## 4 Field Experiment Evaluations
### Collision-Avoidance Experiment Evaluations
This can be observed from the measured maximum intrusion distance of 26 m between UAV\({}_{1}\) and UAV\({}_{2}\), which is close to the theoretical maximum of 30 m, in spite of the fact that a more advanced information-sharing mechanism is adopted in this case. In fact, based on the key findings from our previous simulation-based study [27], a higher frequency (i.e., smaller \(T\)) of information sharing may result in smaller intrusion distances and better performance in the collision-avoidance process. Conversely, a higher UAV speed results in larger intrusion distances and poorer performance. When both high sharing  
Figure 12: An experimental result of UAV flights before, during, and after collision avoidance in the field experiment with \(N=4\). Figure 13: Intrusion distances for UAV collision-avoidance experiments based on a LoRa module with \(N=4\), \(v_{n,n}=2\) m/s, and \(r_{n,n}=10\) m \(\forall n\in\{1,\cdots,N\}\). frequency and large UAV speed are adopted simultaneously in our experimental results, the latter one would dominate the performance. To provide a fair comparison and demonstrate the effect of speed on intrusion distance, we conducted experiments for varying UAV speeds. Figure 15 illustrates the intrusion distances over time for collision-avoidance experiments with different UAV speeds: \(v_{n,n}=5\,\mathrm{m/s}\) and \(v_{n,n}=6\,\mathrm{m/s}\) for all \(n\in\{1,\cdots,N\}\). For the sake of simplicity, only two UAVs are deployed in this experiment and flying towards each other. In both cases, we employ the same FSK wireless module in the AFCCS with \(T=0.25\,\mathrm{s}\), and the alert radius \(r_{n,n}\) of UAV is set to \(15\,\mathrm{m}\) for safety considerations. The results, as depicted in Figure 15, reveal that the maximum intrusion distance increases from \(21\,\mathrm{m}\) to \(26\,\mathrm{m}\) when the UAV speed is increased by just \(1\,\mathrm{m/s}\). This observation confirms that UAV speed is one of the factors that plays a dominant role in the collision-avoidance process, as higher speeds can introduce larger safety risks. Consequently, it is recommended to adjust the alert radius of the UAV flexibly to accommodate varying UAV speeds. Indeed, drawing conclusions based on the deployment of only two UAVs for the experiments may be insufficient, and there could be other factors that influence the UAV avoidance process. In the near future, we plan to refine our conclusions by conducting more comprehensive comparisons, including increasing the number of UAVs. It is worth noting that, for the current experiments, we employed up to four UAVs to evaluate the collision-avoidance method based on the developed AFCCS. Undoubtedly, the number of deployed UAVs is relatively small for assessing generality, and the algorithm used in the collision-avoidance experiments may not perform optimally in a real-life scenario where most UAVs will be in operation. However, considering the limitations imposed by hardware and economic constraints, and given that our primary focus is to evaluate whether UAVs can accomplish their intended missions with the assistance of AFCCS, the effectiveness of the developed products and the conclusions drawn from our experiments are reliable. In fact, we have proposed a collision-avoidance algorithm for scenarios involving multiple UAVs in [27] and have completed evaluations based on computer simulations. We also plan to conduct further evaluations of this algorithm using the developed AFCCS in our future work. Figure 14: Intrusion distances for UAV collision-avoidance experiments based on an FSK module with \(N=4\), \(v_{n,n}=5\,\mathrm{m/s}\), and \(r_{n,n}=15\,\mathrm{m}\)\(\forall n\in\{1,\cdots,N\}\).