WO2024026531A1 - Systèmes et procédés de chargement de produits en vrac dans un stockage - Google Patents

Systèmes et procédés de chargement de produits en vrac dans un stockage Download PDF

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Publication number
WO2024026531A1
WO2024026531A1 PCT/AU2023/050716 AU2023050716W WO2024026531A1 WO 2024026531 A1 WO2024026531 A1 WO 2024026531A1 AU 2023050716 W AU2023050716 W AU 2023050716W WO 2024026531 A1 WO2024026531 A1 WO 2024026531A1
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WO
WIPO (PCT)
Prior art keywords
bulk material
conveyor
boom
storage space
lidar
Prior art date
Application number
PCT/AU2023/050716
Other languages
English (en)
Inventor
Gerard LITYNSKI
Andrew PASQUALE
Adam James
Lee SHARPE
David Malcolm
Matthew Mcdonald
Original Assignee
Bhp Innovation Pty Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2022902153A external-priority patent/AU2022902153A0/en
Application filed by Bhp Innovation Pty Ltd filed Critical Bhp Innovation Pty Ltd
Publication of WO2024026531A1 publication Critical patent/WO2024026531A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0832—Special goods or special handling procedures, e.g. handling of hazardous or fragile goods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G15/00—Conveyors having endless load-conveying surfaces, i.e. belts and like continuous members, to which tractive effort is transmitted by means other than endless driving elements of similar configuration
    • B65G15/22—Conveyors having endless load-conveying surfaces, i.e. belts and like continuous members, to which tractive effort is transmitted by means other than endless driving elements of similar configuration comprising a series of co-operating units
    • B65G15/26—Conveyors having endless load-conveying surfaces, i.e. belts and like continuous members, to which tractive effort is transmitted by means other than endless driving elements of similar configuration comprising a series of co-operating units extensible, e.g. telescopic
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G67/00—Loading or unloading vehicles
    • B65G67/60—Loading or unloading ships
    • B65G67/606—Loading or unloading ships using devices specially adapted for bulk material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/003—Bistatic lidar systems; Multistatic lidar systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/04—Systems determining the presence of a target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/481—Constructional features, e.g. arrangements of optical elements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

Definitions

  • the present invention generally relates to loading bulk material into storage.
  • Bulk materials such as ores, coal, sands or grains, are typically collected (e.g., mined or harvested) at the sources, stockpiled, then transported and delivered to end users. The flow of materials between the origin and destination, can affect the profitability of such materials.
  • a transportation vessel known as a bulk carrier.
  • Such bulk carriers typically contain openings known as hatches, whereby the bulk material may be loaded into a storage area or hold of the bulk carrier for transport.
  • Bulk carriers range in size from singlehold small carriers to very complex large ore ships having gear including cranes integral to the vessel, lights for twenty-four-hour-operation, bridges containing the equipment necessary to safely navigate a vessel on passage (such equipment will vary with ship type, but generally includes navigation devices, radars, communications systems and light/sound signalling devices).
  • a shiploader mainly consists of a central column, a potentially extendable boom, a belt conveyor extending out of the boom structure, potentially a slewing mechanism, and a loading chute to transfer bulk materials from a source conveyor or feeder.
  • the boom can move front and back, up and down by separate drives or actuators so that it can fill the whole breadth of the ship hold and adapt to the ships increasing draft while it is loaded.
  • Shiploaders are essential to the global shipping industry. Globalization has promoted the need for maritime ports to be equipped with efficient and durable shiploading machinery able to a handle a variety of bulk materials that enter into harbors within short time frames. This need for efficiency of loading and unloading has promoted advances in shiploader technologies including automation.
  • conveyor belts offer a very efficient method of loading, if they need to be shutdown due to a collision, for example, the required shutdown, repair and start-up and shutdown procedures can be complicated and require time to carry out.
  • the conveyance of bulk materials may also generate dust and other airborne occlusions.
  • Conventional sensors used in automation may be impacted if dust is present.
  • a system for loading bulk material into at least one storage space comprising: at least one conveyor mounted on a boom and cooperatively coupled for conveyance of bulk material from an infeed end of the conveyor to an output end of the conveyor; at least one arm supported by the boom and positioned proximate to the output end of the conveyor, the arm extending from the conveyor and bulk material thereon; at least one LiDAR sensor provided on the arm for detecting LiDAR sensor data from a region of at least one of the storage space and bulk material; and a processor configured to output data to position the boom relative to the storage space based on the LiDAR sensor data so as to optimise distribution of bulk material therein.
  • the arm is supported vertically by the boom and extends away from the conveyor and bulk material thereon.
  • this configuration positions the LiDAR sensors away from dust and other airborne occlusions that may be generated as bulk material is discharged into holds.
  • LiDAR sensors can be sensitive to dust, rain, spray, sunlight and reflections and minimising filtering algorithms or the like to remove dust or spray noise from the sensor data by way of optimal mechanical positioning provides significant performance advantages.
  • the sensors may also be positioned away from heavy wash-down as is required in shiploading systems from time-to-time.
  • the storage space comprises a cargo hold accessible by a hatch opening in a ship.
  • the ship may include a bulk carrier with a plurality of cargo holds accessible by respective hatches.
  • Bulk carriers are a type of ship which transports cargo in bulk quantities.
  • the cargo transported in such ships may be iron ore or coal.
  • the LiDAR sensor data includes a plurality of LiDAR points, each LiDAR point having an associated location.
  • the LiDAR sensor data includes data from a plurality of scans of the at least one LiDAR sensor.
  • LiDAR point cloud data can include data obtained during a plurality of sensor scans. Each sensor scan can represent a full revolution of the LiDAR sensor.
  • the LiDAR sensors may include internal prisms to create non-overlapping scan patterns.
  • the processor is further configured to: generate, from the LiDAR sensor data, a representation of a LiDAR range image of the region of at least one of the storage space and bulk material; generate, using one or more machine-learned models, classification data from the representation of the LiDAR range image, the classification data representing one or more classifications of objects depicted in the LiDAR range image; determine one or more bounding shapes of the objects based at least on the classification data; and output data representing the one or more bounding shapes to position the boom relative to the respective object.
  • this configuration allows the system to leverage machinelearning technology or advanced algorithms to analyse the voxel grid representation and extract object information from the voxel grid representation of the LIDAR point cloud data.
  • object classification can include low-level information about the point cloud data, including information about how the bulk material is being stored in the hold (i.e., the storage space), including edges, surfaces, heights, volumes, widths, and so on.
  • the system can, using one or more machine- learned models, identify one or more higher-level object classification based on the voxel grid representation.
  • the detected objects may be a ship bridge, hatch covers in open and closed configurations, light poles, personnel, cranes, light and communications towers, cables and wires, tugboats, a bulk carrier, adverse environmental conditions.
  • the detected objects may be represented in a bird's eye view.
  • the processor is further configured to generate a voxel grid representation of the LiDAR sensor data.
  • the one or more classification objects includes a ship.
  • the ship is a bulk carrier.
  • the one or more classification objects include a hatch opening in a ship.
  • the one or more classification objects include at least one of an obstacle area or a restraint area.
  • a restraint area may be a complicated area for the boom to navigate, such as a passage area, or close to an obstacle or personnel.
  • the one of more classification objects includes a property of the bulk material.
  • the property of the bulk material includes at least one of a height dimension, a height map, a width dimension, a volume, and a location of the bulk material within the storage space.
  • the processor is further configured to dynamically compensate for movement or drift of the storage space.
  • An advantage of this feature is that the boom can dynamically compensate for smaller movements of the ship to and/or away from the wharf and along the wharf caused by swell or tidal movement in substantially real-time. Not compensating for those minor movements may “blur” the sensor data, which may adversely affect the performance of system.
  • the system includes a GPS (Global Positioning System) to determine a current location of the storage space.
  • the GPS may include a real time kinematic (RTK) differential GPS receiver that can be used to obtain the position locations of LiDAR sensors with a very high degree of accuracy.
  • RTK real time kinematic
  • the GPS may be used for time synchronization of each of the LiDAR sensor allowing the sensor data to be accurately combined into and single 3D image.
  • the GPS may also provide positional information which in conjunction with existing shiploader position encoders can provide increased confidence and reliability of the boom position with respect to obstacles and the like.
  • the system further comprises a plurality of LiDAR sensors spaced apart and positioned to provide a wide substantially uninterrupted view of the region of at least one of the storage space and bulk material.
  • the processor is further configured to generate a fused representation of the region of at least one of the storage space and bulk material, based on the LiDAR sensor data detected from the plurality of LiDAR sensors, the LiDAR sensor data may be additionally or alternatively fused with other sensor data.
  • fusing sensor data can improve the confidence of results, be it in depth finding from an image or for object detection applications when combined with GPS data, for example.
  • the system includes at least two arms supported by the boom and positioned proximate to the output end of the conveyor, each arm extending away from each other, the conveyor and bulk material thereon.
  • the at least two arms each support at least one LiDAR sensor.
  • the at least two LiDAR sensors are arranged symmetrically with respect to a central axis.
  • the system further comprises: an elevated bridge frame extending over the boom; and at least one LiDAR sensor provided on the elevated bridge for detecting LiDAR sensor data from the region of at least one of the storage space and bulk material.
  • this arrangement is suitable for shuttle shiploaders.
  • the systems further comprises: an elevated mast frame supporting the boom as balanced structure; and; at least one LiDAR sensor provided on either side of the mast frame for detecting LiDAR sensor data from the region of at least one of the storage space and bulk material.
  • this arrangement is suitable for slewing shiploaders.
  • a method for loading bulk material into at least one storage space comprising: providing at least one conveyor mounted on a boom and cooperatively coupled for conveyance of bulk material from an infeed end of the conveyor to an output end of the conveyor; providing at least one arm supported by the boom and positioned proximate to the output end of the conveyor, the arm extending from the conveyor and bulk material thereon; providing at least one LiDAR sensor provided on the arm for detecting LiDAR sensor data from a region of at least one of the storage space and bulk material; and accessing a processor configured to output data to position the boom relative to the storage space based on the LiDAR sensor data so as to optimise distribution of bulk material therein.
  • an autonomous shiploader comprising: at least one conveyor mounted on a boom and cooperatively coupled for conveyance of bulk material from an infeed end of the conveyor to an output end of the conveyor; at least one arm supported by the boom and positioned proximate to the output end of the conveyor, the arm extending from the conveyor and bulk material thereon; at least one LiDAR sensor provided on the arm for detecting LiDAR sensor data from a region of at least one of the storage space and bulk material; one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: outputting data to position the boom relative to the storage space based on the LiDAR sensor data so as to optimise distribution of bulk material therein.
  • FIG. 1 shows a diagram of a system for loading bulk material into a storage space in accordance with an embodiment of the present invention
  • FIG. 2 shows a rendering of two davit arms used to support a LiDAR sensor proximate to the output end of a conveyor in accordance with an embodiment of the present invention
  • FIG. 3a shows a rendering of a LiDAR sensor configuration on a slewing shiploader in accordance with an embodiment of the present invention
  • FIG. 3b shows a rendering of a LiDAR sensor configuration on a slewing shiploader in accordance with an embodiment of the present invention
  • FIG. 4 shows a rendering of a LiDAR sensor configuration on a shuttling shiploader in accordance with an embodiment of the present invention
  • FIG. 5 shows a block diagram of an example system for position a boom relative to a bulk carrier based on LiDAR sensor data so as to optimise distribution of bulk material therein.
  • the invention is suitable for loading bulk material into a storage space or cargo hold of a bulk carrier by way of a shiploader, and it will be convenient to describe the invention in relation to that exemplary, but non-limiting, application. However, it will be appreciated that the same approach is applicable to other storage areas, stockpiles and the like. The approach is also suitable for other conveyance systems.
  • Examples of known shiploader installation configurations include several basic types of shiploading systems. These systems include fixed loaders, traveling loaders, quadrant loaders, slewing/traversing loaders, and linear loaders.
  • FIG. 1 there is shown a simplified plan view of an exemplary embodiment of a system 100 for loading bulk material 102 into a storage space or cargo hold 104 of a bulk carrier 106 in accordance with an embodiment of the present invention.
  • the bulk carrier 106 is docked adjacent to a berth, wharf or pier 108 in a body of water 110.
  • the body of water 110 is a seaport located on the shore of a sea or ocean.
  • the invention may be suitable for use at different types of facilities handling bulk materials, for example, at a river port used for river traffic, such as barges and other shallow-draft vessels.
  • the bulk carrier 106 is typical of the type of transport vessel used for dry bulk material such as ores or coal and includes a bow 112, a stern 114, a first hold 116, a second hold 118, and a third hold 120 located between the bow 112 and the stern 114, but the number of holds of the bulk carrier is not limited thereto, and in this embodiment only five holds are taken as an example, but the number of holds may also be three, four or six, and is not limited herein.
  • the bridge 122 contains the equipment necessary to safely navigate the bulk carrier 106 on passage.
  • such equipment will vary with vessel type, but generally includes sensitive navigation devices, one or more radars, a communications system (including distress calling equipment and various antennas) and light/sound signalling devices. It will be appreciated that interference with the equipment on the bulk carrier 106, including the bridge 122, can present serious problems in regard to safety of the vessel and its personnel.
  • Each hold 116, 118, 120 is accessible by way of hatches 124.
  • the hatches 124 provide an opening at the top of a cargo hold. Hatches 124 vary between bulk carriers but are generally rectangular and occupy a significant portion of the vessel’s breadth or beam.
  • the bulk material 102 is stored holds 116, 118, 120 for transit. As bulk material 106 is distributed in each hold 116, 118, 120 the bulk carrier’s 106 position within the body of water 110 may change. In the simplest sense, as the volume of bulk material 102 increases in each hold 116, 118, 120 the vessel may increase its draft. However, depending on the distribution of the bulk material 102, the vessel may also list or pitch during operation, which can also pose a danger to the vessel and its personnel. Accordingly, the vessel may need to be loaded in a specific sequence so as to not overstress the hull.
  • the bulk material 106 is conveyed to each hold 116, 118, 120 by at least one conveyor 128 mounted on a boom 126.
  • the boom 126 and conveyor 128 receive the bulk material 106 along respective loading axes 132 and discharge the bulk materials to holds 116, 118, 120.
  • the boom 126 is operable to vary its discharge points relative to its loading axes 132. In some embodiments, this is both radially and angularly and those skilled in the art will recognize suitable designs for providing the stated functions.
  • the invention is suitable for both linear and radial type shiploaders that pivot about a central point, the former having a longitudinal runway beam adjacent to the berth and the latter having a quadrant beam on a radius.
  • the boom 126 includes two arms 132 supported by the boom 126 and positioned proximate to the output end 134 of the conveyor 128, the arms 132 extend away from the conveyor and bulk material thereon.
  • Each arm 132 includes a Light Detection and Ranging (LiDAR) sensor 134 provided on the arm for detecting LiDAR sensor data from a region of at least one of the bulk carrier 106 (and holds 116, 118, 120 is accessible by way of a hatches 124 and bulk material 102).
  • the LiDAR sensors 132 which may be commercially available off the shelf (COTS) components or the like, are arranged symmetrically with respect to a central axis of the conveyor 128.
  • COTS off the shelf
  • LiDAR sensors 132 are positioned away from dust generated as the bulk material is discharged into holds 116, 118, 120.
  • LiDAR sensors when used in this manner may be subject to a range of austere environmental and atmospheric conditions (e.g., rain, spray, sunlight, dust, reflections) and minimising the need for filtering algorithms or the like to remove dust or spray noise from the sensor data by way of mechanical positioning provides a significant technical advantage.
  • the LiDAR sensors 134, 136 are in electrical communication with a processor 130 configured to output data to position the boom 126 relative to the cargo holds 116, 118, 120 and the bulk carrier 106 based on the LiDAR sensor data so as to optimise distribution of bulk material 102 therein.
  • the processor 130 may use standard and proprietary communication protocols to transport the output data to position the boom 126 relative to cargo holds 116, 118, 120. These protocols may use telemetry techniques such as provided by wires, cables, fibre optics, and/or radiofrequency transmissions such as broadcast, microwave, and/or satellite communications.
  • the processor 130 in this example may perform local control of actuators and monitor sensors of the boom 126.
  • the boom 126 and other parts of the shiploader a may include one or more PLCs.
  • a PLC is a small industrial computer originally designed to perform the logic functions formerly executed by electrical hardware (such as switches, and/or timers and counters). PLCs have evolved into controllers capable of controlling complex processes, and are used extensively in distributed control systems. Other controllers used in the filed may include process controllers and Remote Terminal Units, which may provide the same level of control as a PLC but may be designed for specific control applications. However, PLCs are often used as field devices because they are often more flexible, and configurable than special-purpose controllers.
  • the PLC may control actuators and monitor sensors.
  • actuators include rotary and linear motors, pneumatic actuators, hydraulic pistons, valves and pumps, among others that drive the shiploader machine.
  • sensors include position encoders, level sensors, pressure sensors, angle sensors, inertial measurement unit(s), and/or other sensors. Any of the actuators or sensors may be “smart” actuators or sensors or intelligent electronic devices. Intelligent electronic devices may include intelligence for acquiring data, communicating with other devices, and performing local processing and control. An intelligent electronic device could combine an analog input sensor, analog output, low-level control capabilities, a communication system, and/or program memory in one device.
  • processor 130 encompassing several subprocessors and PLCs will be discussed in greater detail with reference to FIG. 5.
  • the davit arms 202 used to support a LiDAR sensor 204 proximate to the output end 206 of a conveyor 208 in accordance with an embodiment of the present invention.
  • the davit arms 202 provide a convenient means of supporting respective LiDAR sensors 204 up and away from the conveyor 208 and bulk material 212 thereon.
  • the davit 202 includes a jib extending to one side and the jib includes a connection point for connecting the LiDAR sensor 204 and any associated cabling (not shown).
  • the davit 202 may be provided in a sleeve, collar or the like, to allow the LiDAR sensor to be positioned at a selected angle of rotation around a vertical axis as represented by ellipsoid 216.
  • the davit 202 may also be provided with a latch or locking mechanism to lock the davit 202 at a selected angle of rotation around that axis.
  • the conveyor 208 is mounted on a boom 210 and cooperatively coupled for conveyance of bulk material 212 from an infeed end of the conveyor (arrow 214) to an output end of the conveyor 206.
  • davit arms 202 are exemplary only and other arm designs are conceivable within the spirit and scope of the present invention.
  • an arm extending up and away from boom 210 at an angle.
  • the arms 202 may be connected to the boom 240 by weld or mechanical fastener, for example, or by other means known in the art.
  • LiDAR sensors 204 are employed, but the number of LiDAR sensors 204 is not limited thereto, and in this embodiment only two sensors are taken as an example, but the number of sensors may also be four or six, and is not limited herein.
  • the LiDAR sensors are spaced apart and positioned to provide a wide substantially uninterrupted view of the bulk material 212 and bulk carrier 106 including hold 102 and hatches 124.
  • the sensor data may be fused to provide a representation of the bulk material 212 and bulk carrier 106. It will be appreciated that the LiDAR sensor data may be additionally or alternatively fused with other sensor data.
  • the configuration of LiDAR sensors 204 can include a first set of LiDAR sensors that include a field of view that encompasses a region in front of the boom 210, and a second set of sensors having a field of view that encompasses the side regions extending laterally from each side of the boom 210.
  • the side regions can extend to substantially include the full length of the bulk carrier 106.
  • this can provide anticollision measurement and protection for the left-hand side and right-hand side of the boom as well as providing hatch coaming mapping regardless of which way the boom is slewed (i.e., towards the bow or towards the stern of the ship).
  • Different configurations may be required for different shiploaders as discussed with reference to FIG. 3 and FIG. 4.
  • FIG. 3a there is shown a rendering of a LiDAR sensor configuration on a slewing shiploader 302 in accordance with an embodiment of the present invention.
  • LiDAR sensors 304a, 304b are disposed on an elevated mast frame 306 supporting boom 308 as a balanced structure.
  • the LiDAR sensors 304a, 304b have a field of view that encompasses the side regions extending laterally from each side of the boom 308 in addition to two LiDAR sensors 305a, 305b that include a field of view that encompasses a region in front of the boom 308.
  • LiDAR sensors 305a and 305b are similarly positioned to the first set of LiDAR sensors 204 as described with reference to FIG. 2.
  • the side regions can extend to substantially include the full length of the bulk carrier 106.
  • this allows for the system to identify obstacles, including light poles, hatch covers, the vessel bridge, cables and wires. Given the very broad field of view provided by the placement of the LiDAR sensors 304, obstacles may be detected and avoided in a variety of areas to protect the boom (e.g., underside, topside and sides).
  • FIG. 3b there is shown an additional rendering of a LiDAR sensor configuration on a slewing shiploader 302 in accordance with an embodiment of the present invention.
  • the LiDAR sensors 304a, 304b have a field of view that encompasses the side regions extending laterally from each side of the boom 308 in addition to two LiDAR sensors 305a, 305b that include a field of view that encompasses a region in front of the boom 308.
  • FIG. 4 there is shown a rendering of a LiDAR sensor configuration on a shuttling shiploader 402 in accordance with an embodiment of the present invention.
  • LiDAR sensors 404a, 404b are disposed on an elevated bridge frame 406 extending over boom 408.
  • the LiDAR sensors 404a, 404b also have a field of view that encompasses the side regions extending laterally from each side of the boom 408.
  • the side regions can extend to substantially include the full length of the bulk carrier 408 (in some instances the effective range may be in the order of 300 to 1000 metres).
  • this allows for the system to identify obstacles, including light poles, hatch covers, the vessel bridge, cables and wires when the shiploader transits alongside the vessel (in order to avoid vessel haulage, for example).
  • FIG. 5 shows a block diagram of an example system 500 for positioning a boom relative to a bulk carrier based on sensor data so as to optimise distribution of bulk material therein.
  • FIG. 5 shows a system 500 that can include a shiploader 502 and a processor 504 associated with the shiploader 502.
  • the processor 504 can be located onboard the shiploader (e.g., it can be included on and/or within the shiploader 502) or remote from the shiploader 502 (e.g., a server accessed via a network).
  • the shiploader 502 can be configured to operate in a plurality of operating modes.
  • the shiploader 502 can be configured to operate in a fully autonomous operating mode in which the shiploader 502 is controllable without user input (e.g., can load bulk material into a cargo hold optimally and autonomously move with no input from a human operator present in the shiploader 502 and/or remote from the shiploader 502).
  • the shiploader 502 can operate in a semi-autonomous operating mode in which the shiploader 502 can operate with some input from a human operator present in the shiploader 502.
  • the shiploader 502 can implement operating assistance technology (e.g., collision mitigation system or a restraint system etc.), for example, to help assist a human operator of the shiploader 502.
  • the shiploader 502 can also operate in a combination of the above modes, for example, the shiploader 502 may operate for long periods of time fully autonomously, then allow input from a human operator to trim the hatch.
  • an operator may be able to make fine adjustments to the loading pattern, to ensure the vessel is stable prior to completion.
  • the processor 504 can include one or more processors and/or modules located onboard or remote from the shiploader 502.
  • the processors 504 can include various components for performing various operations and functions.
  • the processors can include one or more computers and one or more tangible, non- transitory, computer readable media (e.g., memory devices, etc.).
  • the one or more tangible, non-transitory, computer readable media can store instructions that when executed by the one or more processors cause the shiploader 502 (e.g., its computing system, one or more processors, etc.) to perform operations and functions, such as those described herein for controlling a shiploader, and in particular a boom relative to a bulk carrier, communicating with other computing systems, etc.
  • the shiploader 502 can include a network system 506 configured to allow the processor 504 to communicate with other computing devices.
  • the network system 506 can include any suitable components for interfacing with one or more networks 508, including, for example, transmitters, receivers, and/or other suitable components that can help facilitate communication.
  • the processor 504 can use the network system 506 to communicate with one or more computing devices that are remote from the shiploader 502 over one or more networks 508 (e.g., via one or more wireless signal connections).
  • the networks 508 can exchange signals, data (e.g., data from a computing device), and/or other information and include any combination of various wired (e.g., twisted pair cable) and/or wireless communication mechanisms (e.g., cellular, wireless, satellite, microwave, and radio frequency) and/or any desired network topology.
  • the network 508 can include a local area network (e.g., intranet, TCP/IP), wide area network (e.g., Internet, TCP/IP), wireless LAN network, and/or any other suitable communication network for transmitting data to and/or from the shiploader 502 and/or among computing systems.
  • the shiploader 502 can include one or more sensors.
  • the shiploader 502 may be fitted with four LiDAR sensors 510 installed at optimal vision points around the shiploader 502, for example, proximal to the boom tip 512 or on the bridge or apex 514 of the shiploader (based on the type of shiploader i.e., bridge for shuttling shiploader and apex for skewing shiploaders).
  • the sensors generally operate on a time-of-flight principle. They emit pulses of laser light in a scanning pattern and measure the time taken for the pulses to reflect off an object and return to the sensor to calculate the distance to an object.
  • GPS global positioning system
  • the GPS 516 is real time kinematic (RTK) differential GPS receiver that can be used to obtain the position locations of LiDAR sensors 510 with very high accuracy.
  • RTK real time kinematic
  • the GPS 516 may be used for time synchronization of each of the LiDAR scanner data streams allowing them to be accurately combined into and single 3D image.
  • the GPS 516 may also provide positional information which in conjunction with existing shiploader 502 position encoders can provide increased confidence and reliability of the boom position 512.
  • a direct benefit of this arrangement is that the boom 512 can compensate for smaller movements of the ship to and/or away from the wharf and along the wharf caused by swell (or broken or loose mooring lines) in substantially real time.
  • the hatches are tracked during ship drifts and the vision system 518 maps a LiDAR point cloud. Not compensating for the boom position 512 can lead to errors such as loading outside the hatch, or loading with the wrong distribution of ore in the hatch.
  • the communication channels can include one or more data buses (e.g., controller area network, and/or a combination of wired and/or wireless communication links.
  • the sensors 510 can send and/or receive data, messages, signals, etc. amongst one another via the communication channels.
  • each LiDAR sensor 510 may transport sensor data to a machine vision system 518 for processing e.g., assembling sensor (i.e., scan) data in module 520, fusing sensor data amongst other possibilities.
  • the sensors 510 can be configured to acquire sensor data. This can include sensor data associated with the surrounding environment of the shiploader 502 and bulk material.
  • the surrounding environment of the shiploader 502 can include/be represented in the field of view of the sensors 510. While the sensors as described herein are generally LiDAR sensors, they should not be limited to LiDAR systems and may also include one or more Radio Detection and Ranging (RADAR) systems, one or more cameras, one or more motion sensors, and/or other types of imaging capture devices and/or sensors.
  • RADAR Radio Detection and Ranging
  • the sensor data can include image data (e.g., video data, 2D camera data etc.), LiDAR data (e.g., 3D point cloud data, including a plurality of LiDAR points, each LiDAR point having an associated location on the bulk carrier etc.), LiDAR data from a plurality of sweeps, and/or other types of data.
  • image data e.g., video data, 2D camera data etc.
  • LiDAR data e.g., 3D point cloud data, including a plurality of LiDAR points, each LiDAR point having an associated location on the bulk carrier etc.
  • LiDAR data from a plurality of sweeps e.g., a plurality of sweeps, and/or other types of data.
  • the shiploader 502 can also include other sensors configured to acquire data associated with the shiploader 502, sensor data may be processed by machine vision system 518.
  • the various sensor data be fused to improve the confidence of results, be it in depth finding from an image or for object detection applications.
  • a camera image and LiDAR point cloud may be overlapped and unsampled to obtain depth values for each pixel in the image, or the like.
  • the machine vison module 518 may access LiDAR point cloud data and GPS 516 data.
  • the LiDAR point cloud data can include data obtained during a plurality of sensor sweeps.
  • Each sensor sweep can represent a full revolution of the LiDAR sensor as the boom 514 moves in relation to the cargo hold, for example.
  • each sweep represents a different time step between a point in the past and the most recent sweep (effectively the current time).
  • the LiDAR point cloud data and GPS data can be organized and represented as voxel grid representations.
  • the area associated with the LiDAR point cloud data can be subdivided into a plurality of voxels (e.g., three-dimensional portions of the total area) and each voxel can be assigned a value representing the number of points that fall into that voxel and their average distance from the sensor.
  • Software running on modules 522 and 524 may then evaluate potential collision objects detected and reports their location and distance to the machine PLC 526 via a TCP/IP communications link.
  • the machine vision module 518 consists of two modules 522 and 524.
  • Module 522 can be dedicated to ship model protection systems, whereas module 524 can be configured to a real-time protection system. Both modules 522 and 524 report boom collision object data to the shiploader machine control PLC 526.
  • the ship model protection system 524 uses a scan of the vessel that is taken/updated when the boom conveyor is not moving and therefore not able to generate any dust which can interfere with LiDAR sensors 510.
  • the model is stored in memory and is compared with a 3D model of the ship loader structure along with the machine position information reported by the encoders and GPS 516. Potential collision objects are calculated for each zone and report to the machine PLC 526.
  • this configuration provides resilience to dust.
  • the real-time protection system 522 uses live scan data from LiDAR sensors 510.
  • the system provides protection using data that is constantly updated from all LiDAR sensors 510. New collision objects can be detected in real-time, but environmental factors i.e., dust, fog may affect the performance.
  • Software filters are used to remove these false detections from the scan data, and those skilled in the art will recognise filters for providing the stated functions.
  • the system 500 for positioning a boom relative to a bulk carrier can leverage machine-learning technology to analyse the voxel grid representation.
  • the processor 504 is configured to generate, using one or more machine-learned models, classification data from the voxel grid representation, the classification data representing one or more classifications of objects depicted in that LiDAR voxel grid representation. Bounding shapes of the objects based on the classification can be determined and data representing the one or more bounding shapes can be used to position the boom relative to the respective objects by way of PLC 526.
  • the classification objects can include a bulk carrier itself, hatch openings in the bulk carrier, obstacle areas and the like.
  • the classification objects can also include properties of the bulk material itself, for example, at least one of a height dimension, a height map, a heat map, a width dimension, a volume, and a location of the bulk material within the storage space (material deposition in the storage space or hold, natural settling effects, and the like), a trajectory of the bulk material.
  • classifying properties of the bulk material allows for the bulk material to be optimally distributed in the hold.
  • the bulk material can be evenly distributed to avoid listing and to protect the structural integrity of the hull, keep the vessel upright, among others.
  • a height map may include an array of height values and compared with a reference plane relating to the cargo hold.
  • the height map may be represented as a textured polygonal mesh from depth, and position data generated by the LiDAR sensors 510 and GPS data 516.
  • the height map may alternatively be described as a “profile” of the bulk material.
  • One or more algorithms may be employed to maintain vessel list and evenly distribute the bulk material within the hold to enable the maximum capacity to be realised.
  • an algorithm may generate load points within the safe pour zone of the hatch based on the vessel list and the stage of the pour, and prioritise which one of these to target based on the height of the hold bulk material profile. The target load point can then be converted to a set of machine drive targets for the automated motion control of the boom and shuttle, its spout or spoon and so forth.
  • the automated hatch loading supports dual loading a vessel through the synchronisation of available pour zones to ensure the shiploaders work together to control the vessel list, while maintaining safe machine separation and evenly distributing the bulk material within their respective holds.
  • the processor 504 and in particular the machine vision module 518 can extract object information from the voxel grid representation of the LIDAR point cloud data.
  • object classification can include low-level information about the point cloud data, including information about how the bulk material is being stored in the hold, including edges, surfaces, heights, volumes, widths, and so on.
  • the system 500 can, using one or more machine-learned models, identify one or more higher-level object classification based on the voxel grid representation.
  • the classified and/or detected objects can be a ship bridge, hatch covers in open and closed configurations, light poles, personnel, cranes, light and communications towers, cables and wires, tugboats, a bulk carrier, adverse environmental conditions.
  • the detected objects can be represented in a bird's eye view.
  • the machine vision module 518 tracks the position of the hatch corner coordinates either by the hatch covers or the coaming itself.
  • the PLC 526 manipulates the position of the boom so that its position is dynamically updated to move with the ship to maintain the desired bulk material deposition point in the hatch.
  • the machine vision module 518 dynamically adjusts the bulk material deposition point to the port and starboard based on the list feedback of the ship. Additionally or alternatively, the machine vision module 518 may also evenly distribute the bulk material to the forward and aft parts of the hatch to create an even distribution length ways.
  • the profile of the bulk material is maintained via two methods: modelled and measured as described above.
  • the profile of the bulk material my be continuously updated by a modelling algorithm by tracking properties of the bulk material.
  • the properties of the material may include location and volume of the bulk material deposition in the hold, natural settling effects, and the like.
  • the volume of the bulk material is known from the discharge rates and material density, while the location of deposition is determined based on the intersection point of the latest hold bulk material profile and ore trajectory as determined by any one of the methods as described above.
  • the intersection point relates to the point where the ore trajectory meets the bulk material in the hold.
  • the processor 504 and in particular the machine vision module 518 may generate a hatch map of the latest known bulk material profile height measurements, which is used to update and verify the modelled hold bulk material profile.
  • the modelled bulk material profile enables the system 500 to maintain an accurate representation of the bulk material profile in areas of the hold that the machine module 518 is unable to measure due to obstructions from structures or the bulk material stream.
  • the hold bulk material profile may then be passed through a smoothing algorithm to mimic the settling of bulk material in the hold based on the materials angle of response.
  • the angle of response relates to the angle of descent or dip relative to the horizontal plane on which the bulk material can be piled without slumping. At this angle, the material on the slope face is on the verge of sliding.
  • the morphology of the material affects the angle of repose.
  • the one or more machine-learned models can be or can otherwise include various machine-learned models such as, for example, neural networks, convolutional neural networks (e.g., dual-head convolutional neural networks, etc.), decision trees, ensemble models, support vector machines, Bayesian networks, or other types of models including linear models, and/or non-linear models.
  • Example neural networks include multi-layer perceptron networks, feed-forward neural networks, recurrent neural networks, convolutional neural networks, or other forms of neural networks.
  • the various embodiments can be implemented in a wide variety of operating environments, which in some cases can include one or more user computers, computing devices, industrial computers or processing devices which can be used to operate any of a number of applications.
  • User or client devices can include any of a number of general purpose industrial or personal computers, such as desktop or laptop computers running a standard operating system, as well as cellular, wireless, and handheld devices running mobile software and capable of supporting a number of networking and messaging protocols.
  • Such a system also can include a number of workstations running any of a variety of commercially available operating systems and other known applications for purposes such as image processing, computer vison and database management.
  • Some embodiments may utilize at least one network that would be familiar to those skilled in the art for supporting communications using any of a variety of commercially-available protocols, such as TCP/IP, OSI, FTP, UPnP, NFS, and CIFS.
  • the network can be, for example, a local area network, a wide-area network, a virtual private network, the Internet, an intranet, an extranet, a public switched telephone network, an infrared network, a wireless network, and any suitable combination thereof.
  • the environment can include a variety of data stores and other memory and storage media as discussed above. These can reside in a variety of locations, such as on a storage medium local to (and/or resident in) one or more of the computers or remote from any or all of the computers across the network. In a particular set of embodiments, the information can reside in a storage-area network (“SAN”) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers, servers, or other network devices can be stored locally and/or remotely, as appropriate.
  • SAN storage-area network
  • each such device can include hardware elements that can be electrically coupled via a bus, the elements including, for example, at least one central processing unit (CPU), at least one input device (e.g., a mouse, keyboard, controller, touch screen, or keypad), and at least one output device (e.g., a display device, printer, or speaker).
  • CPU central processing unit
  • input device e.g., a mouse, keyboard, controller, touch screen, or keypad
  • at least one output device e.g., a display device, printer, or speaker
  • Such a system can also include one or more storage devices, such as disk drives, optical storage devices, and solid-state storage devices such as random access memory (“RAM”) or read-only memory (“ROM”), as well as removable media devices, memory cards, flash cards, etc.
  • ROM read-only memory
  • Such devices can include a computer-readable storage media reader, a communications device (e.g., a modem, a network card (wireless or wired), an infrared communication device, etc.), and working memory as described above.
  • the computer-readable storage media reader can be connected with, or configured to receive, a computer-readable storage medium, representing remote, local, fixed, and/or removable storage devices as well as storage media for temporarily and/or more permanently containing, storing, transmitting, and retrieving computer-readable information.
  • the system and various devices also typically will include a number of software applications, modules, services, or other elements located within at least one working memory device, including an operating system and application programs, such as a client application or Web browser.
  • Storage media and computer readable media for containing code, or portions of code can include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information such as computer readable instructions, data structures, program modules, or other data, including RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a system device.
  • storage media and communication media such as but not limited to volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information such as computer readable instructions, data structures, program modules, or other data, including RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage
  • Embodiments of the present disclosure can be provided as a computer program product including a non-transitory machine-readable storage medium having stored thereon instructions (in compressed or uncompressed form) that can be used to program a computer (or other electronic device) to perform processes or methods described herein.
  • the machine-readable storage medium can include, but is not limited to, hard drives, floppy diskettes, optical disks, CD-ROMs, DVDs, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, flash memory, magnetic or optical cards, solid-state memory devices, or other types of media/machine-readable medium suitable for storing electronic instructions.
  • embodiments can also be provided as a computer program product including a transitory machine-readable signal (in compressed or uncompressed form).
  • machine-readable signals whether modulated using a carrier or not, include, but are not limited to, signals that a computer system or machine hosting or running a computer program can be configured to access, including signals downloaded through the Internet or other networks. For example, distribution of software can be via Internet download.

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Abstract

Système de chargement de produits en vrac dans au moins un espace de stockage, le système comprenant : au moins un convoyeur monté sur une flèche et couplé de manière coopérative pour le transport des produits en vrac de l'extrémité d'alimentation du convoyeur vers une extrémité de sortie du convoyeur ; au moins un bras soutenu par la flèche et positionné à proximité de l'extrémité de sortie du convoyeur, le bras s'étendant depuis le convoyeur et les produits en vrac sur celui-ci ; au moins un capteur LiDAR disposé sur le bras pour détecter des données de capteur LiDAR à partir d'une région de l'espace de stockage et/ou des produits en vrac ; et un processeur configuré pour délivrer en sortie des données pour positionner la flèche par rapport à l'espace de stockage sur la base des données de capteur LiDAR de façon à optimiser la distribution des produits en vrac à l'intérieur de celui-ci.
PCT/AU2023/050716 2022-08-01 2023-08-01 Systèmes et procédés de chargement de produits en vrac dans un stockage WO2024026531A1 (fr)

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