Robot and system for robotic analysis of samples

Abstract

An in-situ sampling system, and a robot for analysing rock is provided. The robot includes: one or more drive elements, configured to drive the robot; one or more sensors, configured to generate sensor data relating to the rock; a processor, configured to process the sensor data to generate sample characteristic data corresponding to one or more characteristics of the rock.

Description

ROBOT AND SYSTEM FOR ROBOTIC ANALYSIS OF SAMPLESTECHNICAL FIELD

[0001] The present invention relates to robotic analysis of samples. In particular, although not exclusively, the present invention relates to in-situ, robotic analysis of rock or ore samples in mining.BACKGROUND ART

[0002] Sample analysis is an important part of mining operations. In many cases, samples are collected from various locations, taken to a laboratory, and analysed. This is generally performed periodically, and is useful in characterising mineral deposits, and to help in planning and decision making in mining operations.

[0003] Often mines have areas where assays are routinely taken to get an overview of the grade of the rock currently in that area. For example, in a copper mine, it is desirable to know what rate of copper is in rock in each area, and to monitor changes in that rate over time.

[0004] To achieve this, a person typically enters the mine, and collects rocks for analysis. The rocks are placed in bags, which are labelled with information regarding where the sample was taken. The bag is then transported to a lab, where the stone is crushed and analysed for grade.

[0005] A problem with such approach is that mines are generally dangerous areas. As a result, there is a health and safety risk associated with persons entering the mine to obtain samples therefrom.

[0006] A further problem with such approach is that there is a delay associated with the remote processing of samples in a laboratory. Yet further again, such remote processing requires careful managem ent of samples after collection to ensure that the data is appropriately mapped to the correct area of the mine. As an illustrative example, if data from one area is incorrectly mapped to another area of the mine, or samples are mixed, improper operational decisions may be made, which may have significant negative impacts on the efficiency of the mine.

[0007] Certain attempts have been made to collect samples using unmanned vehicles or robots. While these vehicles are useful in reducing the number of persons entering dangerous areas, they are generally not good at sample management. Furthermore, in many environments, global positioning system (GPS) data is unavailable, making mapping of samples to locationproblematic. Yet further again, sample collection and tracking is difficult in robots.

[0008] As such, there is clearly a need for improved robots and systems for robotic analysis of samples.

[0009] It will be clearly understood that, if a prior art publication is referred to herein, this reference does not constitute an admission that the publication forms part of the common general knowledge in the art in Australia or in any other country.SUMMARY OF INVENTION

[0010] The present invention relates to robots, and systems for robotic analysis of samples, which may at least partially overcome at least one of the abovementioned disadvantages or provide the consumer with a useful or commercial choice.

[0011] With the foregoing in view, the present invention in one form, resides broadly in a robot for analysing rock, the robot including:one or more drive elements, configured to drive the robot;one or more sensors, configured to generate sensor data relating to the rock; a processor, configured to process the sensor data to generate sample characteristic data corresponding to one or more characteristics of the rock.

[0012] Advantageously, the robot is able to navigate to areas of interest, generate sensor data from rock or ore there, and determine characteristics of the rock, without requiring a user to handle the rock. This in turn reduces the need for persons to enter dangerous environments, such as mines, where the rock or ore may be present. Furthermore, the robot is configured to determine characteristics of the rock, alleviating the need to collect and manage samples, e.g. for analysis at a laboratory.

[0013] Preferably, the robot includes a battery. The one or more drive elements may be powered by the battery.

[0014] The battery may be rechargeable. The battery may be releasably coupled to the robot.

[0015] Preferably, the robot comprises an unmanned vehicle. Preferably, the robot comprises an unmanned land vehicle.

[0016] Preferably, the one or more drive elements comprise wheels. The wheels may comprise a plurality of wheels. The plurality of wheels may be provided on opposing sides of abody. The wheels may be arranged in parallel rows. The wheels may include multiple wheels that are driven. The wheels may each be driven. The wheels may each be independently driven by a respective motor.

[0017] Alternatively, the one or more drive elements may comprise tracks. The tracks may extend along opposing sides of the robot.

[0018] Preferably, the one or more sensors are configured to generate a plurality of sensor data measurements relating to the rock, wherein the processor is configured to process the plurality of sensor data measurements to generate sample characteristic data corresponding to the one or more characteristics of the rock.

[0019] The plurality of sensor data measurements may be from a plurality of different angles. The plurality of sensor data measurements may be from a plurality of different locations of the rock.

[0020] The plurality of sensor data measurements may be from different rocksfrom a group of rocks. The group of rocks may comprise a muckpile, for example.

[0021] The rocks may be randomly selected from the group of rocks. The rocks may be selected according to size. The rocks may be selected according to one or more characteristics thereof.

[0022] Preferably, the rock comprises ore.

[0023] Preferably, the characteristics of the rock include a grade of the rock. The characteristics of the rock may include a measure of purity or quality of the rock. The characteristics of the rock may include a concentration of a desired material. The characteristics of the rock may include composition information.

[0024] In a particular embodiment, the characteristics of the rock comprise a mineral grade (e.g. percentage metal content, or part per million of a mineral) of the rock.

[0025] As outlined above, the processor is configured to process the sensor data to generate sample characteristic data corresponding to one or more characteristics of the rock.

[0026] The sensor data may comprise electrical signals which are converted to the sample characteristic data. The electrical signals may be converted to the sample characteristic data according to one or more predefined rules and / or formulae.

[0027] Preferably, the sample characteristic data is stored in a memory of the robot.

[0028] Preferably, the sample characteristic data is stored in a memory of the robot together with a timestamp and location data. The sample characteristic data, the timestamp and the location data may define a sample data entry.

[0029] The memory may include sample data entries relating to a plurality of samples captured by the robot.

[0030] Preferably, the robot includes one or more location sensors, for determining a location of the robot.

[0031] Preferably, the location is defined according to a three dimensional (X, Y, Z) coordinate system. The location may be defined with reference to an origin.

[0032] Preferably, the location sensors include a plurality of location sensors. The plurality of location sensors may include a plurality of types of location sensors.

[0033] Preferably, the plurality of location sensors include one or more non-GPS sensors.

[0034] The plurality of location sensors may include a GPS sensor (for example, for use when GPS is available). The plurality of location sensors may include a GPS sensor and one or more non-GPS sensors.

[0035] Preferably, the location sensors include one or more of Inertial Navigation System (INS) sensors, Radio Frequency (RF) based sensors, Vision based location sensors, Lidar sensors, acoustic positioning sensors, and magnetic field based positioning sensors.

[0036] Preferably, the robot is configured to determine its location according to data from two or more of the plurality of location sensors. For example, a first sensor may provide coarse location data, and a second sensor may be used to refine the coarse data.

[0037] The robot m ay be configured to fuse location data from a plurality of location sensors, to determine a location. The data may be fused using a Kalman filter, or any suitable method.

[0038] The robot may be configured to navigate autonomously. The robot may be configured to navigate semi-autonomously.

[0039] The robot may be configured to receive area of interest data, defining one or more areas of interest. The robot may be configured to autonomously or semi-autonomously travel to areas of interest defined by the area of interest data.

[0040] The robot may be configured to autonomously or semi-autonomously travel to aplurality of areas of interest defined by the area of interest data. The robot may travel to the areas of interest in an order determined by the robot.

[0041] The robot may include a camera, configured to capture images relating to surroundings of the robot. The robot may be configured to navigate at least in part according to images captured by the camera.

[0042] The robot may include a range sensor or scanner, configured to capture range data relating to surroundings of the robot. The robot may be configured to navigate at least in part according to the range data.

[0043] The robot may include map data. The robot may be configured to navigate at least in part according to the map data.

[0044] The robot may be configured to determine a path from its current location to the areas of interest, and navigate according to the path.

[0045] Preferably, the one or more sensors comprise non-destructive testing (NDT) sensors.

[0046] The one or more sensors may comprise a plurality of sensors.

[0047] The sensors may include image capture sensors. The sensors may include cameras. The sensors may include hyperspectral cameras.

[0048] The sensors may include X-ray diffraction (XRD) sensors. The sensors may include X-ray fluorescence (XRF) sensors.

[0049] The sensors may include laser-induced breakdown spectroscopy (LIBS) sensors.

[0050] The sensors may include Raman spectrometers.

[0051] The sensors may include radiation-based spectrometers, such as Gamma-ray spectrometers.

[0052] The sensors may include neutron detectors.

[0053] The robot may include a body. The one or more drive elements may be coupled to the body.

[0054] The body may be substantially rigid.

[0055] The body may be substantially hollow. One or more components of the robot may be housed in the hollow body.

[0056] The body may be positioned above the one or more drive elements in an elevated position. Such configuration may enable the robot to operate on uneven terrain, and for the body to travel above rocks or other obstacles.

[0057] An arm may extend outwardly from the body. The arm may comprise an articulable arm.

[0058] At least one of the one or more sensors may be provided on the arm. The at least one sensor may be provided on an end of the arm.

[0059] In some embodiments, a modular attachment is provided at the end of the arm. The modular attachment may, for exam pie, comprise a mounting plate, with which different sensors can be attached.

[0060] Preferably, the robot includes one or more tools.

[0061] Preferably, the one or more tools include one or more rock or ore preparation tools. The rock or ore preparation tools may be configured to prepare the rock or ore for sampling by the one or more sensors.

[0062] Preferably, the rock or ore preparation tools are provided on an arm that extends outwardly from a body of the robot.

[0063] The tools and the sensors may be provided on the same or different arms.

[0064] The tools may include a brush, for brushing the rock or ore.

[0065] The tools may include a nozzle, forspraying fluid on the ore. The fluid may comprise compressed air. Alternatively, the fluid may comprise a liquid.

[0066] The tools may include a cloth, for wiping the rock or ore.

[0067] The tools may include a laser ablation system, for cleaning the rock or ore or preparing a sample from the rock or ore for analysis.

[0068] The robot may include a processor, and a memory, coupled to the processor, the memory including instruction code executable by the processor for performing various functions of the robot.

[0069] In another form, the invention resides broadly in an in-situ sampling system including a robot as outlined above.

[0070] The system may include a remote computing device, wherein the robot is configured to transmit data relating to the characteristics of the rock to the remote computing device. The remote computing device may comprise a server.

[0071] The remote computing device may be configured to provide a user interface to a user device, with which the user may interact. The remote computing device may include a web server configured to provide the user interface to the user device.

[0072] The user interface may enable the user to configure areas of interest, which are provided to the robot.

[0073] The user interface may enable the user to view characteristics of the rock.

[0074] The system may include a docking station, for charging a battery of the robot.

[0075] The system may include multiple like robots.

[0076] Any of the features described herein can be combined in any combination with any one or more of the other features described herein within the scope of the invention.

[0077] The reference to any prior art in this specification is not, and should not be taken as an acknowledgement or any form of suggestion that the prior art forms part of the common general knowledge.BRIEF DESCRIPTION OF DRAWINGS

[0078] Various embodiments of the invention will be described with reference to the following drawings, in which:

[0079] Figure 1 illustrates an in-situ sampling system, according to an embodiment of the present invention.

[0080] Figure 2 illustrates a side view of the robot (simplified), according to an embodiment of the present invention.

[0081] Figure 3 illustrates a schematic of the robot (also simplified), according to an embodiment of the present invention.

[0082] Figure 4 illustrates a schematic of a memory of the robot, according to anembodiment of the present invention.

[0083] Preferred features, embodiments and variations of the invention may be discerned from the following Detailed Description which provides sufficient information forthose skilled in the art to perform the invention. The Detailed Description is not to be regarded as limiting the scope of the preceding Summary of the Invention in any way.DESCRIPTION OF EMBODIMENTS

[0084] Embodiments of robots, systems and methods are described below that relate to in-situ, robotic analysis of rock or ore samples in mining. The skilled addressee will, however, readily appreciate that the systems and methods may, however, be adapted for use in a range of other scenarios without deviating from the scope of the invention.

[0085] Figure 1 illustrates an in-situ sampling system 100, according to an embodiment of the present invention.

[0086] The system 100 includes a robot 105, which is configured to autonomously navigate in tunnels 110 and similar environments, to perform in-situ sampling of rock or ore. In particular, the robot 105 is configured to navigate to one or more areas of interest 115 in the tunnels 110, perform in-situ sampling at the areas of interest 115, and report the results of the sampling to a server 120 or similar computing system.

[0087] Figure 2 illustrates a side view of the robot 105 (simplified), according to an embodiment of the present invention. Figure 3 illustrates a schematic of the robot 105 (also simplified), according to an embodiment of the present invention.

[0088] The robot 105 includes body 200, and a plurality of wheels 205, arranged on opposing sides thereof. In the embodiment illustrated in Figure 2, the robot includes six driven wheels 205, arranged in parallel rows and on each side, but in other embodiments, other arrangements may be used, including different numbers of wheels 205, or even tracks. Such configuration enables the robot 105 to navigate on rough terrain, including over rocks and debris, which is common in tunnels and other excavations.

[0089] The body 200 is substantially hollow, and is used to house electronics, a battery, motors for driving the wheels 205, and other components of the robot 105.

[0090] An articulable arm 215 extends outwardly from a front of the robot 105, and includes tools 220 and sensors 225 on an end thereof. The tools 220 and sensors 225 may take any suitable form, but the tools 220 are generally configured to prepare rock or other material forsampling, and the sensors 225 are configured to sample the rock or other material for analysis.

[0091] Furthermore, the tools 220 and sensors 225 are modular and may be switched out based on the type of material being sampled. As will be readily appreciated, different sensors 225 may be needed to detect the presence of different materials, or other characteristics of the rock.

[0092] Finally, the robot 105 includes a range of sensors, such as a camera, a processor and memory, configured to control navigation of the robot, and receiveand process sensor data, and store the processed sensor data for transmission to the server 120, together with various other functions of the robot 105. The processed sensor data is transmitted to the server 120 using a wireless interface including an antenna 210.

[0093] Turning back to Figure 1, in use, a user 125 will initially configure the robot 105 and system 100. In particular, the server 120 includes a web server configured to provide a user interface to a user device 130, with which the user 125 may interact to configure the system.

[0094] The system 100 may be initially configured to include maps, and a range of other configuration details. One or more areas of interest 115 of the robot 105 may then be defined using the user interface, together with operating parameters of the robot 105.

[0095] As an illustrative example, the robot 105 may be configured to sample a plurality of areas of interest 115 at a particular schedule (e.g. daily).

[0096] The areas of interest 115 may be defined using a graphical representation of a map.

[0097] Once the system 100 has been configured, the robot 105 may wait, e.g. in a docking station, until it is configured to operate.

[0098] When the robot 105 operates, e.g. according to the abovementioned schedule, it leaves its dockingstation, or where it waits in standby, and autonomously navigates to an area of interest 115.

[0099] To do this, the robot obtains location data associated with the area of interest 115. The robot 105 then uses the location data, together with a map, to determine a path from its current location to the area of interest 115. The robot 105 then travels to the area of interest 115 using the determined path.

[0100] As outlined above, the robot 105 includes sensors, such as a camera, lidar or similar, which enables autonomous navigation and obstacle avoidance. Furthermore, in case anobstacle is identified, the robot 105 may recalculate a path. In some embodiments, the robot 105 need not use a path, but instead navigate to the area of interest 115 using direction and obstacle avoidance alone.

[0101] When the robot 105 reaches the area of interest 115, it stops, and prepares to take a sample. In particular, the robot uses the tools 220 to prepare the area of interest 115 (or part thereof) for taking a sample, e.g. by cleaning fine particles from the surface being sampled. Further detail on preparing the area of interest 115 for taking the sample is provided below.

[0102] When the area of interest 115 (or part thereof) is prepared, the sensors 225 are used to capture data of the sample.

[0103] The sensors 225 capture sensor data, which may be in the form of an electrical signal. A processor processes this sensor data to generate sample characteristic data corresponding to one or more characteristics of the rock. This may be performed using any suitable algorithm and will be dependent on the type of rock and characteristic investigated.

[0104] In the case of mineral grading, the sensors 225 are used to determine a concentration of a desired material in a mineral or ore. In short, different types of sensors 225 are used depending on the type of ore assayed. For example, low grade copper deposits may be analysed using laser-induced breakdown spectroscopy (LIBS) or radiation-based spectrometers, but coal may be analysed using hyperspectral imaging.

[0105] The sensors 225 are used to capture data of the sample multiple times to minimise the impact of heterogeneity in the area of interest 115. In particular, the sensors 225 are configured to generate a plurality of sensor data measurements relating to the rock. The processor processes the plurality of sensor data measurements to generate sample characteristic data corresponding to the one or more characteristics of the rock.

[0106] The sensor data measurements may be from a plurality of different angles, or even from different rocks with a group of rocks, such as a muckpile.

[0107] As outlined above, the sensors 225 are attached to an articulable arm 215. In some embodiments, the arm 215 moves around the sample to capture data from slightly different angles. The data may then be combined to remove outliers and to obtain a more accurate grade of the mineral.

[0108] In the case of the area of interest 115 comprising a muckpile or other similar collection of rock, the arm 215, or the robot 105, may move between different rocks or rock pieces, to capture data from different rocks or rock pieces.

[0109] The sensor data is processed at the robot 105, to generate sample characteristic data, the sample characteristic data relating to characteristics of the rock. The sensor data may comprise electrical signals which are converted to sample characteristic data in the form of a mineral grade (e.g. percentage metal content, or part per million of a mineral).

[0110] The sample characteristic data is then stored in a memory of the robot together with a timestamp and location data.

[0111] If more than one area of interest 115 is defined, the process is repeated for each area of interest 115.

[0112] The robot 105 then returns to its docking station, or where it waits in standby. The sample characteristic data is then uploaded to the server 120 together with the timestamp and location data (together sample data).

[0113] As previously mentioned, the server 120 includes a web server that provides a graphical user interface to the user device 130. The user 125 may use this user interface to view sample data, to make informed decisions in relation to the mine.

[0114] The server 120 may be coupled to other systems, such as monitoring systems, control systems and the like. In some embodiments, the sample data is automatically used as an input to other systems, such as control systems, monitoring dashboards, or the like.

[0115] The robot 105 will now be described in further detail below.

[0116] Now turning back to Figure 3, the robot 105 includes a controller 305, which may be in the form of, or include a processor, or similar. The controller 305 is coupled to a memory 310, which includes instruction code, executable by the controller 305, or a processor thereof, to perform the various functions of the robot 105. The memory 310 further includes other data, including map data, for use by the robot when navigating, and sample data that has been captured by the robot.

[0117] Figure 4 illustrates a schematic of the memory 310, according to an embodiment of the present invention.

[0118] The memory includes a data store 405, comprising a plurality of sample data entries 410, each relating to sample data captured by the robot 105. Each sample data entry 410 includes a timestamp field 410a, a location field 410b, and a sample data field 410c.

[0119] The sample data entries 410 may include any other suitable information, such as asample ID, unit of measurement (e.g. percentage, ppm, etc), mine name, metadata, or other data relating to the sample (e.g. rock size), or the like.

[0120] The memory 310 further includes a variety of program code modules, including a navigation module 415, a location module 420, and a sample data processing module 425.

[0121] The navigation module 415 enables the robot to navigate through tunnels or open pits to one or more areas of interest.

[0122] The location module 420 is configured to receive data from location sensors, and determine a location of the robot based thereon.

[0123] Finally, the sample data processing module 425 receives data from the sensors, and determines sample data, such as sample composition or grade, based thereon.

[0124] Now turning back to Figure 3, the robot further includes a battery 315, coupled to the controller 305, motors 320, coupled to the controller 305 and battery 315, and wheels 325, coupled to the motors 320.

[0125] The robot 105 further includes a plurality of location sensors 330, which are configured to determine a location of the robot. These sensors may take any suitable form, but are generally configured such that they are able to operate underground, where GPS does not work. Examples of such sensors include Inertial Navigation System (INS) sensors, Radio Frequency (RF) based sensors, Vision based location sensors, Lidar sensors, acoustic positioning sensors, and magnetic field based positioning sensors.

[0126] The location is defined according to a three-dimensional (X, Y, Z) coordinate system , and from multiple sensors. In short, Sensor Fusion is used to estimate position and resolve positioning errors. This includes both on-line sensor fusion techniques and post processing to refine and reduce position and environment representational errors.

[0127] Any suitable method may be used to combine data from the sensors, such as Kalman filtering.

[0128] The robot includes tools 220, as outlined above, and the rock is prepared prior to sampling. If the rock surface is not prepared prior to sampling, the results may be inaccurate based on the presence of fine particles, for example, that impact the sensors data.

[0129] In a mining environment, the tools 220 may be brushes, or nozzles outputting compressed air, as they remove fine particles from the rock, without impacting sensor readings.Wiping (e.g. by a lint-free cloth) may also be used, e.g. as a secondary step. Washing may also be used, but is less desirable as it may skew sensor data, particularly if the rock is highly porous.

[0130] Bristled brushes are particularly useful in scenarios where loose dust and fine particles is problematic. In such case, a soft brush may be used to initially remove loose material by softly brushing and gradually increasing pressure as needed. Such configuration is useful in avoiding generating more dust during the process.

[0131] The brushes may be configured to brush in one or more directions to avoid moving the dust around.

[0132] A nozzle with compressed air is a more powerful option for heavy dust or larger areas. In such case, short bursts of air may be used to blow away dust and dirt. In such case, the nozzle may be angled relative to the rock surface to avoid embedding dust particles further.

[0133] Lint-free cloths or wipes are useful in cleaning rock, e.g. after brushing. The cloth / wipes are preferably lint free to avoid leaving fibres behind that could interfere with analysis. Microfiber cloths are an example of a cloth particularly suitable for this purpose.

[0134] In some embodiments, the cloth may be impregnated with a liquid, such as water or a solvent, to assist in cleaning. As outlined above, however, liquid may impact analysis, and as such, it is desirable to use a dry cloth or compressed air to dry the rock.

[0135] As an alternative to compressed air, other fluids, including liquids may be used. In the case of water, distilled or deionized water is preferred to avoid contaminating the rock.

[0136] Mild soap may be used in some instances, e.g. to remove oily or greasy contaminants. In such case, soft brushes may be used in combination with the soap. Furthermore, the rock is rinsed with clean water, and dried.

[0137] The tools 220 may similarly include a laser ablation system, for cleaning the rock or ore or preparing a sample from the rock or ore for analysis.

[0138] In addition to cleaning the rock, the tools 220 may be used to clean the robot and keep the sensors clean. For example, a nozzle with compressed air may be used to clean the sensors.

[0139] As outlined above, multiple measurements may be taken and combined, to combat problems associated with heterogeneous samples.

[0140] In some instances, multiple measurements are taken per rock. In such case, 10-20or more measurements may be taken on each rock or rock fragment, e.g. distributed across its surface. In some embodiments, a grid of the surface is generated for systematic sampling.

[0141] In some instances, multiple rocks are sampled. In such case, a statistically significant number of rocks may be analysed to represent the unit of interest (e.g., 30-50 rocks fora muckpile). The more heterogeneous the material, the larger the number of rocks that may be used.

[0142] In such case, as the robot 105 is analysing the samples in situ (and at or near realtime), it can use the variance to determine whether enough samples have been taken to converge to a representative grade. For example, the robot 105 may determine that additional samples no longer add information based on a threshold, and thus stop sampling as an adequate number of samples has been taken.

[0143] Rocks may be chosen randomly from the area of interest to get an overall average grade. Alternatively, the area of interest may be divided into strata, wherein a rock is selected from each stratum and analysed separately. This is particularly useful if grade variations within the area of interest are suspected.

[0144] Rocks may be chosen according to rock size. If there's a wide range of rock sizes, rocks of different sizes (e.g., large, medium, small) may be separately analysed to understand potential grade variations based on size.

[0145] Finally, the robot includes a data interface 335, for communicating to the server 120 or other computing devices. The data interface 335 may comprise a WIFI data interface, Bluetooth interface, RF interface, NFC interface, or any suitable data interface.

[0146] In addition to sensing sample data, for navigation and obstacle avoidance, the sensors 225 may be used for other purposes in the robot. For example, the robot may determine presence (or absence of) persons, to ensure people are not exposed to hazards. For example, when XRF sensors are used, X-ray radiation is emitted.

[0147] In such case, other sensors (e.g. imaging or Lidar) may be used to determine the risk that a person is in the area, such that use of the hazardous sensors is prevented in case the risk is above a threshold.

[0148] While the above example describes an autonomous robot 105, in other embodiments, the robot 105 may be remotely controlled, semi-autonomous, or a combination thereof. In such case, the areas of interest need not be defined in advance, and the robot may be remotely controlled to navigate to areas of interest 115.

[0149] While a server 120 is illustrated, the skilled addressee will readily appreciate that the user device 130 may interact with the robot 105 directly. Similarly, the sensor data may be uploaded to the user device 130, rather than via the server 120.

[0150] Advantageously, the robot is able to navigate to areas of interest, generate sensor data from rock or ore there, and determine characteristics of the rock, without requiring a user to handle the rock. This in turn reduces the need for persons to enter dangerous environments, such as mines, where the rock or ore may be present.

[0151] Furthermore, the robot is configured to determine characteristics of the rock, alleviating the need to collect and manage samples, e.g. for analysis at a laboratory.

[0152] The system may be more accurate than similar corresponding systems, as the robot may take multiple samples, and combine these in various ways to improve accuracy. Furthermore, by preparing the sample with tools, accuracy is further improved.

[0153] In the present specification and claims (if any), the word ‘comprising’ and its derivatives including ‘comprises’ and ‘comprise’ include each of the stated integers but does not exclude the inclusion of one or more further integers.

[0154] Reference throughout this specification to ‘one embodiment’ or ‘an embodiment’ means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearance of the phrases ‘in one embodiment’ or ‘in an embodiment’ in various places throughout this specification are not necessarily all referring to the same embodiment Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more combinations.

[0155] In compliance with the statute, the invention has been described in language more or less specific to structural or methodical features. It is to be understood that the invention is not limited to specif ic features shown or described since the means herein described comprises preferred forms of putting the invention into effect. The invention is, therefore, claimed in any of its forms or modifications within the proper scope of the appended claims (if any) appropriately interpreted by those skilled in the art.

Claims

CLAIMS1. A robot for analysing rock, the robot including:one or more drive elements, configured to drive the robot;one or more sensors, configured to generate sensor data relating to the rock;a processor, configured to process the sensor data to generate sample characteristic data corresponding to one or more characteristics of the rock.

2. The robot of claim 1, including a battery, wherein the one or more drive elements are powered by the battery.

3. The robot of claim 1 , wherein the robot comprises an unmanned land vehicle.

4. The robot of claim 1, wherein the one or more sensors are configured to generate a plurality of sensor data measurements relating to the rock, wherein the processor is configured to process the plurality of sensor data measurements to generate sample characteristic data corresponding to the one or more characteristics of the rock.

5. The robot of claim 4, wherein the plurality of sensor data measurements are from a plurality of different angles and / or from a plurality of different locations of the rock.

6. The robot of claim 4, wherein the plurality of sensor data measurements are from different rocks in a group of rocks.

7. The robot of claim 6, wherein the rocks are selected from the group of rocks randomly, according to size and / or according to one or more characteristics thereof.

8. The robot of claim 1 , wherein the rock comprises ore.

9. The robot of claim 1 , wherein the characteristics of the rock include a grade of the rock, a measure of purity or quality of the rock, a concentration of a desired material, and / or composition information.

10. The robot of claim 1, wherein the sensor data comprises electrical signals, wherein the processor is configured to convert the electrical signals to the sample characteristic data according to one or more predefined rules and / or formulae.

11. The robot of claim 1 , wherein the sample characteristic data is stored in a memory of the robot together with a timestamp and location data, wherein the sample characteristic data, thetimestamp and the location data define a sample data entry, and wherein the memory includes sample data entries relating to a plurality of samples captured by the robot.

12. The robot of claim 1, wherein the robot includes a plurality of location sensors for determining a location of the robot, the plurality of location sensors including (i) one or more nonGPS sensors; and (ii) optionally, a GPS sensor for use when GPS is available.

13. The robot of claim 12, wherein the plurality of location sensors include one or more of Inertial Navigation System (INS) sensors, Radio Frequency (RF) based sensors, Vision based location sensors, Lidar sensors, acoustic positioning sensors, and magnetic field based positioning sensors.

14. The robot of claim 1 , wherein the robot is configured to navigate autonomously or semi-autonomously.

15. The robot of claim 14, configured to receive area of interest data, defining one or more areas of interest, wherein the robot is configured to autonomously or semi-autonomously travel to areas of interest defined by the area of interest data to sample rock at the areas of interest.

16. The robot of claim 15, including a camera and / or a range sensor or scanner, wherein the robot is configured to navigate at least in part according to images captured by the camera and / or according to the range data from the range sensor or scanner.

17. The robot of claim 15, wherein the robot includes map data, wherein the robot is configured to navigate at least in part according to the map data.

18. The robot of claim 15, wherein the robot is configured to determ ine a path from its current location to the areas of interest, and navigate according to the path.

19. The robot of claim 1 , wherein the one or more sensors comprise non-destructive testing (NDT) sensors.

20. The robot of claim 1 , wherein the one or more sensors comprise a plurality of sensors.

21. The robot of claim 1 , wherein the one or more sensors include one or more of an image capture sensor, a hyperspectral camera, an X-ray diffraction (XRD) sensor, an X-ray fluorescence (XRF) sensor, a laser-induced breakdown spectroscopy (LIBS) sensor, a Raman spectrometer, a radiation-based spectrometer, and a neutron detector.

22. The robot of claim 1 , including a body that is substantially rigid, wherein the one or more drive elements are coupled to the body, and an articulable arm that extends outwardly from thebody, wherein at least one of the one or more sensors is provided on the arm.

23. The robot of claim 22, including a modular attachment at an end of the arm, to enable different sensors to be attached thereto.

24. The robot of claim 1, including one or more rock or ore preparation tools configured to prepare the rock or ore for sampling by the one or more sensors.

25. The robot of claim 24, wherein the one or more rock or ore preparation tools include one or more of a brush, forbrushing the rock or ore, a nozzle, for spraying fluid on the rock or ore, a cloth, for wiping the rock or ore, and a laser ablation system, for cleaning the rock or ore or preparing a sample from the rock or ore for analysis.

26. An in-situ sampling system includinga robot according to claim 1 ; anda remote computing device,wherein the robot is configured to transmit data relating to the characteristics of the rock to the remote computing device.

27. The in-situ sampling system of claim 26, wherein the remote computing device is configured to provide a user interface to a user device, with which the user may interact.

28. The in-situ sampling system of claim 27, wherein the user interface enables the user to configure areas of interest, which are provided to the robot.

29. The in-situ sampling system of claim 27, wherein the user interface enables the user to view characteristics of the rock.

30. The in-situ sampling system of claim 26, including a docking station, for charging a battery of the robot.

31. The in-situ sampling system of claim 26, including a plurality of robots according to claim 1, wherein the plurality of robots are each configured to transmit data relating to the characteristics of rock to the remote computing device.

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