Underground mine traffic management and
optimization: an intelligent application based on a
real-time localization system
N.Varandas, F.Cordeiro, H.Almeida, N.Sousa, P.França, A.Cruz, J.Oliveira,
Eneida Wireless & Sensors SA, PORTUGAL
ABSTRACT
We present an innovative technological solution for traffic management and optimization in underground mines. This solution starts with a real-time localization system installed in the mine that is able to compute the localization of all vehicles at all times. This global localization data is then centralized at one server and processed by an intelligent application that is able to decide the optimal color configuration for a set of traffic lights placed at specific control points along the mine. The main innovation of our solution is that it is able to do traffic optimization, i.e. to minimize the travel time of all vehicles while assuring specific requirements of each particular mine, which can lead to a significant production increase and costs reduction. In addition to this, the solution includes what other solutions in the market have been offering so far: traffic safety, visual tracking of vehicles, and many other assets and people tracking/management tools that are constantly being upgraded.
INTRODUCTION
One of the most difficult tasks the mine industry has to deal with is the control and protection of its assets and workers. In the critical and risky environment of underground mines, accidents involving vehicles and/or people are very common; leading to higher costs with vehicles maintenance, decrease in production/productivity caused by inoperability of damaged vehicles, and, the worst case of all, workers physical injuries [3]. Still today, many mines in the world perform many of these control/protection tasks in a manual and human way: for example, having staff within the mine dedicated to the traffic control of vehicles. Not only is this a very error prone approach, as it also consumes human resources that could be used in more efficient ways [1].
Fortunately some new technological solutions are arising that promise to completely automatize these kind of tasks [2]. These solutions generally consist of a localization Wireless Sensor Network (WSN) that is installed within the underground mine, which includes small tags attached to assets and people. This WSN enables the computation of the location of every asset or person by a software that appropriately presents all that
information to an operator while it also offers many functionalities regarding the control and management of assets and people.
One especial application of these systems is to the traffic control of vehicles [4]. The WSN enables the localization of vehicles by local hardware units that are connected to nearby traffic lights and can decide the appropriate color of the latter based on the localization information. Because these kinds of solutions use the localization data only locally, they are able to guarantee just traffic safety, i.e. avoid collisions.
Our solution, presented in this paper, goes one step further: traffic optimization. By optimization we mean decreasing the travel time of all vehicles while also assuring specific requirements of each mine. For example, at an Iberian Peninsula underground mine, our solution is being used to optimize the travel time of the route “mine entrance – loading points – mine exit (= mine entrance)” while also giving priority to loaded vehicles so that these never have to stop once they start their way up.
We are able to achieve such a solution because our WSN sends all localization data to a central server that hence has the information about the position of all vehicles in the mine at all times. This global information is then treated by a smart application with especial traffic rules that are able to decide at every instant the optimal color of all traffic lights within the mine. These rules work on the basis of “safe prediction” and take into account some physical parameters of the mine like distances between control points and information related to the relative velocity of vehicles, which can be configured by the responsible mine personnel. The prediction is safe because even if something unexpected happens, like a driver exceeding the speed limit or crossing some previous red light, the system is always able to ultimately activate its “local safe mode” in order to guarantee traffic safety.
With the information about the positions of all vehicles and the referred physical parameters, we are theoretically able to do traffic optimization for every mine and for any special additional traffic requirements. Indeed this information enables us to make predictions about the route of each vehicle, so that we can base our traffic rules on the following lemma: show a red light only when the prediction process results in a situation
that is unsafe or that violates any of the especial requirements of the mine.
It is important to stress that our system is also protected against any hardware or software fails that might occur and can cause the normal connection software-traffic lights to be lost. This is achieved by giving local intelligence to the system, i.e. the local hardware units (gateways) that are connected to the traffic lights are set with basic traffic rules that will be automatically applied in case any such fail occurs, thus guaranteeing traffic safety at all times.
The application also has a graphical interface that enables mine operators to: track vehicles on a mine map, receive relevant system alerts (“vehicle X ran a red light”, “vehicle Y is going in opposite direction”, etc), apply system changes on real -time (disable/enable vehicles, control points, traffic lights, etc), and also (re)draw the mine map and its components at any time.
In a near future we will attach tags also to people and other assets, and add various new functionalities to the application related to assets and people tracking, control, process optimization and safety. Our aim is to construct a complete technological solution, indispensable in every mine of the world, able to help improving production goals, reduce costs with fuel and assets maintenance, and increase safety.
The paper is organized as follows. In the section “The Underlying Real-Time Localization System” we describe the various components of the WSN hardware, their functions and how they are connected and installed within the mine. The section “Traffic Control Software” is dedicated to the description of the software: its various layers with focus on the user interface, the traffic rules, and the way the software communicates with the WSN and the traffic lights. In the final section, “Additional Value-added Services & Products”, we describe the present and future add-ons we want to give to the system.
THE UNDERLYING REAL-TIME LOCALIZATION SYSTEM
Technology and Equipments
Our traffic management and optimization software needs real-time information about the position of all vehicles inside the mine. In order to get this information we install at the mine a Wireless Sensor Network (WSN) consisting of mobile tags and fixed readers. Each vehicle has an active RF tag with a unique ID (left image on Figure 1) that is sending a signal periodically (each second for example). These signals are transmitted in the free license ISM band of 433MHz (which can be easily adapted to 915MHz). This band was chosen because of its propagation characteristics: it has a range bigger than other frequently used bands and behaves well in face of obstacles.
Along the walls of the mine, at strategic locations, are installed a number of fixed readers (center image on Figure 1) that measure and identify the individual tags. When a certain tag is detected by a given reader, we know that it must be in the zone defined by the range of that reader.
Figure 1: Eneida WS products. Left image: Tag EWS u433MV; Center image: EWS Reader; Right image: EWS GIP-pwr.
The detection information is then sent by each reader to a local gateway (right image on Figure 1). The latter aggregates a short number of readers and processes the information sent by them before relaying it to the traffic management and optimization software. Each gateway is also connected to a number of nearby traffic lights and is responsible for setting their color according to the “decision information” sent by the software. The gateways are also set with basic traffic rules and are ready to apply them if any unexpected fail causes the connection to the central server to be lost. This way traffic safety is always assured even if the main system (responsible in particular for traffic optimization) fails.
Installation inside the underground mine
The installation of the WSN inside the mine is an important process that should be carefully planned since there are many factors that can have a significant influence on the performance of the localization system. Most importantly, one must decide the most appropriate network topology to use. This depends much on the specific morphology of the mine and the localization precision one wants to have.
First one needs to decide the location of the control points. These are the places where we are going to install the traffic lights, and are generally the most critical points of the mine with regard to the traffic behavior. For example, control points should be placed at crossroads and at retreat areas that enable the crossing of vehicles on opposite directions in a tight mine road.
After defining the location of the control points, one must decide the number of readers to install at each of them. A normal situation would be to have 2 readers, one before the traffic light and another after it, but more readers can be added depending on the specific situation and the localization precision one wants to have: more readers lead to a bigger number of independent detection zones and therefore to a better localization precision. The readers installed near each control point are generally connected to only one gateway unit, i.e. we have one gateway per control point. However more gateways can be added if necessary.
After this we must define the location of the readers, or equivalently the distance between the readers and the control point. In doing this, one can choose between a bigger location precision around the control point (readers more close to the control point) and a more equally distributed precision along the mine (readers further away from the control point). The range of the readers is also a fundamental factor here. This is defined by choosing an appropriate power transmission for the tags: larger power transmissions lead to larger ranges.
Additional gateways and readers can also be added at points in between two control points in order to increase the location precision, especially if the distance between control points is very large.
A better localization precision will lead also to a better degree of traffic optimization. However, after some point the gain in traffic optimization may not justify the bigger cost of installing a larger number of readers and gateways. Generally, that is the point at which we stop adding readers to the system.
Our solution has been installed at a certain Iberian Peninsula underground mine. Figure 2 shows the topology of the installation made in a specific part of that mine. The image is taken from the associated mine map which is included in the user interface of the software application discussed at the next section.
Figure 2: Example of the WSN topology at a certain Iberian Peninsula underground mine.
The image represents a tight mine road where vehicles in opposite directions have no space to pass each other, unless they use the retreat (parking) areas represented by the grey squares. We have placed a control point at each retreat, with two traffic light devices, each one facing one direction. Through the traffic light devices, our solution will optimally decide if a vehicle coming in a certain direction will have to stop at a retreat or not, depending on the localization information about the other vehicles. The distance between retreats or control points is in average 300m, and we have placed two readers at each of them, one at the left and the other at the right, both at a distance of 60m from the center of the retreat. The numbers .101, .102 and .103 are indications of the IP number of the gateways installed at each control point. The small indices shown near the retreats represent their present occupation, and only one vehicle is allowed per retreat at each time.
The ellipses in the image represent the ranges of the readers, their center being the location of the readers themselves. We have set these ranges to less than 60m (i.e. the horizontal width of the ellipses is less than 120 m) by adjusting the tag power transmission to 0 dBm.
Notice that, with this configuration, the ranges of the readers do not intersect. This means that some zones are defined by the non-existence of detection together with the information of the direction of the vehicle and the last zone where the vehicle was detected.
TRAFFIC CONTROL SOFTWARE
Architecture of the application
The development of this application brought us some needs that could not be satisfied by the common 3-Tier Architecture. This is mostly because of the large number of clients that can be connected at the same time and with the information being synced. Also, our mine definition has to be cohesive, this means, at any given moment we are able to navigate to any previous state of the mine, with all the elements with the same state, with the minimum effort.
So, to achieve this we broke our data source in two parts. A XML type part where we store the mine definition, custom behaviors of the application and custom traffic rules (discussed below). The other entities are stored in a regular Database format. We also delegated the responsibility to the Data layer of converting the XML definition into an object model that can be used and known by the other layers.
The Business layer groups the data provided by the Data layer and collected from the gateway units from the physical WSN. With this information, it is capable of making decisions that may consist in ordering the Data layer to store data (logs, changed values), apply custom traffic rules defined in the XML, change the state of any connected hardware (traffic lights and gateway units) or show various types of alerts to the user.
The Service layer has the job of controlling the instance of the Business layer, and opens the possibility of multiple clients to connect to it and access to the same information. Each client is required to subscribe to the information that he wants to look at and the Service will keep feeding him the updates until he unsubscribes.
The Presentation is the least (because it doesn’t really need to exist for the system to work) and at the same time the most significant layer since it is the bridge between the system and the user. It is thought to be the simplest but at the same time be able to show the most information possible and allow the user to control the components. All of this needs to be taken into account when we want to provide a fast and efficient way to show all the data transformed. This layer shows the structure of the mine, the hardware installed and its state, alerts, logs and travel times.
Figure 4 bellow show us a little of what can be seen on our application and what it can do for us. On top, we can select the way it should behave. The Automatic mode runs the traffic rules and lets them control the state of the traffic lights according to the localization information. In other words, it is the online mode. In the Manual mode, the user is allowed to change the state of the traffic lights by clicking on them. In the Emergency mode, all the traffic lights enter the emergency state.
The black area contains the various elements that can be found in the mine. The small dots represent the place where the gateway units are installed and on top of them the name or address of the given gateway. The larger ovals represent the range of the tag readers, their center being the position of the tag readers themselves. The entrance contains a different label so it’s more identifiable. The Plant Outside is a place where the vehicles outside the underground mine are allocated. We can also identify trucks, traffic
Presentation Services Business Data XML Database GIP
lights and squares. The latter ones represent retreat or parking areas, and are the only places where vehicles in opposite directions can pass each other. Each retreat has a certain limit of storage of vehicles that it can hold which in this case is only one. The small index near each retreat represents the current/maximum occupation. At the end we have a representation of a Loading Point. When crossing this point the vehicles are loaded and their direction changes.
We also have data grids containing information about the vehicles and alerts. On the left we can find the identification of a vehicle, when he entered the mine and how long his latest travel lasted. A vehicle can also be marked as disabled, meaning that it is not expected to be detected. This may happen for example when a vehicle is broken and temporarily stopped. On the right we can see the most relevant alerts, they can be of 3 types of severity: Information, Warning and Critical. The user can manage these alerts. He can ignore\remove the ones that no longer mater. If a critical alert is received, the application background changes to a defined color in the XML (for example red) until it is removed.
Figure 4: The software user interface.
The other tabs hold logs and status information. The logs tab shows the messages received and sent to the gateways. The gateways tab, allow us to check the gateways status and to change some settings.
A Centralized Communication System
In the following section we will be speaking about the algorithm/rules to optimize traffic. But even before any rules, traffic optimization is possible only if we have a global knowledge of the system, i.e. knowing the position of all vehicles in the mine at all times. Indeed, traffic optimization consists in minimizing the overall stopping time of every vehicle, and this is a global condition on the system.
In order to have such knowledge we must therefore centralize all localization data at one place so that it can be used by the software application as desired. So, in our solution, each gateway in the mine sends the localization information via TCP/IP to a database in a central server. This database is then used by the upper layers of the software as described in the last section. In the last stage of the process, the software application computes de color of each traffic light and sends this information back to the gateways via TCP/IP. The latter units are connected to the traffic lights and have then the ultimate job of setting them with the color computed by the software.
Figure 5: Communication stages, from readers to traffic lights.
Other systems on the market use the localization data only locally, i.e. each gateway uses just the localization data sent by its readers in order to define the color of its traffic lights. Although such systems can provide traffic safety, they can never provide traffic optimization since they have no “global vision” of the localization data.
In order to be failing safe, our system also provides the gateways with local intelligence, i.e. sets them with basic traffic rules. This way, if any unexpected fail occurs that makes the connection to the central server to be lost, the gateways are ready to automatically assume the control of the traffic lights, thus guaranteeing traffic safety at all times and circumstances. Therefore, one can see our system as a block pyramid where the upper
block “Traffic Optimization”, which is the innovation of our system, is constructed upon the more basic block of “Traffic Safety”.
Figure 6: Block pyramid representation of our system. Traffic safety is always assured even if the connection to the server is lost.
Intelligent Rules: Traffic Management and Optimization
As already mentioned above, the main innovation of our solution, when compared to other solutions in the market, is the ability to make traffic optimization. Indeed, we have developed smart traffic rules that are able to process the overall localization data and decide the optimal color of all traffic lights, in such a way that the travel time of all vehicles is minimized while assuring specific requirements of each particular mine (for example, a certain kind of vehicles can have priority over others).
The traffic rules that guarantee traffic safety are relatively easy to implement. Basically one just has to set the traffic light to red if a vehicle with priority coming from a different direction is detected after the traffic light. In the case of traffic optimization, however, the rules become far more complex. First, the travel time of vehicles being minimal is necessarily a global condition on the system, so that the positions of all vehicles must be known at all times. This is possible to know in our case because we centralize the localization data as described in the last section. Second, we also need to take into account with some physical parameters of the system, namely the distances between traffic control points and an indication of the relative velocity of vehicles. These physical parameters can be configured in the XML.
With these two types of information (positions of all vehicles and physical parameters), we are theoretically able to do traffic optimization for every mine and for any special additional traffic requirements. Indeed, this information allows us to be constantly making predictions about the route of each vehicle. So, for example, when we have vehicles coming from different directions, we can always predict which one reaches a certain control point first. Therefore, our philosophy is the following:
If we are able to predict the routes of the vehicles, then we just show a red light when the prediction process results in a situation that is unsafe or that violates any of the especial traffic requirements of the mine.
The prediction process assumes that there are no “system violations”, like a vehicle exceeding the velocity limit, traveling too slowly due to some sudden malfunction, or even crossing a red light. However, this prediction process is always completely safe because, even if any system violation happens, the process is always prepared to ultimately activate its “local safe mode”, i.e. the standard traffic safety rules. In the latter case, we may even demand a priority vehicle to stop in order to avoid a collision, but safety is always assured.
So basically, our application consists of a standard traffic safety process with an additional traffic optimization process working on the top of it.
As already referred, we have our system installed at an Iberian Peninsula underground mine. The map of part of the mine is represented in Figure 4 of the section Architecture of the Application. The especial requirements of this mine are the following:
1. The entrance and exit of the mine, to and from the loading points, is the same. This entrance/exit ramp is roughly 3.5km long but only has enough width for one vehicle. The crossing of vehicles in opposite directions can only happen by means of a number of retreat areas that exist along the ramp, approximately equally spaced at a distance of 300m from each other. These correspond to the grey squares in Figure 4.
2. Loaded vehicles that want to leave the loading areas towards the exit of the mine must always have priority, i.e. once they start their way up they must never stop. We have customized our application and traffic rules to this situation. This means we were able to minimize the travel time of the route “mine entrance – loading points – mine exit (= mine entrance)”, while always giving priority to loaded vehicles so that they never have to stop once they leave the loading area.
We have defined control points at the retreat areas and placed traffic lights at each control point: one traffic light pointing up (TLUp), i.e. controlling unloaded vehicles going down, and one traffic light pointing down (TLDown), i.e. controlling loaded vehicles going up, as shown in Figure 4.
Figure 7 shows a situation where the vehicle going down (i.e. to the right) is shown a green light even though there is a loaded vehicle coming up in the way. Since the vehicle going down is predicted to reach the next enabled retreat before the other vehicle, we can let it go and show it a red light just at the next retreat, thus minimizing its overall stopping time. The knowledge about the distances between retreats and the relative velocities of the two vehicles, enable us to make such prediction.
Figure 7: A simple example of traffic optimization. The vehicle on the left is shown a green light because we are able to predict, according to the mine physical parameters configuration, that it will reach the retreat .102 first than the loaded/prioritary vehicle on the right.
It is important to stress that the prediction is always made considering extreme situations, so that normal scenarios always lead to correct predictions. For example, in the case of
Figure 7 , we are taking the extreme situation where the loaded vehicle going up has the same velocity of the vehicle going down, i.e. the relative velocity is zero. In any case, the
extreme relative velocity can be configured by the mine software operator and be different at different areas of the mine. If for example, the chosen configuration is that the extreme relative velocity is less than zero, i.e. the loaded vehicle going up can have a velocity greater than the vehicle going down (which is a much more preventive configuration), then the vehicle going down is shown a red light and has to park at the retreat .101.
In Figure 8, we show the same situation as above, but now there is no space for the vehicle going down at the next retreat, therefore it is shown a red light.
Figure 8: The vehicle on the left is shown a red light because there is no space for it at the next enabled retreat. Although, at this mine, retreats have capacity for only one vehicle, the traffic rules can be applied to arbitrary retreat capacities.
As for the traffic lights TLDown, they are supposed to work as the ultimate traffic safety guarantee. These traffic lights show a red light if there is any vehicle going down at the ramp branch right after them. Therefore, if any of the system violations referred above occur, these traffic lights will order the vehicle going up to stop in order to avoid any accident. For example, in Figure 9 a vehicle going down has crossed a red light at the next enabled retreat (now we are going up, or left on the image). Therefore we must prevent any vehicle going up to enter in the next ramp branch where the transgressor vehicle is, by showing a red light to the former. In this situation, the loaded vehicle is ordered to go to the retreat and wait until the other vehicle passes by.
Figure 9: The vehicle on the left has crossed a red light and therefore, in order to avoid any accident, we have shown a red light to the loaded vehicle on the right and sent it to the retreat.
The only TLDown that works differently is the one after the loading area. Indeed this traffic light is responsible for telling when the loaded vehicles can enter on the ramp to exit the mine. The rules for this traffic light are the most complex ones since they need to automatically select certain critical zones of the ramp which depend on the distribution of enabled and disabled retreats. If there are not vehicles going down at these zones, then
the light is set to green. Otherwise it is set to red.
Figure 10 shows a situation where the light is set to red. This happens because in that situation the vehicle going down would not eventually reach the next enabled retreat before the vehicle going up if we would let the latter go at that moment (assuming for example a zero relative velocity and equal distances for the two vehicles). Two of the retreats have been disabled, which makes the distance to the next enabled retreat longer. Notice also that the last branch of the ramp is by definition a critical zone, since vehicles going down have no more retreats after this point.
Figure 10: The last TLDown shows a red light because there is a vehicle going down in a critical zone. The traffic rules are able to compute all such critical zones and can re-adapt the computation automatically even if some retreats are suddenly disabled/enabled.
It is important to stress that the operation of enable/disable retreats, or even vehicles, can be taken on the fly, and our rules are prepared to readapt automatically to those changes. The Loading Point shown in the figure represents a very large area of the mine where there exist various loading points. Very shortly we will be adding each individual loading point to the mine map, and extend these rules to that case.
These rules are the most natural approach to minimize the overall stopping times of each vehicle along its route, and therefore to optimize traffic globally.
ADDITIONAL VALUE-ADDED SERVICES & PRODUCTS
The current system architecture has been developed in such a way that it enables the introduction of additional value-added services/products without changing the base system platform (eneida products and management software).
The decision to start with the implementation of a Traffic Management Solution is associated to the high return on investment (payback below 12 months) related with the production efficiency and maintenance costs reduction. After installing the required
components for this solution, the effort/investment needed to complement it with new services is quite low.
The system platform enables the implementation of the following additional services: Personnel and Asset Tracking;
Proximity Detection within vehicles and vehicles/persons or other assets; Assets Control & Monitoring:
o On-line monitoring and control of electrical engines (motors, pumps and ventilators);
o On-line monitoring of Distribution Transformers (DT’s); Fuel Tracking.
All these require the installation of specific products (according to Table 1) being potentially visualized within the same software by applying specific filters.
Table 1 : Additional services and the eneida products required to install them. The software application to visualize all data remains however the same.
The (1) technology – 433MHz communication network; the (2) concept – centralized intelligence and control; and the usage of (3) common equipment’s for various solutions; are what distinguish eneida portfolio from its market competitors.
Our aim is to construct a complete technological solution, indispensable in every mine of the world, able to help improving productivity (mineral output), reduce costs with fuel and assets maintenance, and increase safety.
ACKNOWLEDGEMENTS
This work has technology trasferred from EPRIL – a project co-financed by QREN, on the scope of Mais Centro – Programa Operacional Regional do Centro, and European Union (QREN IDT Nr. 24842/2012).
LITERATURE
[1] Viewpoint: perspectives on modern mining, 2010, issue 7; a publication of Caterpillar Global Mining.
[2] Mining Association of Canada, 100 innovations in the Mining Industry; Library and Archives Canada, 2012.
[3] Deloitte; Tracking the trends 2013; the top 10 issues mining companies may face in the coming year; Deloitte Design Studio, Canada. 12-3013
[4] http://minesite.net/news/intelligent-traffic-control-systems/
