# UAV Swarm Real-Time Rerouting by Edge Computing D* Lie Algorithm
## 3 Results
### Dynamic Avoidance Test--Three UAVs
#### 3.3.1 Autonomous Flight Mission Simulation (Including Immediate Avoidance)  
As described in this section, full-system functional verification was achieved through one simulation and two experiments. The first experiment tested dynamic avoidance using three UAVs. In Figure 23, the green area in the left frame is an obstacle that is unknown at first, and the original yellow flight path intersects with the obstacle. However, the experimental results showed that once provided with the obstacle information, the drone will instantly act to avoid it. Once the obstacle is recognized as a restricted area, the flight route is re-planned in real time, allowing the drone to circumvent it. The first experiment took place during a strong wind, affecting drone stability and making it difficult to correct the course, as shown in Figure 24. Nevertheless, the flight trajectory log in Figure 24 still shows that the UAV successfully avoided the obstacles and completed the entire flight. Thus, the first experiment indicated the feasibility of immediate obstacle avoidance.  
Figure 23: Route planning results of the first experiment.  
Figure 22: Programmed group route (**left**) and real-time avoidance simulation (**right**).  
The second experiment was conducted at another site. As shown in Figure 25, the completed paths of each station were assigned to simulate the flight trajectory and avoidance effects. As shown in Figure 26 (left), the pink flight path shows the simulated UAV receiving location information for the trees, triggering two immediate turns to avoid them.  
The experimental task route was the same as the group route planned during the simulation, as shown in Figure 25. The trees in the experimental field were also used as obstacles for real-time avoidance. In the experiment, while the outbound copter flew at a speed of 5 m/s, the experimenter suddenly triggered new obstacle information (tree) when the distance between copter and tree was less than 10 m, which said it had around  
Figure 26: Multi-UAVs dynamic avoidance simulation (**left**) and real flight test trajectory (**right**).  
Figure 24: Flight trajectory log of the first experiment.  
Figure 25: Route following route planning and assignment.  
2 s to handle an avoidance act, including D* Lite rerouting and the vehicle's avoidance maneuver. The experimental flight trajectory is presented as the log in Figure 26 (right). The yellow trajectory shows that the UAV successfully avoided the trees twice, thus verifying the effect of dynamic path planning through the mobile vehicle's edge computing without any command from the central ground station. The computing time of first avoidance (outbound) for D* Lite path re-planning took about only 3.2 milliseconds to calculate. Since the first avoidance instance had already recorded the position of the obstacle, the calculation for the subsequent second avoidance (backbound) was 1.4 milliseconds, less than half that of the first instance, owing to having a shorter path to calculate. From the results, the calculation time of fewer than 4 milliseconds was quite effectively instantaneous, indicating an effective obstacle avoidance.