**1. Introduction**

The development of robotic applications has increased significantly in the last decade, and currently, robotic systems are being utilized for many purposes, in various environments. Mobile robots are no longer used exclusively in research laboratories and indoor controlled environments, but are now also used in dynamic industrial environments and outdoor sites. Moreover, the efforts for developing autonomous cars and drones have had the effect of strengthening the development and use of other autonomous machines and vehicles in various industries.

Mining is one industry in which autonomous vehicles have been in use for at least 13 years. Industrial use of autonomous hauling trucks started in 2008 in the Gabriela Mistral copper open-pit mine, located in the north of Chile. Currently, the use of autonomous mining equipment, mainly vehicles, is an important requirement in the whole mining industry. This is because mining operations need to increase the safety of the workers, as well as to augment the productivity, efficiency, and predictability of the processes. Safety is,

**Citation:** Mascaró, M.;

Parra-Tsunekawa, I.; Tampier, C.; Ruiz-del-Solar, J. Topological Navigation and Localization in Tunnels—Application to Autonomous Load-Haul-Dump Vehicles Operating in Underground Mines. *Appl. Sci.* **2021**, *11*, 6547. https://doi.org/10.3390/ app11146547

Academic Editors: Luis Gracia and Carlos Perez-Vidal

Received: 1 June 2021 Accepted: 13 July 2021 Published: 16 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

without doubt, a key factor, and has been the top priority of mining companies in recent years. This is true, especially, in underground mining operations with their hazardous environments in which workers are constantly exposed to the risks of rock falls, rock bursts, and mud rushes, and where the presence of dust in the air can result in a number of associated occupational diseases in the workers [1].

In an underground mine, autonomous vehicles navigate inside tunnels. This kind of navigation has different precision requirements from those where they are navigating in an open environment. First, tunnels are GNSS-denied environments and thus vehicles cannot use any GNSSs (Global Navigation Satellite Systems) to self-localize. Secondly, when driving inside tunnels, it is not relevant to have an accurate localization system, but it is essential to be able to move safely through the tunnel, and to make appropriate decisions at its intersections and access points. This is completely different from most robotic applications, where safe navigation requires an accurate determination of the robot's position and orientation at every moment.

To address this need, a topological navigation system for mining vehicles operating in tunnels is proposed and validated in this paper. This system was specially designed to be used by Load-Haul-Dump (LHD) vehicles, also known as scoop trams, operating in underground mines. An LHD is a four-wheeled, center-articulated vehicle with a frontal bucket used to load and transport ore on the production levels of an underground mine (See Figure 1). These machines are a key component in the extraction of ore from underground mines because the ore extraction rate from the mine depends directly on the efficiency of the LHD. The proposed system permits a commercial LHD, which is a very large vehicle, to navigate inside tunnels that are just a couple of meters wider than the LHD. In addition, it allows bi-directional navigation and so-called *inversion* maneuvers, both required in standard LHD operations inside productive sectors of underground mines. (See an example in Figure 2). In addition, a localization system, specifically designed to be used with the topological navigation system and its associated topological map, is also proposed.

**Figure 1.** LHD and sensors for the Autonomous Navigation System.

An important aspect to be addressed when working with heavy-duty machinery, such as the LHDs, is the way in which automation systems are developed and tested. In order to address this important issue, we use a development process for autonomous systems for mining equipment, which is comprised of the following four-stages: (i) development using specific simulation tools, which are fed with real data from mining environments, (ii) development using scale-models of the actual machines, (iii) validation and testing using real machines in test-fields, and (iv) validation and testing using real machines in actual mine operations. This development process was used for achieving autonomous navigation of LHDs. The proposed topological navigation and location systems were validated using a commercial LHD, during several months at a medium-scale sub-level stoping mine, located in the Coquimbo Region of Chile.

**Figure 2.** Example of *inversion* maneuver.

The main contributions of this paper are the following:


This paper is organized as follows: First, related work on autonomous LHD navigation is presented in Section 2. The proposed topological navigation system for LHD is described in Section 3. Then, in Section 4, the development and testing methodology is described, and in Section 5, validation results achieved in an actual mine are presented. Finally, conclusions of this work are drawn in Section 6.

#### **2. Related Work**

Autonomous navigation of LHDs has been a subject of scientific research since the late 1990s [2–4]. The main objectives have been to improve productivity and increase safety for the personnel, but simultaneously to benefit from reduced machine maintenance due to less wear of components. The theoretical development, and experimental evaluation, of a navigation system for an autonomous articulated vehicle is described in [4]. This system is based on the results obtained during extensive in-situ field trials and showed the relevance of wheel-slip for the navigation of center-articulated machines. In [5], one of the earlier industrial automation implementations is reviewed. It is mentioned there, that the use of automation in day-to-day operations offers flexibility and convenience for the operators. The development of what would become the first commercially available solutions for autonomous navigation of LHD machines followed shortly thereafter.

The work of Mäkelä [6] set the basis for the AutoMine software of the LHD manufacturer Sandvik, while the work of Duff, Roberts, and Corke [7–9] set a similar precedent for the software MINEGEM of Caterpillar. Only a few years later, the work by Marshall, Barfoot, and Larsson [10–12] configured what would become Scooptram Automation of the company Atlas Copco (now Epiroc). These companies have applied their automation solutions directly to their articulated vehicles [13]. Because of their commercial application, only the initial work on the development of the autonomous systems from LHD manufacturers is available in the literature. For instance, autonomous navigation from the manufacturer Sandvik is based on an absolute localization paradigm, i.e., it relies on odometry and detection of natural markers of the tunnel network [6]. Localization is achieved by taking a profile of the tunnel in a 5 [m] long section and comparing it to a known map. Caterpillar, on the other hand, initially based its system on mainly reactive techniques (wall following), in conjunction with a topological map with information about loading points, dump points, intersections, and other markers that are used in an opportunistic location scheme [9]. Finally, Epiroc made use of a hybrid navigation paradigm [11]. A set of behaviors was programmed under a fuzzy logic scheme to form the reactive part, while a higher level (deliberative) planner was used at intersections or open spaces.

Despite its success in delivering a product to market, research in autonomous navigation for underground tunnels continues to be a relevant topic. In [14], a review is given on the performance of automated LHDs in mining operations. It also mentions some issues and challenges that remain. The dynamic and highly variable nature of mining operations underlines the need for flexible and quickly deployable systems, features that earlier commercial solutions lacked [15,16]. These shortcomings are also known to LHD manufacturers, who continue to improve their systems [17].

Automation in mining is, most certainly, a widespread trend that has already shown corporate benefits, and it will continue to drive the modernization of the mining industry [18]. Cost, productivity, and safety are still the driving forces for investments in automated systems. Recent publications in the field also suggest the increasing interest of China in the application of automation technologies [19–21]. Particular attention has been paid to modeling, and control techniques.

The autonomous navigation system presented in this paper is based on topological navigation, and model predictive control (MPC). Underground mining environments have been shown to be suited for the extraction of features needed to build topological maps [22], and a mixture of topological and metric maps has been used successfully to map and navigate in large environments [23]. MPC has also been proven to perform well in the high-speed control of vehicles with nonlinear kinematics [24–26].

#### **3. Topological Navigation and Localization for LHD**

#### *3.1. General System Overview*

LHD are large vehicles used in underground mining. In this work a LHD model LF-11H, from the GHH Fahrzeuge manufacturer, is used. The vehicle's size is 9.71 [m] in length, 2.45 [m] in width, and 2.45 [m] in height. The LHD's navigation is based on the data provided by two laser scanners (2D LIDARs), one pointing towards the front of the machine, and the other pointing towards the back. For supervision and occasional teleoperation, two cameras give the front and back images to the control station [27]. All on-board processing is done on an industrial computer running Linux OS, while the direct machine control is handled by an internal PLC unit. The LHD and the hardware components mentioned above can be seen in Figure 1.

In order to navigate autonomously inside the network of tunnels of an underground mine a proper representation of the mine is required, in this case a navigation map that includes the topological structure of the mine, tunnels, and intersection, as well as LIDAR measurements. The LHD, therefore, builds a map of the operation area during the system setup, using measurements from LIDAR sensors, as it navigates the tunnels. This map is then linked to a topological representation, thus giving names to all the relevant locations, henceforth referred to as "nodes". The map, once built, allows for route planning and selflocalization of the machine. The latter is carried out by means of scan matching between current LIDAR measurements and the LIDAR landmarks stored in the map. The scan matching is implemented using the ICP (Iterative Closest Point) algorithm, and the final pose estimation is the result of applying a Kalman Filter.

Every time a new mission in which the LHD is requested to move to a target node in the mine is executed, a topological route composed of nodes is defined, and then target poses inside each node are calculated. Afterwards, the path required to reach each target pose is computed, and a reactive control algorithm is put in charge of following the path, keeping the vehicle away from colliding with the walls of the tunnel. A model predictive control strategy is implemented because of the complexity of following the path with this articulated machine, which normally moves at a speed of between 12 and 24 km per hour inside the mine.

#### *3.2. Topological Map and Physical Representation*

The main component of the representation of the mine is the "Topological Map" (TM). Two types of nodes are defined on this map: (i) tunnel nodes and (ii) intersection nodes. A tunnel node is a section of the mine that can be traversed back and forth, i.e., it corresponds to a single path or trajectory between two physical points. Topologically, a tunnel node has two edges, connecting it with two intersection nodes. An intersection node may have multiple edges that connect it to other nodes, and it can also traverse back and forth. An example would be a fork in the tunnel, where the vehicle must choose one of the possible path alternatives.

Each topological node contains a number of access points (APs) and waypoints (WPs), which are represented by a 2D pose within the node's coordinate system. APs connect different topological nodes; they signal the transitions between map nodes. WPs, on the other hand, correspond to specific poses within the topological node, each one containing relevant information for the navigation system, such as maximum driving speed. Examples of waypoints can be a location for dumping ore, a place to park the vehicle, or a location the vehicle must go through when traversing the node.

As an example, Figure 3 shows a portion of a real mine, where a TM has been created. In this scenario, four main tunnels, T6, T7, T9, and T10, and one intersection, I0 (in green), define the map. It can also be seen that T9 contains a number of waypoints throughout its path, shown by arrows.

**Figure 3.** Example of a topological map of a real mine.

Besides their associated 2D pose, APs and WPs have a defined heading. APs always face the outer side of the node they are in, but WPs always face in the predefined direction of the node. Because of this, APs and WPs can be connected in 4 different ways: front-tofront, front-to-back, back-to-front, and back-to-back (see Figure 4, the orange arrow is the default direction of the node). In Figure 5, a TM with 2 tunnel nodes, 1 intersection node, and its AP, WP, and the connections between them, is shown.

**Figure 4.** Types of connection between AP and WP.

**Figure 5.** TM example showing one intersection node (Intersection 1), two tunnel nodes (Tunnel 1 and Tunnel 2), and the corresponding APs, WPs and connections between them.

One of the problems that articulated vehicles face while traversing an underground mine is that they sometimes need to change their direction of movement, using a maneuver known as *inversion*. (See Figure 2). To tackle this problem, a Topological Movement's Map (TMM) is built automatically from the TM. To do this, for each AP and each WP in the TM, 6 nodes are created in the TMM. These 6 nodes represent how the vehicle would move through an AP or WP in the TM:


These new nodes in the TMM are then connected using the information on the topological map and a predefined set of rules that reflect how a vehicle would move between two different pairs of nodes in the TM. These rules are presented on Tables 1 and 2. Rules are slightly different when considering the special case of connecting APs from different TM nodes (see Table 2), because as these APs connect different TM nodes, they have the same physical location, and then connections between "stopped" nodes must be allowed.


**Table 1.** TMM's connection rules for connections within the same TM node.

**Table 2.** TMM's connection rules for connections between different TM nodes.


It is important to note that these connections are directional, and it is required to go through a "stopped" node ((v) or (vi)) to change the vehicle's direction of movement. Therefore, these rules must be applied in both directions of each connection. Nodes (v) and (vi), with the same origin AP/WP, are always connected. An example of the TMM creation from the TM of Tunnel 1 (in Figure 5) is shown in Figures 6 and 7. For clarity of purpose, only half of the connections are shown in each figure. In Figure 6, only connections in the direction of the tunnel are shown, while in Figure 7, only connections against the direction of the tunnel are shown. Each node in the TMM is represented by a blue arrow. Each node has an associated LHD heading and movement direction. The heading is represented by the LHD image and the movement direction by the orange arrow next to the LHD.

**Figure 6.** TMM construction example. Connections heading in the same direction of the Tunnel are shown.

**Figure 7.** TMM construction example. Connections heading in the opposite direction of the Tunnel are shown.

An example is shown in Figure 8 for the inversion maneuver. Relevant TMM nodes involved in the inversion movement are highlighted.

**Figure 8.** Representation of an *inversion* maneuver in the TMM.

Once the TMM is set up, topological paths can be calculated upon demand. When a new request is received, the vehicle's current state is pushed to the TMM as a starting point, and then the optimal route to the desired destination is calculated using Dijkstra's algorithm [28]. Traveling costs between TMM nodes are set up initially based on the distance between AP/WP, but can be fined-tuned with additional considerations such as: the change in the vehicle's movement direction, the feasibility of the movement (sometimes a curve is too sharp for the vehicle to turn in a certain direction), and unwanted paths for other reasons such as road repairs, narrow spaces, or uneven tracks.

Path planning using the TMM is extremely fast. Its execution in a very large mine layout was simulated in a layout similar to that of the new Chuquicamata underground mine, with eight parallel streets, each one having 19 intersections. Under these conditions, the TMM contained 5378 nodes and 8760 vertices, and path planning using the TMM took less than 5 [ms] on an i7 Intel processor [27].

Each node contains so-called LIDAR-landmarks that are stored when the map is built, and are used for the LHD self-localization. In regard to tunnel nodes, the LIDAR-landmarks correspond to LIDAR point clouds acquired at certain selected positions of the tunnel. In the case of intersection nodes, the LIDAR-landmark corresponds to an integration of the point clouds acquired inside the intersection.

Map Building—Determination of LIDAR-Landmarks

For map building the LHD needs to visit all the tunnels of the mine in one direction. In order to achieve this, the LHD is teleoperated. At the end of this process the LIDARlandmarks need to be determined.

The following procedure is followed for the tunnel nodes:

	- For each LIDAR-landmark:
		- - The values of similarity with other LIDAR-landmarks are ordered from lowest to highest.
		- - A delta threshold and a radius are defined:
			- The last LIDAR-landmark for which the next highest similarity value does not belong to one of the current LIDAR-landmark's local neighbors is selected (searching from lowest to highest similarity value).
			- This similarity value between the current LIDAR-landmark and the selected LIDAR-landmark is stored as the threshold of this LIDARlandmark. This value represents the minimum ICP's fitness value that must be met for a valid geometric fit.
			- The difference between the threshold and the next similarity value is stored as the delta threshold of this LIDAR-landmark. This value represents how different the current LIDAR-landmark is from the other LIDAR-landmarks that are not its neighbors.

If it is not near an existing LIDAR-key-landmark (farther than 5 LIDAR-landmark measurement distance) then it is added to the LIDAR-key-landmark list.

In the case of an intersection node, the LIDAR point clouds obtained in the intersection are integrated and stored as a single cloud point. This cloud point is the LIDAR-landmark of the intersection.

### *3.3. Self-Localization*

Self-localization is composed of two modules: a global topological localization estimation, and a local node localization estimation. The topological localization estimates the location of the LHD inside the TMM (and also the TM, because each node in the TMM has an equivalent in the TM), as well as the distance between its current location and the closest AP/WP. The intra-node localization estimates the localization inside the current tunnel or intersection node.

#### 3.3.1. Localization Inside Tunnel Nodes

Inside tunnels the LHD's pose is defined as the one-dimensional distance or trajectory *ρ*, measured from the beginning of the tunnel. This distance is updated incrementally as:

$$
\rho\_{t+1} = \rho\_t + \upsilon\_t \cdot \mathrm{dir}\_t \cdot \Delta t \tag{1}
$$

where *vt* is the current linear speed of the LHD, and *dirt* is the current direction of movement, with the value 1 meaning that the LHD is oriented to the tunnel orientation and −1 if it is not. The values of *ρt*, *vt*, and *dirt* are estimated using a standard Kalman filter. The filter is updated using two different observations sources: first, using the LHD wheel's odometry obtained directly from the LHD encoders, and second, using the LIDAR point cloud. In the latter case, the ICP algorithm is used to match the current LIDAR point cloud with the LIDAR-landmarks that are near the current LHD position and the key LIDAR-landmarks within 50 [m] of distance. The result of the ICP matching is the distance to the most similar LIDAR-landmark, which is then used to estimate the relative position of the LHD in the node's coordinates, and its orientation. Both the wheel's odometry, and the ICP-estimated position and orientation, are used in the corrective stage of the Kalman filter asynchronously, i.e., every time they arrive.

It is important to mention that the ICP matching is local, not global, meaning that the matching is made only with LIDAR-landmarks that are near the current position of the LHD. The main reasons for this decision are time efficiency and to avoid wrong matching due to the similarity that different LIDAR-landmarks may have. (Mining tunnels have regular surfaces and LIDAR-landmarks computed in different tunnels may be similar). This also prevents the self-localization from changing drastically in its localization estimation.
