### Research Status of Intelligent Excavators
When the dust concentration in the air reaches a certain degree, it will seriously affect the effective measurement accuracy of the Light Detection and Ranging scanner on the working environment and the working target, and more serious cases will appear blocking phenomenon. To study the acquisition and noise reduction processing of three-dimensional point cloud data of excavator operating environment under dusty conditions, and to reduce the influence of dust on LiDAR measurement data. It is of great significance to improve the sensing ability of the operating environment of intelligent excavators. 3. The excavation targets and loading vehicles faced by intelligent excavators in the construction process are often complex in structure, with diverse details such as shape, color, texture, etc., and outdoor operations susceptible to the impact of light, weather, etc. These will seriously affect the environmental perception effect of intelligent excavators. Multi-sensor information fusion technology is of great significance in improving the working environmental perception ability of intelligent excavators under complex working conditions. In environmental perception tasks, a single sensor faces many shortcomings, and LiDAR can obtain accurate position information. But the collected point cloud data lack important information such as color and texture, and the point cloud data are relatively sparse. In complex operating environments, this will affect the ability to identify fine target features. The camera can acquire rich color and texture information, and the resolution of the image data is relatively high. However, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Research Institution** & **Major Contribution** & **Literature** \\ \hline
2019 & Zhejiang University & The experimental mining robot is developed, which can realize the functions of trajectory planning, trajectory tracking and remote control, and has a certain degree of automation. & [19] \\ \hline
2021 & Harbin Institute of Technology & Computer vision was introduced into the identification of the working cycle stage of the excavator, and the depth vision target detector was established by using the YoloV2 algorithm to locate and classify the three feature parts (bucket, arm joint and body). & [20] \\ \hline
2022 & Dalian University of Technology & The environmental perception algorithm is designed to process the point cloud data, carry out multi-level ground extraction for the point cloud and identify the mining loading target based on feature computing, and help the excavator to determine the location of the material pile and truck in the working environment, so as to realize the perception of the working environment of the excavator. & [21] \\ \hline
2024 & Yanshan University & A time-convolutional recurrent neural network (TCRNN) combining stackable extended convolution with an attention-based Sequence-to-sequence (Seq2 Seq) module is proposed to accurately predict excavation forces and a data-driven excavation trajectory planning framework based on TCRNN is established to improve operational performance in autonomous excavation scenarios. & [22] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._since the camera is a passive measurement sensor, it is less robust, seriously affected by the environment, and unable to directly obtain depth information. Compared with the traditional single sensor, the multi-sensor information fusion technology can obtain more comprehensive, accurate, and robust operating environment information. Therefore, it plays a crucial role in improving the operating environmental perception ability of intelligent excavators under complex conditions. 4. The segmentation and recognition of the three-dimensional operation scene of the intelligent excavator (as shown in Figure 4) is the basis for realizing the intelligence of the excavator.