# Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path
## 4 Augmented Path Mapping Algorithm
The graphs show that the traditional approach fails with the medium, i.e., when the medium moves from land to air, a new system is required, which provides a much better result. The graphs also indicate that if we apply for the whole journey, the UAVs have a much higher chance of completing the task than with the traditional approaches as it reduces one of the most limiting factors, i.e., the power factor. With power being used more efficiently, it can cover a greater distance using the same amount of power and thus increase the area of reach and number of tasks at any particular time. Figure 4: Time of ascent. Figure 5: Time of descent. Thus, this indicates that if the above ascent and descent algorithms are used, it provides us with faster drones, which helps in two ways: one is making them more efficient. The ascent algorithm follows an inclined plane, which not only reduces the time but also decreases the path travelled while gaining both distance and height simultaneously. On the other hand, the descent algorithm is a brachistochrone curve and one of the major advantages of this curve is that it can be followed with minimum adjustments and even in a power-saving mode as basically, it uses gravity as its driving force and thus the cumulative efficiency of this path will provide a better overall performance and higher energy output. For comparison, the path is taken to be a rectangle (2D) or a cuboid shape (3D), where the drone first follows a straight-line path to reach the maximum height and then to reach the destination as shown in Figure 7. It means that first there is a change in the \(y\)-axis or height only and then after reaching the maximum height, the drone moves forward. #### Assumptions

* Ascent
* Speed is constant and due to the presence of external thrust by motors;
* The original path is a rectangle (2d) and cuboid (3d) and the final path is their diagonals, respectively;
* Although the path is changed and calculated at each instance of time, for regularity in the results, it is kept constant and values are taken keeping that in mind. Figure 6: Total time of flight. Figure 7: Initial path. Descent
* In descent, UAV is in a free-fall condition and time is calculated for it;
* No external force is taken into account apart from the forces keeping the drone on the path. ## 6 Conclusions and Future Work

In this paper, there is a way to increase the efficiency of the UAVs with the help of a new algorithm as we see today. The basic measure of these devices is based on distance-efficient systems to reduce their journey; however, as they are in the air, these physical boundaries like roads, plots, etc. disappear. Thus, the advantage of this algorithm is to improve the efficiency by reducing the time and completing tasks faster. This helps in using battery power for a longer period. It brings out more output from these UAVs, thus making them more efficient and increasing their flight duration drastically. A disadvantage of this algorithm is although it works efficiently, all the axes are fixed, i.e., two axes are fixed for movement while the third is for barrier dodging and so this fixation might be difficult to implement in this AI as some barriers cannot be prevented by just one axis. In this paper, we did not include the internal intelligence transport system, which may be carried out later on [23]. Additionally, the cyber security in our system should be implemented, such that there would not be any possibility for cyber-attacks, which may lead to many problems [24]. For future work, we can bring a more efficient system by using it in real-time applications like on actual UAVs or quads to get real results and more ways to improve. The system could be provided with a third ultrasound or infrared scanning device with an AI, which can easily detect the incoming deviations or barriers, which can prove fatal for the drone and prevent it from using the same algorithm on the third axis while staying on the path. Another major factor is wind speed since the drone has both horizontal and vertical speeds.