**1. Introduction**

In recent years, major mining groups have increased their investment in intelligent mining, and the mining industry is gradually entering the era of being remote, smart, and unmanned [1–5]. Intelligent vehicles are the most important pieces of equipment for intelligent mining with unmanned driving. Path planning is one of the key technologies for autonomous driving of intelligent unmanned vehicles. A reasonable path planning algorithm helps vehicles optimize the running trajectory, avoid obstacles according to the environment, and realize safe and efficient driving. The intelligent vehicles include drilling rigs, charging jumbo, load–haul–dump (LHD), trucks, scaling jumbo, and bolting jumbo, etc., the goal of which is to achieve intelligent mining processes by autonomous positioning, autonomous navigation, autonomous driving, and autonomous operation. These underground intelligent vehicles are shown in Figure 1.

The path planning of underground intelligent vehicles is one of the branches of research of unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV). With the advancement of technology, they have been widely used in the fields of logistics, transportation, disaster relief, etc., [6,7]. The research into UGV automatic driving in underground mining can be traced back to the 1960s [8,9]. The USA, Canada, Sweden, etc., have researched the remote control of vehicles, but due to the limitations of communications and sensors, the application progress was slow. With the technological revolution, such as the Internet of Things (IoT) and machine learning, unmanned mining operation has become a research hotspot in the mining field again. The European Union (EU) initiated the "Robominers" project to develop bionic robots for underground mining operations in harsh environments [10]. Rio Tinto launched the "Mine of the Future" program, which

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**Citation:** Wang, H.; Li, G.; Hou, J.; Chen, L.; Hu, N. A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT\* Algorithm. *Electronics* **2022**, *11*, 294. https://doi.org/10.3390/ electronics11030294

Academic Editor: Daniel Gutiérrez Reina

Received: 13 December 2021 Accepted: 14 January 2022 Published: 18 January 2022

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aims to remote control more than 10 mines in Pilbara from Perth to realize unmanned mining operations [11]. The Swedish Mining Automation Group (SMAG) also proposed a plan to lead the automation upgrading of the mining industry [12]. The main research interest in this paper is the path planning of underground intelligent vehicles. Based on the known environmental map, starting point, and target, we use the path planning algorithm to obtain an appropriate path that accords with mining operation and vehicle kinematics. More generally, we research global path planning algorithms.

**Figure 1.** Underground intelligent vehicles.

In addition to the characteristics of common UGVs, the control of underground vehicles has strong industry specificity, which leads to more complicated path planning. First, the mechanical structure of the underground vehicles is more complicated, which is different from the common four-wheeled UGVs on the ground and UAVs in the air [13]. Thus, the underground vehicles are more difficult to control from kinematics and need a defined path. Second, compared to roads on the ground, the underground space is narrow and curved, with many irregular surfaces. Path planning for underground vehicles needs to focus more on passing narrow points and turns. Finally, there is no GPS underground, and the communication is worse than that on the ground. The path is required to be relatively simple, which reduces the control commands. Above all, the path planning method for UAVs or UGVs will not totally accord with that of underground vehicles. Therefore, it is necessary to upgrade the existing path planning method to adapt to underground intelligent vehicles.

RRT\* is a sampling-based algorithm with probabilistic and complete resolution, high speed, and smooth results. For the 2D finite space of underground vehicles, it has a higher probability to create a path through narrow points and turns, which is closer to the underground requirements. Therefore, the RRT\* algorithm was selected as the basic algorithm in this paper. With the aim of intelligent mining operation, by considering the kinematics of the intelligent vehicles and the drift environment, three improvements are proposed, including dynamic step size, steering angle constraints, and optimal tree reconnection. The algorithm improves the effectiveness of obstacle avoidance and shortens the distance while ensuring efficiency, which provides a feasible path planning method for intelligent vehicles.

Overall, this paper proposes a path planning method based on an improved RRT\* algorithm to solve the problem of path planning for underground intelligent vehicles under articulated structures and drift environment conditions. Fully considering the environmental and equipment characteristics of underground mines is also an important feature. The remainder of this paper is organized as follows. In Section 2, the related works are reviewed and the necessary preliminaries of intelligent mining are presented. In Section 3, the constraints of intelligent mining are formulated, including the drift environment formulation and the kinematics of vehicles. In Section 4, the process of the classic RRT\* algorithm is analyzed and three improvement measures are proposed to adapt to underground intelligent vehicles. In Section 5, the case study by simulation method is presented, and the results are discussed. In Section 6, the paper is concluded.

#### **2. Related Works**

### *2.1. Underground Intelligent Vehicles*

Autonomous vehicle driving is one of the key technologies of intelligent mining, and its main sensors and operating modes are shown in Figure 2. The intelligent vehicles collect their states and environmental information by laser lidar, inertial measurement unit (IMU), camera, RFID, and other sensors and calculate their current position and state by using edge computing. Then, they interact with cloud computing through wireless communication to obtain driving paths and complete the current driving process.

**Figure 2.** The main sensors and operating modes of autonomous driving vehicles.

Intelligent vehicles for underground mining can be divided into either an integral type or an articulated type according to their structure. The integral type has the advantage of a simple structure but has the disadvantage of often having insufficient power. It is mainly used in pick-up trucks, small LHDs, and other small vehicles. The articulated type has the advantages of a small steering radius and sufficient power and is more suitable for the narrow environment of underground mining [14]. Therefore, it is widely used in heavy equipment such as underground large LHDs, trucks, and jumbos. Articulated vehicles are more suitable in underground mines [13]. However, articulated vehicles have more complex structures than four-wheeled cars. For these reasons, articulated vehicles were selected as the main research object of this paper.

Large vehicles have a higher production capacity, but increasing the size of the drift increases the development cost. The size of the drift is often selected to meet the minimum specifications for vehicles, which put forward strict requirements for the running trajectory of vehicles. Therefore, the core of path planning for intelligent vehicles is to coordinate the environment of the drift and the kinematics of vehicles to obtain an optimal path trajectory that guides the vehicles to drive autonomously.
