set of 500 different scenarios were grouped by the UAV number, from 24 to 48 within 100 tests. The extra consuming time is a frequently used performance metric to measure the different performance metrics that are used to measure the different performances of CA system algorithms.  
Figure 7 shows the extra consuming time of the CN algorithm compared with the randomly selected'sense.'  
The simulation result presented herein clearly demonstrates the good performance of the CN algorithm for solving high-density CA problems, owing to the potential threat being solved preferentially and reducing the domino conflict. This shows that the wasted time increases if the number of UAVs increases, because more encounters will occur if more UAVs are cruising in the same limited airspace. This also shows the extra consuming time increases faster than the UAV density, and the difference between the extra consuming time under the CN algorithm and the extra consuming time under the random algorithm increases as the density of the UAV increases. However, while the key node is chosen randomly, the wasted time is always larger than that of the CN algorithm where the key node is chosen based on the degree or node contraction methods. Under the middle density circumstance (90 UAVs), the key node selection method by degree consumes more time than the key node selecting method by node contraction, because the node contraction method can obtain more potential threat information and can more accurately. choose the most threatened node The opposite applies for the high airspace density (more than 100 UAVs), because the airspace is sufficiently clouded such that the most intuitive method can more efficiently handle this type of situation.  
We made a comparison of our proposed CN algorithm with the satisficing game theory-based algorithm (SGTA) [41], the reactive inverse proportional navigation(PN) algorithm (RIPNA) [42] and the geometric optimization model algorithm (GOM) [43], which is capable of dealing with large scale UAV cluster collision avoidance scenarios. This paper compared the computational time of different methods when the scene setting for each method was the same. The results for scenarios consisting of 20 UAVs, 40 UAVs and 60 UAVs are shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key Node Selecting Approach** & **RTF** \\ \hline Node selection based on degree & 0.0126 \\ Node contraction method & 0.0093 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RTF (residual threat factor) of two key node selecting approaches in two scenarios.  
Figure 7: Consuming time of the complex network (CN) algorithm and random approach.  
Evidently, the computational efficiency of the CN algorithm is higher than the other three algorithms, and it is worth mentioning that the average consuming time did not exponentially increase. Table 4 shows that this method can solve the collision avoidance problem in the case of high-density traffic airspace within an acceptable range.