# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 1 Introduction  
Excavators, the backbone of construction, mining, and infrastructure projects, are transitioning from being manually operated to becoming autonomous as part of digitization and intelligization in the construction machinery industry [1]. This shift towards unmanned excavation is not just a leap in operational efficiency and production capacity, especially in the mining sector, but also a step forward in worker safety by reducing exposure to hazardous conditions. However, the transition to unmanned systems introduces considerable challenges, as the current reliance on expert experience and dynamic programming for trajectory planning is limited by their inability to match the efficiency of manual excavation and their poor adaptability to diverse environmental conditions and soil types [2]. The challenges of unmanned excavation are multifaceted, involving the intricacies of data complexity and the need for robust and reliable operations [3]. Excavators must navigate complex terrains, adapt to varying excavation areas, and integrate a wealth of mechanical and sensor data to accurately perceive and respond to their environment [4]. Discrepancies between virtual simulations and real-world scenarios necessitate careful data sampling and raise issues of integration among simulation platforms, control systems, and reinforcement learning algorithms. These complexities can lead to overfitting and a degradation in performance, underscoring the need for advanced solutions that can handle the sophisticated decision making and environmental perception required for effective unmanned excavator operations [5]. Addressing key gaps in autonomous excavation, we identify:  
1. Terrain Adaptability: Autonomous systems lack flexibility, falling short in efficiency compared to manual excavation. 2. Simulation-Reality Discrepancy: Complex environmental methods struggle with overfitting due to differences between simulations and actual conditions. Static Decision Models: Existing systems need dynamic decision making to adapt in real-time to environmental changes. To address these challenges, we designed the E-GTN framework. It is a specialized pipeline designed for the autonomous mining domain, focusing on terrain information extraction related to reinforcement learning tasks in unmanned mining operations.The E-GTN framework first uses LiDAR to capture raw point cloud data to reconstruct terrain features in multiple ways. The terrain feature extraction module is the core of the E-GTN framework and utilizes a convolutional neural network for advanced feature extraction. Our customized GridNet proficiently processes grid data to extract key terrain features that contribute to reinforcement learning tasks in autonomous mining. Finally, the decision module utilizes the features extracted by the GridNet to inform the reinforcement learning algorithm, ensuring efficient and intelligent autonomous excavator operation. E-GTN ensures a smooth transition from data collection to extraction for terrain features, simplifying the entire process. The operational environment awareness technology provides the necessary technical support for the next path generation worker, which is the precursor of the excavation trajectory generation technology. Our main contributions are as follows:  
1. We introduce advanced multisensor fusion for terrain environment reconstruction within the E-GTN framework, significantly enhancing the terrain's geometric representation and laying the groundwork for high-fidelity environmental reconstruction. 2. We present a convolutional network-based perception for grid-based excavation environments through the Terrain Feature Extraction module, enabling the application of our custom-designed model, GridNet, to extract salient terrain features crucial for the reinforcement learning algorithm. 3.