Patent Application
20240012430
The present disclosure relates to the field of automated fleet management. In particular, the present disclosure relates to automated balancing of transport cycles in mass excavation operations.
Operations such as digging or blasting at a construction site often generate large amounts of waste material (such as dirt, rock, or similar) that need to be removed from the construction site and taken care of elsewhere, e.g. at a landfill, crusher or other dump site. The planning of such mass excavation operations is thus often a large part a construction project, and require proper scheduling and schedule-following in order to avoid unnecessary delays.
Mass excavation is often performed using various transport cycles, wherein dump trucks are scheduled to pick up material such as rock, dirt or similar at a construction (or load) site, and to deliver the material to a dump site where the material can be sorted and properly handled. After having made a delivery to the dump site, a truck may return back to the load site to pick up another load of material, thereby completing the transport cycle. In order to avoid driving back empty from the dump site, the dump truck can also be scheduled to e.g., on its way back to a main load site, stop at one or more other load sites to pick up construction material needed at the main load site. For example, an outbound journey from the main load site may include transporting blasted stone or rock to a dump site, while an inbound journey to the main load site may include picking up and returning materials such as gravel, concrete, or similar, from one or more additional load sites.
For larger and more complex construction projects, the number of dump trucks required to perform such mass excavation operations may become large, and the task of maintaining a constant flow of material from and to a main load site may quickly become overwhelming. If too many dump trucks simultaneously arrive at a same dump site, a limited capacity for material handling at the dump site may cause one or more of the dump trucks having to queue before being able to unload their transported material. Likewise, a limited capacity at the main load site may quickly cause a buildup of inbound dump trucks queuing to deliver new material to the main load site, and similar. As a consequence, imbalance in inbound and outbound dump trucks to the main load site may become both costly and have unwanted environmental impact, as dump trucks queuing in line may e.g. not always be able to shut off their engines. To compensate for such unwanted variations, and to make sure that e.g. a load site always has at least one dump truck ready to be loaded, conventional strategies may include e.g. an intentional overcapacity in terms of a number of dump trucks used, which may be both expensive and have further environmentally negative effects.
Due to the complexity of the mass hauling operations and the various transport cycles involved, a person (such as a production manager or dispatcher) may often end up being reactive instead of proactive, and often have to resort to commanding drivers of the various dump trucks directly via voice radio, in an attempt to resolve an already ongoing imbalance at the main load site. In addition, human behavior, such as bunching of vehicles, may cause a plurality of dump trucks having left a particular site well separated and according to a predefined schedule to anyway arrive together at a next site, especially before e.g. lunch, coffee breaks or end-of-day when the drivers e.g. want to spend time together or leave for home as early as possible, resulting in unnecessary waiting time due to queue-buildups at the various sites of a transport cycle.
To at least partially alleviate the above-identified issues in mass hauling operations, the present disclosure provides an improved method of (automated) transport cycle balancing, as well as a corresponding monitoring device, dump truck, material handling fleet, computer program and computer program product.
According to a first aspect of the present disclosure, a method is provided. The method is a method of (automated) transport cycle balancing and is performed in processing circuitry of a monitoring device. The monitoring device may for example for part of a fleet management system or similar. The method includes recurrently receiving first data indicative of movements of a plurality of dump trucks each operating in accordance with a transport cycle, where each transport cycle includes a planned hauling of material between a same first site (i.e. there is a same main load/unload site for all of the plurality of dump trucks) and at least one second site (which may be same or different for the various dump trucks depending on their respective transport cycles). The method further includes forecasting, based on the received first data, an imminent imbalance of inbound and outbound dump trucks for the first site. The method further includes determining, in response to forecasting the imminent imbalance, a mitigative action for at least a first dump truck of the plurality of dump trucks, in order to reduce of the imminent imbalance (actually) occurring. The method further includes communicating the mitigative action to the first dump truck.
As used herein, and as will be explained in more detail later herein, a “mitigative” action may e.g. be a command that a vehicle (or a driver thereof) must follow in order to avoid that the imbalance becomes a reality. For example, in some embodiments of the method, such an action may include e.g. to i) reduce a speed of the first dump truck, ii) increase a speed of the first dump truck, iii) temporarily stopping the first dump truck, and/or to change a planned route for the first dump truck. That the first data is received recurrently means that as the various dump trucks move around, updated information about their position and e.g. speed are fed to the monitoring device, such that e.g. the forecasting may be performed taking the updated information into account.
The solution of the present disclosure, as defined e.g. in the above method, improves upon currently available technology in that it allows to automate the task of keeping track of all dump trucks, and in particularly in that it uses the movements of the various dump trucks to forecast an imminent imbalance before the imbalance needing to actually occur. By determining the mitigative action before the imbalance has occurred, the imbalance may be avoided by the first dump truck (or e.g. the driver thereof) acting in accordance with the mitigative action. This thus removes or at least reduces the need for e.g. a dispatcher having to manually instruct each dump truck to take action, and in particular removes or at least reduces the need for actions as a response to an already occurring imbalance. By proactively forecasting the imminent imbalance, instead of first detecting and then reactively responding to an already ongoing imbalance, actions may instead be taken proactively to avoid the imbalance, which can help to reduce both environmental pollution (as the avoidance of queues makes the use of fuel for the dump trucks more efficient and of course also increases the overall production rates). As the mitigative action (or a set of mitigative actions) can be sent only to those dump trucks whose actions are considered most relevant for avoiding the imbalance, the other dump trucks (and the drivers thereof, if present) may continue their operations as normal without being disturbed by e.g. voice commands over radio intended for one or more other dump trucks (which is otherwise often the case when a dispatcher manager manually commands the dump trucks, as multiple dump trucks often share a same radio frequency). As used herein, that a particular truck (or the actions thereof) is relevant for avoiding an imbalance may e.g. include that the particular truck, if not taking further action, is likely to arrive too early to the first site, i.e. such that there will be no available capacity for either loading or unloading the particular truck once it arrives, or similar. Depending on the situation, it may also be such that all trucks (and their actions) are considered relevant for avoiding the imminent imbalance, in which case mitigative actions may be determined for, an communicated to, all trucks. In other envisaged situations, it may e.g. be only trucks involved in driving a same, particular transport cycle (i.e. between the same sites) whose actions are relevant to avoid the imminent imbalance, in which case mitigative actions may be determined for, and communicated to, only these trucks, etc.
In some embodiments of the method, communicating the mitigative action to the first dump truck may include sending a signal indicative of the mitigative action to a user device associated with a driver of the first dump truck. This may be advantageous as the driver may already have a suitable such user device, such as e.g. a phone, smartphone, tablet, or similar, available. The mitigative action may for example be communicated as a text message to the driver via the user device, or similar.
In some embodiments of the method, receiving the first data may include receiving, from the user device, a signal indicative of movements of the user device. The user device (such as e.g. a smartphone or tablet) may already be equipped with positioning equipment, such as e.g. a GPS or GNSS receiver, and such equipment may be used to provide the position and/or speed of a vehicle without having to install additional equipment to the vehicle. This because the user device, is e.g. kept within a cabin of the dump truck while driving, will have a substantially similar position, speed and e.g. acceleration as the dump truck itself.
In some embodiments of the method, communicating the mitigative action to the first dump truck may include sending a signal indicative of the mitigative action to i) a display device of the first dump truck (such that the mitigative action may be seen by the driver of the first dump truck, and/or to ii) an autonomous or semi-autonomous driving control system of the first dump truck. With option ii), the dump truck may thus act in accordance with the mitigative action without e.g. a need for the driver to interfere. In particular, in case there is no driver of the first dump truck (i.e. the first dump truck is a self-driving dump truck), this may be particularly useful as the process of transport cycle balancing may then be fully automated without any required human intervention once properly set up. The display device may e.g. be a screen on an instrument cluster/board of the dump truck, or on some other display provided within e.g. a cabin of the dump truck and visible to the driver. In other embodiments, such visual information may e.g. be complemented, or even replaced, by for example one or more audio signals which the driver is trained to interpret correctly, or similar.
In some embodiments of the method, the first site may e.g. be a main load site/main construction site, and the at least one second site of the transport cycle of the first dump truck may include both e.g. a dump site and an additional load site. For example, the first dump truck may be scheduled to both unload old material (from the main load site) and to pick up new material (to be used at the main load site) in order to complete its transport cycle. With such more complex transport cycles available, the use of the envisaged solution may be particularly beneficial.
In some embodiments of the method, the forecasting of the imminent imbalance may be based also on e.g. weather and/or traffic information. The method may then further include obtaining second data including such weather and/or traffic information pertinent to at least the transport cycle of the first dump truck.
In some embodiments of the method, forecasting the imminent imbalance may include using a machine learning algorithm which have been trained to, based on the first data, perform such a forecasting. If the weather and/or traffic information is also provided, the machine learning algorithm may be trained to also take such weather and/or traffic information into account during its forecasting of the imminent imbalance.
According to a second aspect of the present disclosure, there is provided a monitoring device (for transport cycle balancing). The monitoring device includes processing circuitry which is configured to cause the monitoring device to: recurrently receive first data indicative of movements of a plurality of dump trucks each operating in accordance with a transport cycle, wherein each transport cycle includes a planned hauling of material between a same first site and at least one second site (120); forecast, based on the received first data, an imminent imbalance of inbound and outbound dump trucks for the first site; determine, in response to forecasting the imminent imbalance, a mitigative action for at least a first dump truck off the plurality of dump trucks to reduce a risk of the imminent imbalance occurring, and communicate the mitigative action to the first dump truck. The monitoring device may thus be configured to perform the steps of the method of the first aspect. In some embodiments, the monitoring device may e.g. form part of a cloud-based solution, and be or e.g. be included as part of a computer server or computer server cluster, or similar.
In some embodiments of the monitoring device, the processing circuitry may be further configured to cause the monitoring device to perform any embodiment of the method of the first aspect as disclosed and discussed herein.
According to a third aspect of the present disclosure, there is provided a dump truck. The dump truck includes a positioning device configured to recurrently track (i.e. receive information about, from e.g. a GPS, GNSS, or similar, satellite) a movement of the dump truck. The dump truck further includes a wireless transmitter configured to recurrently send information indicative of the movement of the dump truck to a monitoring device according to the second aspect (or any embodiment thereof disclosed and described herein), e.g. as part of the first data received by the monitoring device.
In some embodiment of the dump truck, the dump truck may further include a wireless receiver configured to receive a signal indicative of the mitigative action from the monitoring device. The dump truck may further include circuitry configured to i) display the mitigative action to a driver of the dump truck (if the circuitry is or includes e.g. a display), and/or ii) to control a driving of the dump truck in accordance with the mitigative action (if the dump truck is e.g. an autonomous or semi-autonomous vehicle/dump truck, and where the circuitry is e.g. a control system for such a vehicle).
According to a fourth aspect of the present disclosure, there is provided a fleet for material hauling (e.g. mass excavation operations). The fleet includes a plurality of dump trucks (e.g. according to the third aspect or any embodiment thereof disclosed and described herein), and a monitoring device (e.g. according to the second aspect or any embodiment thereof disclosed and described herein), such that the monitoring device is configured to balance the transport cycles of the plurality of dump trucks by providing one or more mitigative actions in accordance with the method of the first aspect (or any embodiments thereof disclosed and discussed herein).
According to a fifth aspect of the present disclosure, there is provided a computer program for transport cycle balancing. The computer program includes computer code that, when running on processing circuitry of a monitoring device (such as the device of the second aspect, or any embodiment thereof), causes the monitoring device to: recurrently receive first data indicative of movements of a plurality of dump trucks each operating in accordance with a transport cycle, wherein each transport cycle includes a planned hauling of material between a same first site and at least one second site (120); forecast, based on the received first data, an imminent imbalance of inbound and outbound dump trucks for the first site; determine, in response to forecasting the imminent imbalance, a mitigative action for at least a first dump truck off the plurality of dump trucks to reduce a risk of the imminent imbalance occurring, and communicate the mitigative action to the first dump truck. The computer code is thus such that it causes the monitoring device to perform the steps of the method of the first aspect.
In some embodiments of the computer program, the computer code may be such that it causes the monitoring device to perform any embodiment of the method of the first aspect as disclosed and described herein.
According to a sixth aspect of the present disclosure, a computer program product is provided. The computer program product includes the computer program of the fifth aspect, and a computer-readable storage medium on which the computer program is stored. In some embodiments of the computer program product, the storage medium may be non-transitory.
Other objects and advantages of the present disclosure will be apparent from the following detailed description, the drawings and the claims. Within the scope of the present disclosure, it is envisaged that all features and advantages described with reference to e.g. the method of the first aspect are relevant for, apply to, and may be used in combination with also any feature and advantage described with reference to the monitoring device of the second aspect, the dump truck of the third aspect, the fleet of the fourth aspect, the computer program of the fifth aspect, and the computer program product of the sixth aspect, and vice versa.
Exemplifying embodiments will now be described below with reference to the accompanying drawings, in which:
In the drawings, like reference numerals will be used for like elements unless stated otherwise. Unless explicitly stated to the contrary, the drawings show only such elements that are necessary to illustrate the example embodiments, while other elements, in the interest of clarity, may be omitted or merely suggested. As illustrated in the Figures, the (absolute or relative) sizes of elements and regions may be exaggerated or understated vis-à-vis their true values for illustrative purposes and, thus, are provided to illustrate the general structures of the embodiments. On some occasions a reference numeral “ABC” will be used to refer to all objects of a same class, while a reference numeral “ABCn” (where “n” is a lowercase letter) will be used to refer to a particular object of the same class.
Various mass hauling scenarios in which the solutions of the present disclosure are applicable will now be described in more detail with reference to
How the present disclosure and the solutions thereof envisages to alleviate the issues with, in particular, scenarios such as 101 and 102 will now be described with reference also to
The device 200 is communicatively connected, via e.g. a signal connection 212, to a receiver/antenna 210 which receives, as part of wireless (e.g. radio and/or optical) signals 214a and/or 214b, first data about the movements of a plurality of trucks, including at least a first truck 131. The first data may e.g. be received as part of the radio signal 214a provided from the first truck 131 itself, and/or e.g. as part of the radio signal 214b provided from a user device 140 associated with a driver of the first truck 131. The first data may include e.g. coordinates and/or velocities (or speeds) of the various trucks, and the first data is provided, once received by the antenna 210, to the device 200 over the signal connection 212. The first data may, in some embodiments, also include e.g. a loading capacity, maximum/optimal speed, or similar, of the respective truck, or e.g. at least an identification of a truck, from which identification such capacities and/or speeds may be obtained from some other source.
The device 200 is also communicatively connected, via e.g. a signal connection 222, to a sender/antenna 220 which may send data indicative of a mitigative action (determined by the device 200, as will be described further below) as part of one or more wireless (e.g. radio and/or optical) signals 224a and 224b to plurality of trucks (including the first truck 131). This may include e.g. sending the mitigative action to the first truck 131 directly via a signal 224a to the truck 131 itself, and/or e.g. sending the mitigative action to the user device 140 of the driver of the first truck 131.
Optionally, the device 200 may also, in some embodiments, be communicatively connected to a first data storage/unit 230 in which various transport cycle details about planned routes, schedules or similar pertinent to the respective transport cycles of the trucks may be stored. This data may be accessed by the device 200 via e.g. a signal connection 232. In some embodiments, this data may also include details about the various trucks, such as their loading capacities, maximal/optimal speeds, or similar, in which case an identification of a particular truck may be used to obtain such details about the particular truck.
Optionally, the device 200 may also, in some embodiments, be communicatively connected to a second data storage/unit 240 in which weather and/or traffic data pertinent to the respective transport cycles (e.g. predicted weather along a scheduled route, predicted traffic information along the scheduled route, or similar) of the trucks. This data may be accessed by the device 200 via e.g. a signal connection 242.
A movement module 310 is provided as part of the device 200 and configured to receive, as part of e.g. the signal 212 and as part of a step S301 of the method 300, the first data indicative of movements of the plurality of trucks, including the first truck 131. Optionally, the movement module 310 may also be configured to receive the signal 232, including the transport cycle details described above. If present, the data of the signal 232 may be made to form part of the first data. In other embodiments, the movement module 310 may provide the data from the signals 212 and 232 as separate output signals. In any embodiment, the data is output (e.g. as part of the signal 312) to a forecast module 320. In some embodiments of the device 200, the movement module 310 may not be included and the data 212 (and e.g. the data 232) may then instead be provided directly to the forecast module 320.
A forecasting module 320 is provided as part of the device 200 and configured to receive the first data 312 from the movement module 310 and to, based on the first data and as a step S302, forecast an imminent imbalance of outbound and inbound trucks to a main load site (such as the load site 110). For example, the forecasting module 320 may be configured to calculate a probability that an imbalance will occur during for example a fixed future time interval. Forecasting the imminent imbalance may then correspond to e.g. the calculated probability exceeding a threshold value, or similar. The forecasting module 320 may e.g. take into account current positions of the trucks 130, current speeds of the trucks 130, current directions of driving of the trucks 130, as well as one or more corresponding historical values for such quantities for the trucks 130. The forecasting module 320 may provide, as part of e.g. a signal 322, an indication of the forecasted imminent imbalance. The signal 322 may for example also include part or all of the first data, e.g. positions and/or movements of the various trucks 130, and similar.
A mitigative action module 330 is provided as part of the device 200 and configured to receive the signal 322 and, as part of a step S303, determine (i.e. if the signal 322 indicates that an imbalance is imminent) a mitigative action for at least the first dump truck 131 of the trucks 130, in order to reduce a risk of the imminent imbalance actually occurring. The determined mitigative action is output, e.g. as part of a signal 332, from the mitigative action module 330.
A communication module 340 is provided as part of the device 200 and configured to receive the mitigative action signal 332 from the module 330 and to, as part of a step S304, communicate the mitigative action to at least the first truck 131. As described above with reference to e.g.
Optionally, a weather and/or traffic data module 340 may be provided as part of the device 200 and configured to, as part of an optional step S305, receive (as part of e.g. the signal 242 from the storage 240, or from any other entity in possession of weather and/or traffic data) weather and/or traffic data pertinent to at least the transport cycle of the first truck 131. The obtained/received weather and/or traffic data may be provided as second data to the forecasting module 320, as part of e.g. a signal 342. In some embodiments, the module 340 may not be included, and the weather and/or traffic data (i.e. the second data) may then e.g. be provided directly to the forecasting module 320.
In some embodiments of the method 300 and the monitoring device 200, the forecasting module 320 may implement one or more machine learning algorithms, which have been trained to, based on the first data (and optionally also based on the second data) forecast the imminent imbalance. The machine learning algorithm may for example be trained to output a probability of an imbalance being imminent (i.e. occurring within a future, predefined time interval, or similar). If the probability is e.g. determined to be above a certain threshold value, the forecasting module 320 may then output the indication 322 of the imminent imbalance. Such a machine learning algorithm may for example, in some embodiments, be implemented using an artificial neural network, wherein the person skilled in the art is assumed to know the basics of setting up such a network. For example, an input layer including a plurality of input neurons may be provided to receive e.g. the movements (i.e. positions and/or velocities) of the trucks 130, and in some embodiments to also receive e.g. the transport cycle details and/or the second data (i.e. the weather and/or traffic data). The network may include one or more intermediate layers of one or more neurons for processing and propagating the information provided to the input layer, and an output layer connected to the last such intermediate layer and configured to calculate a final output from the network. As mentioned earlier herein, this final input may e.g. be a probability or a binary indication (e.g. “yes” or “no”) of the imbalance being imminent. In other embodiments, the output layer may e.g. include several output neurons each providing a probability or binary indication of one or more classes of future forecasts. Examples of such classes may e.g. include that a transport cycle is balanced, imbalanced, or e.g. indefinite. Alternatively, the network may e.g. output (using one or more output neurons) a degree of balance for a transport route, such as e.g. “100% balanced”, “75% balanced”, . . . , “25% balanced”, “0% balanced”, or similar, or the opposite for a degree of imbalance instead.
As used herein, that inbound and outbound trucks are “balanced” does not necessarily mean that a number of inbound trucks is equal to a number of outbound trucks. As described earlier herein, determining whether there is balance or not may also include taking into account e.g. loading capacities and/or maximal/optimal speeds of the trucks, and optionally in combination with e.g. a material handling capacity at the main load site. For example, if the main load site 110 has a handling capacity of X tons/hour, a forecasted situation in which two trucks each having X tons of loading capacity arriving within e.g. less than an hour may be considered as an imbalance, as the last of these two trucks will likely have to wait/queue at the main load site before the first of these two trucks are fully loaded, or similar. A suitable mitigative action may then include e.g. instructing the first truck to speed up, and/or instructing the second truck to slow down, such that the two trucks are not expected to both arrive within an hour. As used in this particular example, the “first truck” may e.g. be the truck expected to arrive first at the main load site 110, while the “second truck” may consequently be the truck expected to arrive later than the first truck at the main load site 110. It should also be mentioned that as envisaged herein, a mitigative action may in some situations e.g. include that the first truck reduces its speed or even stops completely (and/or that the second truck increases its speed), in order for the second truck to pass the first truck on its way to the main load site 110. This may, for example, be beneficial if the capacity of the second truck better matches a current available handling capacity and/or need at the main load site 110, or similar. Other examples of mitigative actions are of course also envisaged.
In particular, determining whether there is an imbalance at a particular site may thus include to take into account also the handling capacities etc. of some or all other sites in one or more transport cycles visiting the particular site. Optimizing the movements of the various trucks may e.g. include taking into account a handling capacity at a next site (e.g. 120, 120a, and/or 120b) in a transport cycle after the main load site 110, which may be reflected in the “outbound” part of the balance/imbalance at the main load site 110. In one example, obtaining a balance of inbound and outbound trucks for the main load site 110 may e.g. correspond to a forecasted inflow rate of material (measured in e.g. tons/hour) matching an offloading capacity at the main load site 110, a forecasted inflow rate of load capacity (also measured in e.g. tons/hour, as provided by available loading capacity of the inbound trucks) matching a loading capacity at the main load site 110 (e.g. of an excavator, front loader, or similar, used to load the trucks), and e.g. a forecasted outflow of material (measured in e.g. tons/hour) at the main load site 110 bound for a particular next site in a transport cycle matching an offloading capacity of the particular next site, or similar.
In some embodiments, the definition of balance/imbalance at the main load site 110 may be simplified to include only that the numbers of inbound and outbound trucks at the main load site 110 are equal, or similar, which may be a suitable definition if e.g. all (or most) of the trucks 130 are similar in terms of load capacity, etc.
As envisaged herein, a neural network or similar may be used also to provide the various mitigative actions for the trucks 130. For example, such a network (which may be standalone, or form part of the network used for the forecasting) may be trained to, based on the first data, output one or more suggested changes in speed and/or route for one or more of the trucks 130, including the first truck 131. For example, the network may be trained to, for a particular current situation (i.e. particular current positions and/or velocities of the trucks 130) output one or more mitigative actions which would make the number of inbound and outbound trucks at the main load site 110 more balanced. For example, such an output may e.g. include for one truck to increase its speed with a recommended value, for another truck to reduce its speed with a recommended value, or e.g. for a third truck to stop for a recommended time or even change its planned route for arriving at the main load site (or its planned route for arriving at a next stop after having left the main load site), or similar. In particular, the network may determine that such changes are only necessary for some (or only one) of the trucks 130, and may provide no recommended changes for the other trucks. This may be advantageous in that e.g. only a first set of trucks which are recommended to change their speeds and/or routes may be communicated to/with, and such that a second set of other trucks may avoid being disturbed with information pertinent only for the first set.
A neural network used to determine mitigative actions may for example be provided with data representing a current situation of the trucks, including e.g. one or more of current positions of the trucks, current speeds of the trucks, current distances between trucks travelling along a same route, current/future available load capacity of the trucks, current/future available handling capacity of the main load site and other sites, or similar, and based thereon generate suitable mitigative actions for each truck, where a mitigative action may in this case also include to do nothing, e.g. to proceed without taking further action(s) for a particular truck. The training of the network may be performed in a similar fashion, and may use e.g. reinforcement learning or supervised learning. For example, the network may be provided with a first situation and propose one or more mitigative actions to improve the first situation. The proposed mitigative action(s) may be taken accordingly (i.e. in real life operations or in a simulated environment), leading to an updated, second situation. The network may e.g. be used in combination with the network for forecasting the imminent imbalance. By determining whether the second situation corresponds to a lower or increased risk of an imminent imbalance, the network responsible for determining the mitigative actions may thus be trained to improve itself, using e.g. backpropagation as commonly done in the art of neural networks. Similarly, the network responsible for forecasting the imminent imbalance may also be trained by recurrently being provided with e.g. a first situation, by making a forecast of whether a later, second situation will be imbalanced or not, and learn by comparing the forecast made with the actual outcome, i.e. with the second situation. As used herein, “training” and “learning” corresponds to an updating of at least one weight of at least one neuron used in the various layers of the neural network, as would be known to the skilled person. Other configurations and/or parameters as those listed above may of course also be used to obtain a same or similar functionality, as long as the network(s) may be trained to forecast an imminent imbalance, and to propose one or more mitigative actions suitable to prevent such an imbalance from happening.
As envisaged herein, a “mitigative action” to a particular truck may be communicated as part of e.g. a text message or similar, shown on either a smartphone of a driver associated with the particular truck, or e.g. on a display provided in the particular truck (as part of e.g. a dashboard of the particular truck, or similar). For example, if forecasting that there is an imminent imbalance which, if not attended to, will lead to the situation 102 depicted in
In case of e.g. traffic jam or bad weather conditions, a mitigative action may e.g. include for a particular truck to change its planned route, in order to avoid e.g. queues caused by an accident or similar. A text message may then include e.g. an instruction for a truck to change its route such that the truck passes position Y before arriving at the next site, or similar. Instead of a text message, such a route change may instead, or in addition, be communicated as a plurality of points defining a suggested new route, or similar. Instead of communicating the route itself, the mitigative action may instead include only the waypoints necessary for e.g. a route planner of the truck to on its own calculate the new route, or similar.
The present disclosure also envisages to provide a dump truck, which will now be described in more detail with reference also to
Optionally, the truck 400 may also include a receiver/antenna 440, which is configured to receive, as part of a wireless signal 224a, an indication of a mitigative action from the monitoring device, and to communicate this mitigative action to the controller 410 as part of e.g. a signal 442. The truck 400 may include e.g. circuitry 450 configured to display the mitigative action (in response to e.g. a control signal 414 from the controller 410, or similar) to a driver of the truck 400 (where the circuitry 450 is then e.g. a display or other optical device, such as a screen on/off a dashboard of the truck 400 or similar). The truck 400 may also be an autonomous or at least semi-autonomous vehicle, and may then include circuitry 460 configured to (in accordance with a control signal 416 from the controller 410) control a driving of the truck 400 in accordance with the mitigative action. In this case, the circuitry 460 may e.g. be or form part of a control system for such (semi-) autonomous driving of the truck 400. The truck 400 may e.g. be equal or similar to one or more of the trucks 130, and in particular be equal to the first truck 131.
The present disclosure also envisages to provide a fleet for material handling, as will now be described in more detail with reference also to
The fleet 500 includes a plurality of trucks 400a-n, each similar or equal to e.g. the truck 400 described with reference to
With reference to
Particularly, the processing circuitry 610 is configured to cause the monitoring device 200 to perform a set of operations, or steps, such as the steps S301-S304 as disclosed above e.g. when describing the method 300 illustrated in
The storage medium 620 may also include persistent storage, which, for example, can be a memory in form of any single or combination of magnetic memory, optical memory, solid state memory or even remotely mounted memory. The storage medium 620 may thus provide non-transitory storage, storing computer-readable instructions for the processing circuitry 610.
The monitoring device 200 may further include a communications interface 630 for communications with other entities and objects, in order to e.g. receive/obtain one or more of the signals 212, 232 and 242, and to e.g. output one or more signals such as the signal 222. The interface 630 may also be used to receive other information about e.g. a dump truck in which the device 200 is included as part of. The communication interface 630 may include one or more transmitters and receivers, including analogue and/or digital components, and may utilize e.g. one or more wired and/or wireless connections for this purpose.
The processing circuitry 610 controls the general operation of the monitoring device 200 e.g. by sending data and control signals to the communications interface 630 and the storage medium 620, by receiving data and reports from the communications interface 630, and by retrieving data and instructions from the storage medium 620. The monitoring device 200 may of course optionally also include other components, here illustrated by the dashed box 640. A communication bus 650 is also provided and connects the various modules/units 610, 620, 630, and 640 (if included), such that they may communicate with each other to exchange information.
In general terms, each functional module (such as modules 601-604) may be implemented in hardware or in software. Preferably, one or more or all functional modules may be implemented by the processing circuitry 610, possibly in cooperation with the communications interface 630 and/or the storage medium 620. The processing circuitry 610 may thus be arranged to from the storage medium 620 fetch instructions as provided by a functional module (e.g. 601-604), and to execute these instructions and thereby perform any steps of the method 300 or any other method envisaged herein, performed by the monitoring device 200 as disclosed herein.
The present disclosure also envisages to provide a computer program for transport cycle balancing as described herein. The computer program includes computer code that, when running on a processing circuitry of a monitoring device (such as e.g. the processing circuitry 610 of the monitoring device 200 described with reference to
The present disclosure also envisages a computer program product (not shown) in which the above envisaged computer program is stored or distributed on a data carrier. As used herein, a “data carrier” may be a transitory data carrier, such as modulated electromagnetic or optical waves, or a non-transitory data carrier. Non-transitory data carriers include volatile and non-volatile memories, such as permanent and non-permanent storage media of magnetic, optical or solid-state type. Still within the scope of “data carrier”, such memories may be fixedly mounted or portable. In general, a “data carrier” may be a computer-readable storage medium, such as e.g. the storage medium 620 of the monitoring device 200.
Although features and elements may be described above in particular combinations, each feature or element may be used alone without the other features and elements or in various combinations with or without other features and elements. Additionally, variations to the disclosed embodiments may be understood and effected by the skilled person in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.
In the claims, the words “comprising” and “including” does not exclude other elements, and the indefinite article “a” or “an” does not exclude a plurality. The mere fact that certain features are recited in mutually different dependent claims does not indicate that a combination of these features cannot be used to advantage.
In summary of the present disclosure, it is provided an improved way of monitoring the movements of trucks performing mass excavation operations, and in particular where the trucks operate according to one or more transport cycles between a main load site and one or more additional sites (such as dump sites and/or additional load sites). In particular, the improved way includes to forecast an imminent imbalance of inbound and outbound trucks for the main load site, and to determine and communicate mitigative, proactive actions which can be taken by (drivers of) the trucks to avoid the forecasted imbalance occurring at all. As a result, unwanted queuing at load and dump sites may be avoided, leading to reduced fuel consumption and an overall reduced negative impact on the environment, while also improving overall productivity.
| Number | Date | Country | Kind |
|---|---|---|---|
| 22183605.9 | Jul 2022 | EP | regional |
