## 2 Related Work
### Sensors and Ranging Technology  
In this era, sensors that utilize lasers to perform non-contact distance measurements have become a completely integral part of automation engineering. In [36], [PERSON] et al. explore sensors and their fusion in autonomous vehicles, with a particular emphasis on \"integrating essential sensors like cameras, radar, and LiDAR\". The study demonstrates that incorporating additional sensors into the fusion system enhances performance and solution robustness. Different simulators that are commonly used to simulate various sensor payloads for autonomous vehicles are summarized in Table 1.  
Table 2 presents a detailed comparison of various types of sensors, highlighting their respective advantages and disadvantages. Numerous ranging technologies exist, each defined by its unique principles and practical uses. Some typical varieties of ranging technologies are as shown in Figure 2. Below are the most prevalent sensors used in perception systems. Table 3 outlines the most prevalent sensors used in perception systems. * Frame rate: Indicates the rate at which the sensor captures and processes data per second. * Size: Refers to the physical dimensions of the sensor, typically described as small, compact, or medium to large. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Simulator** & **License** & **Programming** & **IMU** & **LiDAR** & **ToF Camera** & **Radar** & **RTK** & **GNSS** \\ \hline Gazebo & BSD & C++, Python, ROS & x & x & x & & x & x & x \\ Webots & Commercial & C++, Python, Java, & u & x & x & & - & x & x \\  & MATLAB & & & & & & & & \\ MRDS & Microsoft Public License & Cr & u & x & - & - & - & - \\ Blensor & GPL & Python & x & x & x & u & x & x \\ MORSE & GPL & Python & x & x & u & - & x & x \\ V-Rep & GPL & Lau, C/C++ & x & x & u & & x & x & x \\ USARSim & GPL & C++, Cr & x & x & u & - & x & x \\ OpenRAVE & Apache 2.0 (Free for non- & Python, Cr & s & s & s & s & s & s \\  & commercial use) & & & & & & & & \\ CoppellaSim & Proprietary (Free for educational use) & & & & & & & & \\ [PERSON] & Free for non-commercial & Python & C++ & x & x & x & x & x & x \\  & use & & & & & & & & \\ \hline \hline
**Explanation of symbols:** x = Supported natively. u = Limited or partial support. - = Not supported. s = Requires scripting or external modules. & & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Different simulators and their sensor support for autonomous vehicles. * Visibility: Indicates the sensor's performance under different visibility conditions, such as good visibility, which means the sensor performs well regardless of lighting or environmental conditions. * Field of View: Describes the extent of the scene that the sensor can capture, ranging from wide (covering a large area) to narrow (focusing on a specific region). * Resolution: Refers to the level of detail captured by the sensor, described as low (less detailed) or high (more detailed). * Weather Effects: Represents the impact of adverse weather conditions on the sensor's performance and functionality, with minimal meaning the sensor is relatively unaffected. * Range: Specifies the maximum distance at which the sensor can effectively detect objects or gather data, varying from short to medium or long distances. Table 4 provides an explanation of the comparison between various sensor technologies utilized in the manufacturing and design of autonomous forklifts, emphasizing their components, applications, and accuracy. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Sensor Type** & **Advantages** & **Disadvantages** \\ \hline RGB Cameras & High resolution & Limited depth perception \\ RADAR & All-weather capability & Limited resolution \\ LiDAR & Accurate 3D mapping & Expensive \\ IMU & Low cost, high frequency & Susceptible to drift \\ GNSS & Global coverage & Poor performance in urban canyons \\ RTK & High precision & Limited coverage area \\ ToF & Fast response time & Limited range \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensor comparison.