# The Evolution and Development Trends of LNG Loading and Unloading Arms
## 3 Development of Land-Used LNG Loading and Unloading Arms
### Land-Used Loading Arm Actuation and Its Intelligence and Automation
\begin{table}
\begin{tabular}{c c c} \hline
**Comparative Domain** & **Automatic Docking** & **Manual Docking** \\ \hline Operational efficiency & Fully automated, unmanned operation with short single loading/unloading time & Manual skill-dependent operations with long loading/unloading time \\ Docking accuracy & Visual positioning, both axial and angular deviations are relatively small & Manual visual docking, high deviation \\ Safety performance & Real-time sensor surveillance, EBS & High risk of human error, high accident rate \\  & achieves a 0.5 s response time & \\ Cost & Low labor cost, long-term operation and maintenance cost & Great demand for operators, long training \\ Environmental adaptability & Strong anti-interference capability & Cycle for manual operations \\ \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between manual dragging docking and mechanically driven automatic docking. Figure 14: Intelligent management control system structure framework diagram for LNG tank trucks. (1) Trajectory Planning  
Trajectory planning serves as the foundation for motion control and directly influences the accuracy of trajectory tracking in loading arms [69]. The trajectory planning of a loading arm involves determining its motion path under given constraints to ensure optimal smooth movement from the starting point to the target while avoiding obstacles. There are two methods for generating trajectories: joint-space planning and Cartesian-space planning. In joint-space planning, all joint variables are expressed as functions of time, and these joint functions--along with their derivatives--describe the robot's intended motion. In contrast, Cartesian-space planning defines the position, velocity, and acceleration of the loading arm's end-effector as functions of time. A comparison between these two approaches is presented in Table 2.  
As shown in the table, Cartesian-space trajectory planning requires real-time inverse kinematics solutions to achieve trajectory tracking and obstacle avoidance, making it suitable for complex scenarios with collision avoidance requirements. In contrast, joint-space trajectory planning eliminates the need for inverse kinematics computations, offering simplified and efficient operations. This approach effectively circumvents robotic manipulator singularities and redundancy issues, thus being widely adopted in applications where strict end-effector path control is not critical, such as material handling. Path planning algorithms generally fall into the following categories: the artificial potential field method, graph-based search algorithms, intelligent algorithms, and sampling-based algorithms. The artificial potential field method offers good real-time performance and is suitable for dynamic environments; however, it is prone to local minima and cannot find the global optimal solution. Graph-based search algorithms discretize the environment into nodes and use graph search strategies to find optimal paths. Typical algorithms include [PERSON]'s algorithm and the Dynamic Window Approach (DWA). These algorithms have high precision and are suitable for static, structured environments, but they become computationally complex in high-dimensional spaces, and their real-time performance may be affected. Intelligent algorithms rely on iterative search, with computational complexity growing exponentially as problem size increases. Their performance depends heavily on parameter settings, which are difficult to optimize. Sampling-based algorithms construct a probabilistic graph to search for feasible paths by randomly sampling the environmental configuration space. Common ones include Probabilistic Roadmap (PRM) and Rapidly-exploring Random Tree (RRT). This type of algorithm can handle complex environments, but it has low sampling efficiency and poor adaptability to changing environments. To address these issues, [PERSON] et al. [70] proposed an AGP-RRT* algorithm.