Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review
Abstract
:1. Introduction
2. The Literature Sources and Statistical Analysis
2.1. Data Source
2.2. Statistical Method and Result
- (1)
- As can be seen from Figure 1, the research in foreign countries about intelligent LHD could be traced back early to the 1990s and was mainly focused in 2007–2022. The keyword co-occurrence was not focused, shown as many scattered words with similar font sizes. If anything was summed up, the research topics about simulation prediction, dynamic model, algorithm, navigation and path tracking were relatively popular.
- (2)
- From Figure 2, the research about the intelligent LHD in domestic China was much later than that in foreign countries, beginning from about 2007 and mainly focused in 2009–2021, with hotspots mainly focused on key technologies, such as fuzzy control, remote control, autonomous driving, path tracking, environmental recognition, autonomous navigation and safe obstacle avoidance.
- (3)
- No matter whether at home or abroad, the research on intelligent LHD was scattered and not extensive. However, it is an undoubtedly important machine and would be one of the hotspots for the intelligent mining industry.
3. Development and Application Status of Intelligent LHD
4. Autonomous Shovel Technology
4.1. Rock Pile Identification and Modeling Technology
4.1.1. Image Sensor-Based Rock Pile Identification
4.1.2. Rock Pile Identification Based on Distance Sensor
4.2. Shovel Trajectory Planning
4.2.1. Resistance Force Determined Trajectory Planning Method
4.2.2. Self-Learning-Guided Trajectory Planning Methods
5. Autonomous Navigation Technology
5.1. Positioning Technology
- (1)
- Dead reckoning method
- (2)
- Inertial navigation technology
- (3)
- UWB Positioning
- (4)
- Visual Positioning
- (5)
- Information Fusion and Location Technology
5.2. Path Planning
5.2.1. Global Path Planning
- (1)
- A* algorithmThe A* algorithm is a heuristic search algorithm, which guides and determines the search direction, mainly through an evaluation function [86]. As long as the optimal distance from the node to the target point is determined, an optimal path must be obtained [87]. However, it is necessary to conduct a traversal search around the nodes on the path to optimize the path and save cost, resulting in large calculation amount, poor real-time performance and long operation time. Moreover, as the number of nodes increases, the algorithm search efficiency decreases [12]. In order to improve the efficiency for the optimal path searching and reduce the searching time, Zheng et al. [88] used a jumping point search method based on the A* algorithm and introduced the angle evaluation function into the cost function in the A* algorithm. The number of inflection points on the path obtained by the combined method was minimized compared with that by the original A* algorithm and quick optimal path search was achieved with speed faster than that of the traditional A* algorithm. Ma F. et al. [89] proposed a navigation path planning method for articulating underground LHD based on the improved A* algorithm, through introducing the collision treat cost into the evaluation function, in order to avoid the LHD from scraping the narrow roadway walls. According to the specific requirements of the path planning for unmanned underground LHD, Qi Yulong et al. [90] proposed an improved A* algorithm modeled with extended nodes and introduced the collision threat cost into the evaluation function to avoid the scraper from collision onto the tunnel walls. Simulation tests were also conducted and it was verified that the modified A* algorithm method could enhance the search process, improve the safety of the scraper and prevent collisions.
- (2)
- Fast Random Search Tree AlgorithmThe fast search random tree algorithm is an incremental search algorithm based on probability sampled data. The basic idea is to take the starting point of the automatic LHD as the root node of the random tree, then find a tree node closest to the root one and expand a step length. If collision occurs, the node is discarded and a new expanding direction is set randomly from the current tree node to find the next tree node. The cycle is repeated until a new direction is found. The advantages of this method include high search efficiency, strong search ability, wide search range and no specific requirements for the scene. However, it faces the following shortcomings: nonautonomous search, low utilization rate for the evenly allocated random sampling points, irregular and time-consuming planned path and easily falling into dead zones and causing local minima for searches in complex maps [12,86].
- (3)
- Bioinspired Intelligent AlgorithmCompared with traditional algorithms, the advantages of bioinspired intelligent algorithms are mainly reflected in the ability to solve multi-objective optimization problems effectively, anti-interference strongly, obtain the global optimal value quickly without limitation of local optimal value and the initial value, etc. [91]. It can be mainly divided into genetic algorithm, particle swarm algorithm, ant colony algorithm, etc. [85].
- Genetic AlgorithmGenetic algorithm is an intelligent optimized algorithm based on biological genetic evolution theory in nature. It is the mainstream of robot path planning research and has great research prospects [92]. This algorithm shows good compatibility with other intelligent algorithms, attributed to easy improvement and excellent iterative evolution. The method is flexible in search with the generation of initial population and introduction of crossover and mutation operators and also capable for global optimal path determination. However, at the same time, the calculation speed is slowed down with relative low searching efficiency. In addition, too many inflection points in the path result in the generation of some meaningless populations during iterative evolution of the algorithm, which slows down the subsequent calculation process. Thus, this method is not suitable for online path planning.
- Neural Network AlgorithmNeural networks are intelligent systems composed of many simple but highly interconnected processing elements that transmit information through dynamic responses to external inputs [93]. Neural networks have the characteristics of high fault tolerance, distributed representation, extensive parallelism and generalization. Afifi et al. [94] proposed a multi-level system built with a deep reinforcement policy gradient algorithm, which can collaboratively plan multi-vehicle collision-free travel paths through motion planning. Luviano et al. [95] proposed a multi-agent reinforcement learning algorithm to solve the problem that unmanned vehicles learn slowly or even fail to learn in a completely unknown environment. By ensuring the corresponding reward methods and completing the training process, the optimal path can be found. Pang Ke et al. [96] reported a route search strategy for unmanned vehicles that integrates the reinforcement learning algorithm and the deep learning algorithm. It determines the driving path by driving comfort constraints together with the function about reward and punishment of obstacle information and traffic regulations.
- Ant Colony AlgorithmThe ant colony algorithm has good comprehensive performance and strong global optimization ability, which can complete the scraper path planning in complex mining environments, but it is easy to reach a stalemate of only local optimal. Long Zhizhuo et al. [97] proposed global path planning for underground intelligent LHD through an improved ant colony algorithm to solve the problems of slow convergence speed and easy stagnation due to local optimum in the traditional ant colony algorithm.
- Particle Swarm AlgorithmParticle swarm optimization is also a probabilistic global path planning algorithm. Because of its multi-possibility of the iteration, it is much more possible to cover the global map during the path searching process with this algorithm. Correspondingly, the global optimal solution is easier to be obtained [98]. The particles adapt well to complex situations through the interconnection of information. Hence, this method is highly adaptable, even in a high-dimensional environment.
5.2.2. Local Path Planning
- (1)
- Artificial Potential Field Method
- (2)
- Fuzzy Logic Algorithm
6. Real-Time Monitoring and Fault Diagnosis Technology
6.1. Real-Time Monitoring
6.2. Failure Prediction and Diagnosis
- (1)
- The data for the underground LHD real-time status are not fully utilized. Excavation on the collected data is not deep enough for fault prediction and diagnosis.
- (2)
- Fault prediction and diagnosis are mainly targeted on the engine and hydraulic system of the scraper and few studies have been conducted on other systems.
- (3)
- Even deep learning has attracted the attention of many researchers as a new method in the field of intelligent fault diagnosis, though few studies have been conducted on fault diagnosis for LHD to date.
7. Summary
- (1)
- For better mile pile perception in the future, how to complement and optimize the information of multi sensors in a multi-level and multi-dimensional manner, improve the data processing speed and establish the three-dimensional model would be the critical scientific issues, as a single sensor perceives poorly for the heaps in underground roadways that are dark, dusty and face field interference.
- (2)
- In the research for bucket shovel loading trajectory plan and optimization, the planning method based on reinforcement learning will be one of the mainstream directions under the background of artificial intelligence, big data and cloud computing in the future, while how to complete the shoveling most efficiently with the least energy consumption is the key goal for this method.
- (3)
- As for autonomous navigation technology, it is one of the key researched technologies for underground intelligent LHD, both at home and abroad, and it directly determines whether the transport of the ore will succeed or not. Thus, the research on multi-sensing information fusion technology and the positioning accuracy improvement and speeding should be focused on. The combination of the global path planning with the local path planning methods to plan a travel path, which is without collision and has shortest time consumption, will be the mainstream direction in the future.
- (4)
- With the introduction of digital twin technology into the intelligent mine construction field, synchronous mapping and real-time interaction between physical equipment and virtual equipment can be achieved. By building digital twin models for the intelligent LHDs in the coming future, remote monitoring, fault diagnosis, control optimization and health prediction for the physical machine are expected to be attained through modeling on the extracted feature from the faults and the corresponding process and analyzing the interference factors.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Region | Foreign | China |
---|---|---|
Retrieval date | 17 May 2022 | 17 May 2022 |
Database | Web of Science Core Collection (WOS) | CNKI |
Retrieval method | TS = (autonomous OR automatic OR intelligent OR navigation OR location OR unmanned OR track OR remote OR route plan OR control OR shovel OR perception OR model OR underground mining OR sensors) AND TS = (load haul dump) | SU = (“intelligent” + “unmanned” + “autonomous” + “automatic” + “track” + “location” + “navigation” + “remote” + “control”) × (“load haul dump”) |
Time span | 1980–2022 | 1980–2022 |
Number of documents retrieved/article | 127 | 449 |
Number of valid documents/article | 127 | 158 |
imported into CiteSpace software | 125 | 142 |
Positioning Method | Advantage | Disadvantage |
---|---|---|
Dead Reckoning | It can achieve high accuracy with low cost in the short term | Errors will accumulate over a long period of time |
Inertial Navigation | It is unaffected by external factors and shows good concealment | Errors will accumulate over time and the equipment are expensive |
UWB Positioning | It is insensitive to channel fading, with simple system and high positioning accuracy | Multiple base stations are required, which is costly |
Visual Positioning | It shows high positioning accuracy | Roadway dust, light intensity and other environmental factors affect the positioning easily |
Information fusion positioning | It is extensively applied, with high positioning accuracy | The cost and calculating complexity increase |
Algorithm | Advantage | Disadvantage |
---|---|---|
A* algorithm | It responds quickly to the environment | It has large amount of computation, poor real-time performance and long operation time |
Fast Random Search Tree Algorithm | The search is highly efficient and is adaptable to different scenes | It is nonautonomous and time consuming for the path planning |
Genetic Algorithm | Easy to plan for the global optimal path | The calculating speed is slow with low search efficiency |
Neural Network Algorithm | high fault tolerated and generalization ability | Huge training data is required and there may be some unexpected data which is difficult to be handled |
Ant Colony Algorithm | The optimal path can be searched at multiple points in the global area at the same time | Easy to fall into local optimum and slow convergence |
Particle Swarm Algorithm | Fast search speed and good environment adaptability | Easy to result in local optimum and low convergence accuracy |
Artificial Potential Field | Simple structure, convenient for bottom real-time control | Easy to simply obtain a local optimal solution and “chattering” phenomenon would occur |
Fuzzy Logic Algorithm | The uncertainty and ambiguity for data processing can be overcame, exhibiting good real-time performance | It is expert in experience and requires large amount of calculation for complicated situations |
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Xiao, W.; Liu, M.; Chen, X. Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review. Appl. Sci. 2022, 12, 9290. https://doi.org/10.3390/app12189290
Xiao W, Liu M, Chen X. Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review. Applied Sciences. 2022; 12(18):9290. https://doi.org/10.3390/app12189290
Chicago/Turabian StyleXiao, Wei, Mingxia Liu, and Xubing Chen. 2022. "Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review" Applied Sciences 12, no. 18: 9290. https://doi.org/10.3390/app12189290