## 5 Industry Applications and Manufacturers  
Autonomous forklifts are equipped with technology that enables them to operate independently from human control. Also, machine vision algorithms play a critical role in allowing these forklifts to navigate, perceive their surroundings, and make decisions based on what they see.  
This section is summarized in Figure 19, which highlights the core components discussed in this chapter. The figure provides a symbolic overview of the section.  
\begin{table}
\begin{tabular}{l p{284.5 pt}} \hline
**Ref.** & **Discussed Topic** \\ \hline \multirow{3}{*}{[107]} & It discusses the implementation of a QR code-based automated parking management system for un-instrumented indoor environments by providing QR code slips, and automated entry and exit processes. \\ \hline \multirow{3}{*}{[108]} & It discusses the use of QR codes as artificial landmarks for automated guided vehicles (AGVs), proposing a method based on the distance and angle between an AGV-mounted camera and strategically placed QR codes for precise self-localization. \\ \hline \multirow{3}{*}{[109]} & Utilizing a single camera for steering control, QR codes for localization, and SONAR for collision avoidance, demonstrating improved efficiency compared to existing methods through in-field testing on a prototype vehicle. \\ \hline \multirow{3}{*}{[110]} & QR codes are utilized for high-precision autonomous landing of UAVs on mobile UGVs in a cooperative system, providing relative distance and direction information, and offering a vision-based solution. \\ \cline{2-3}  & \\ \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{l p{284.5 pt}} \hline
**Ref.** & **Discussed Topic** \\ \hline \multirow{3}{*}{[112]} & The paper proposes a solution for the autonomous and accurate landing of UAVs using a combination of on-image search and ArUco marker-based landing algorithms. \\ \hline \multirow{3}{*}{[113]} & The article suggests an innovative approach utilizing two computer vision methods, based on ArUco markers and a DCNN, to precisely determine object positions around vehicles. \\ \hline \multirow{3}{*}{[114]} & The article introduces an independent stereo-vision positioning system for docking operations, incorporating a low-cost setup with a learning-based object detection model. \\ \hline \multirow{3}{*}{[115]} & The article addresses marker-based tracking control of a ground-moving target using a drone with a full HD camera. Experiments conducted in indoor and outdoor environments demonstrate the approach’s effectiveness in tracking targets with various marker sizes and quantities. \\ \hline \multirow{3}{*}{[116]} & The article proposes a vision-based approach using ArUco fiducial markers for the autonomous detection, following, and landing of an aerial robot on a mobile ground platform. \\ \hline \multirow{3}{*}{[117]} & The article introduces an autonomous vision-based guidance and mapping algorithm for drones in GPS-denied environments, utilizing OpenCV ArUco markers and a stereo camera for accurate path detection and guidance. \\ \cline{2-3}  & \\ \hline \end{tabular}
\end{table}
Table 9: Summary of articles discussing ArUco marker applications.  
Machine learning, on the other hand, plays a pivotal role, allowing the vehicle to continuously adapt and improve its performance based on accumulated experience. Connectivity features facilitate communication between autonomous vehicles and infrastructure, further enhancing safety and efficiency. This examination of autonomous technology emphasizes the smooth fusion of hardware and software, opening the door to a future in which self-driving vehicles effortlessly integrate into our everyday routines.