# 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
It uses an adaptive biased probability sampling strategy to dynamically adjust the target deviation threshold, optimizing the sampling process and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparative** & **Joint Space** & **Cartesian Space** \\
**Domain** & & \\ \hline Task types & Point-to-point, high-speed motion & Trajectory tracking, high-precision \\  & Only the joint differences are required, inverse & motion control \\ Computational complexity & kinematics solution is not needed & Real-time inverse kinematics solution \\  & The end trajectory may have errors due to & Plan the end trajectory directly, the end \\ Precision & mechanical errors or flexible deformation & path is strictly controllable \\  & Difficult to directly perceive the relationship & Combine with the environmental \\ Obstacle avoidance capability & between environmental obstacles and the end path & model to avoid obstacles \\ Motion smoothness & The joint motion parameters are directly controlled, & The end path is smooth, the joint \\  & the speed and acceleration are continuous & movement may change abruptly \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of trajectory planning for robotic arms in joint space or Cartesian space. reducing the average running time by 87.34%. [PERSON] et al. [71] put forward an optimized Informed-RRT* algorithm. By adding target deviation, an adaptive step-size, and a pruning strategy, it reduces the average number of sampled nodes by 88.39%, significantly lowering the computational complexity. In the future, the algorithms should be further optimized to reduce planning time, making them applicable to larger-scale dynamic environments. In recent years, meta-heuristic algorithms have been applied to the trajectory planning problem in robotics. The trajectory planning of a robotic arm involves its end-effector smoothly moving from the starting point to the end point without hitting obstacles. Meta-heuristic algorithms have significant advantages in solving such problems. They can find the optimal solution in a large-scale exploration space without knowledge of the problem model. Currently, there are three popular meta-heuristic algorithms: the Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm, and Genetic Algorithm (GA). [PERSON] et al. [72] combined the PSO algorithm with quintic polynomial interpolation. The PSO algorithm ensures the continuity of quintic polynomial interpolation and trajectory control, enabling the robotic arm to achieve the optimal trajectory without hitting obstacles. However, compared with hybrid algorithms, the PSO algorithm has a slower computing time. [PERSON] et al. [73] combined the global exploration advantage of the Whale Optimization Algorithm (WOA) with the GA and proposed a hybrid optimization technique, the Whale Genetic Algorithm (WGA). Compared with the WOA and GA, the WGA shortens the trajectory completion time by 44%, reduces energy consumption by 15%, and maintains compliance with kinematic and operational constraints. Optimizing algorithm parameters and creating hybrid models using two or more optimization methods can optimize the trajectory planning problem and obtain the global optimal solution, which is an important future development direction for trajectory planning. On the other hand, intelligent algorithms simplify complex computations. However, no current algorithm can simultaneously meet the requirements of global convergence, real-time solution, accuracy, universality, and simplicity of the solution process. Therefore, intelligent algorithms remain a key area of future research. (2) Image Processing  
Image processing technology enhances loading arm autonomy by extracting object coordinates from acquired images. It uses an inverse kinematics model to calculate joint angles required for the end-effector to reach the desired position and orientation, achieving precise docking with tanker flanges [74]. The accuracy of tanker flange identification directly impacts loading arm docking precision. [PERSON] et al.