# A Comprehensive Review of Micro UAV Charging Techniques
## 5 Distributed Laser Charging (DLC) for UAVs
[24] proposed a multimodule DLC model and briefly explained the evaluation of power conversion or transmission for each individual module, considering the impacts of temperature, PV cell, attenuation and laser wavelength. [PERSON] et al. [97] have discussed the DLC method for safe mobile applications. The authors have briefly demonstrated the concept of the DLC technique in the context of smartphone charging. They have proposed two wireless charging techniques: (i) DLC-aided, infrastructure-based network, (ii) DLC-based ad-hoc network. They have demonstrated the potential of the DLC technique through these network architectures to enable a fully charged utopia for smartphones. It can provide safe, mobile, long-distance wireless charging for IoT devices. It also has the significant feature to supporting multiple IoT devices simultaneously. DLC has also been used for quadrotor UAVs. It can charge UAVs with its self-alignment feature, thus it does not require specific tracking or positioning as the LoS link is established. DLC receivers can be easily embedded into UAVs due to their small size. These features make DLC suitable for aerial networks. In [100], the authors proposed a simple DLC-source quadrotor UAV model to investigate energy and battery dynamics. In [101], the authors proposed a novel framework for the trajectories of multi-UAVs through the prediction of users' mobility information. Authors used the DLC method for power control. A compact DLC is embedded in a battery-powered off-the-shelf UAV. As DLC has self-aligning capability and LoS path is also available due to the high altitude of UAVs, thus DLC can charge UAVs as long as they remain in the DLC's coverage area. DLC-aided UAVs can fly for a long time without landing before any specific maintenance is required. In [102], the authors proposed artificial intelligence for DLC-aided UAVs. In this method, the trajectory of UAVs and model predictive control (MPC), optimal numbers and locations of charging stations can be optimized by using AI solutions. In [103], the authors proposed a novel design of distributed and multi-layer UAVs (DAMU) 5G networks and stated that DLC methods, along with machine learning-assisted computer vision and sensor techniques, can be helpful in ensuring safe laser energy harvesting. Table 4 summarizes a comparison of DLC with different WPT techniques. \begin{table}
\begin{tabular}{c c c c c} \hline
**WPT Technique** & **Advantage** & **Disadvantage** & **Changing Distance** & **Application** \\ \hline Microwave & & Low charging efficiency, & & LEDs, implanted body \\ radiation & Longer charging range & health and safety issues in & Up to several kilometers & devices, sensors, RFID cards \\ \hline Magnetic resonance & Non-line-of-sight (NLOS) & & Complex implementation, & & Electrical vehicle charging, \\ coupling & charging, high charging efficiency, & & limited charging distance, & Up to a few meters & home appliances, \\ coupling & & charging multiple devices & & mobile electronics \\ \hline Inductive & & Alignment issues, heating & & & \\ coupling & Simple implementation, safe & & effect, short charging range & Up to a few centimeters & Contactless smartards, \\ Distributed laser & & SWIPT and LBS ready, visibility & & & mobile electronics \\ charging (DLC) & & agnostic, EMI free, safe, & Low charging efficiency, & & \\ self-alignment & & LOS required & Up to several meters & LEDs, sensors, consumer \\ \hline \end{tabular}
\end{table} 
Table 4: A comparison of different WPT techniques [97]. Figure 12: DLC potential applications. (**a**) LED array transmission; (**b**) drone transmission.