WO2021084231A1

WO2021084231A1 - Crane device provided with data

Info

Publication number: WO2021084231A1
Application number: PCT/GB2020/052681
Authority: WO
Inventors: Theodore Cosmo HOLTOM
Original assignee: Triple LIDAR Technology Ltd
Priority date: 2019-10-30
Filing date: 2020-10-23
Publication date: 2021-05-06
Also published as: GB201915786D0; GB2588650A; EP4051616A1

Abstract

There exists a problem to be solved for cranes since existing cranes continue to suffer accidents and reduced utilisation. A crane equipped with data can provide industrial advantages. A crane equipped especially with LIDAR (Light Detection And Ranging) data provides industrial advantages. Advantages may include improved crane safety and improved crane utilisation, which solves problems of accidents and reduced utilisation. Optionally LIDAR data can be employed for wind measurement. Optionally LIDAR data can be employed for scene mapping and collision avoidance during crane operations. Numerous machine learning opportunities and control opportunities are provided for. Such systems offer improved safety and reduced operational risk. Optionally such systems may provide a look ahead prediction or short term forecast of wind conditions expected to impinge the crane and its suspended load after a chosen time duration. Optionally machine learning can be applied for learning which LIDAR data gives rise to particular operational states including high loading or high risk states. Optionally the LIDAR data may be gathered prior to crane operation for better characterising the local site conditions.

Description

Crane Device Provided With Data

Description

There exists a problem to be solved for cranes since existing cranes continue to suffer accidents and reduced utilisation.A crane equipped with data can provide industrial advantages.A crane equipped especially with LIDAR (Light Detection And Ranging) data provides industrial advantages.Advantages may include improved crane safety and improved crane utilisation, which solves problems of accidents and reduced utilisation. Optionally LIDAR data can be employed for wind measurement. Optionally LIDAR data can be employed for scene mapping and collision avoidance during crane operations. Numerous machine learning opportunities and control opportunities are provided for. Such systems offer improved safety and reduced operational risk. Optionally such systems may provide a look ahead prediction or short term forecast of wind conditions expected to impinge the crane and its suspended load after a chosen time duration. Optionally machine learning can be applied for learning which LIDAR data gives rise to particular operational states including high loading or high risk states. Optionally the LIDAR data may be gathered prior to crane operation for better characterising the local site conditions.

Cranes typically employ an anemometer which is usually of spinning cup type. The anemometer is effectively a single point measurement instrument although it is understood the anemometer may have some spatial extent such as a few centimetres. The anemometer instrument provides a wind measurement at a single location taken to be the centre of the anemometer instrument. The anemometer does not provide a wind measurement at a second location. It is noted that the anemometer is usually mounted at the top of a crane.

It is typically assumed that a standard wind shear profile applies such that the wind speed is monotonically increasing with height and that by measuring the wind speed at the very top of the crane this errs on the side of caution by indicating a maximum wind speed value representing the worst case wind speed across the entire operating range of the crane.

Within crane standard ISO4302:2016 Crane Wind Loads Assessment it is suggested that one may extrapolate the wind speed at 10 metres height up to a height z (metres) by multiplication by the factor (z/1q)^Lq.14 - using a wind shear factor of 0.14 representative of "flat open country". Furthermore it is suggested that it is conservative to make the assumption that the maximum 3-second gust within a 10-minute period will be not more than 40% above the 10-minute average wind speed for that 10-minute period. It is noted that, whilst such assumptions may be reasonable in flat open country crane operations are often not in flat open country. Crane operations in very complex terrain, such as in the presence of a cliff or in a dense high rise urban scenario, may witness quite different wind shear and gust behaviour.

When these assumptions are employed in calculating wind loads for in-service conditions when the crane is within a more complex flow environment, or indeed during abnormally complex weather conditions, then in-service load calculation may not be appropriate. Therefore the application of wind measurement which takes into account local wind shear conditions can offer an improvement in calculating wind loads.

LIDAR (Light Detection And Ranging), and especially Doppler LIDAR, offers an alternative method of wind measurement as compared with spinning cup anemometer normally employed on cranes. Instead of applying simplistic rules of thumb and applying assumptions about wind shear and gust statistics one may use LIDAR to directly measure the local wind conditions including local wind shear and local gust statistics where local means in the vicinity of the intended or current crane operations such as within the maximum crane radius of the crane centre of mass. One may make a LIDAR measurement at a chosen height which could be the maximum height of the crane. One might alternatively choose to make a LIDAR measurement at another height, for instance if one knows that a load of large cross sectional area will be suspended at that height. Measuring wind shear requires measurement at a minimum of two heights. For instance it may be chosen to measure wind speed at the maximum height of the crane as well as at half the maximum height of the crane.

A LIDAR may be a pulsed LIDAR.A LIDAR such as a pulsed LIDAR may employ range gating or timing in order to estimate distance of a measurement.A LIDAR may be a CW (continuous wave) LIDAR.A LIDAR may incorporate a fibre laser.A LIDAR may incorporate a safety shutter.A LIDAR may incorporate focal length control.A LIDAR may be provided with a means of beam steering, or beam direction switching.A means of beam steering may utilise one or more rotating prism.A means of beam steering may be a Risley prism beam steerer.A means of beam steering may utilise one or more rotating mirror or reflective surface.

Furthermore gathering local wind statistics over a period of time allows calculation of average conditions as well as calculation of the variability of those average conditions. For instance the standard deviation of wind shear may be used to indicate the variability of the wind shear factor over time. This approach may be applied to any number of wind statistics, not just wind shear. Without limitation some commonly considered wind statistics include wind shear, turbulence intensity, wind veer, horizontal wind speed and vertical wind speed, flow inclination, and maximum 3-second gust within a IQ-minute period.

Similarly it can be possible to build a frequency distribution of any wind statistic which may be represented as a numeric value. It will be appreciated that pairs of statistical values can have a two-dimensional frequency distribution and N-dimensional vectors of statistical values can have an N- dimensional frequency distribution.

Given a frequency distribution of historic wind data measured at a given site one may assume that as more data is added to the data set then the data set starts to become representative of the local wind conditions. Then one may use the generated local wind statistic frequency distribution in order to predict the probability of witnessing a given value for that wind statistic during a given time interval.

It will be appreciated that various methods of curve-fitting could be applied to a statistical data set. It will be appreciated that various methods of interpolation and extrapolation may be applied.

It will be appreciated that long term trends such as climate change may provide theoretical justification to apply a correction to process past data statistical distributions for predicting future behaviour. Furthermore correlations with long term reference data sets may be used to further modify a wind distribution for a given location.

Within Annex A of ISO4302:2016 are various geographical maps indicating reference storm wind speeds over large geographic regions. It is noted that crane operators making use of this information are not necessarily taking into account local factors such as complex terrain, or urban canyon effects, which could clearly alter the reference wind speed locally. Therefore, when these reference maps are employed for calculation of out-of-service wind loads then the effects of local complex terrain are not being taken into account.

LIDAR (Light Detection And Ranging) refers to a device and a method, both familiar to those skilled in the art. In brief Figure 1 shows three LIDARs measuring wind velocity, and thereby providing wind velcocity data, at a crane deployment location both prior to crane deployment and during crane deployment.

Figure 2 shows a flat top tower crane with a LIDAR mounted at its top where the LIDAR can direct its beam in different directions in order to make measurements at different locations and thereby provide LIDAR data.

Fgure 3 shows a graph depicting machine learning performance increasing over time such that performance reaches an adequate threshold in order to initialise a first functionality and subsequently continues to learn in order to improve performance to a second threshold whereupon and upgraded functionality may be initialised.

In detail Figure 1 depicts the wind velocity 1 at a measurement point which is measured by LIDARs 2a, 2b, 2c with their LIDAR beams 3a, 3b, 3c. The scenario is in complex terrain 5 where the wind may be complex.A crane 6 may be deployed within the same terrain 5' but at a later time. The crane includes a counterweight 9 and a hook block 7 fom which a load may be suspended. The crane has a luffing jib 8. The same LIDARs 2a', 2b', 2c' continue to make measurements during the crane deployment. The LIDAR data collected prior to crane deployment or during crane deployment may be provided to the crane and its control system. The LIDAR data collected prior to crane deployment may be used to characterise the site local wind conditions.

In detail Figure 2 depicts a flat top tower crane with tower 20 and horizontal lattice jib 21 bearing a trolley 23 which may move on rails along the horizontal jib in order to reduce or increase the load radius of the hook block 22.A counterweight 24 maybalance the load moment. The tower crane may rotate about a slew ring at the top or base of its tower. Furthermore a LIDAR 19 may provide the crane device with data.A first LIDAR beam 25 is horizontal.A second beam 26 may be employde at an angle 28 to the horizontal whilst a third LIDAR beam 27 maybe employed at an angle 29 to the horizontal. The LIDAR 19 includes a means of shining its beam and therefore measuring in different directions and at different ranges. LIDAR 19 may rotate around a vertical axis and may employ beam steering or beam switching. Three non-coplanar beams intersecting a region may provide an estimate of the wind velocity within that region assuming that the wind velocity is constant within that region. If the intersection region grows large then the error on the estimated wind velocity may grow large since wind velocity commonly varies especially over large regions.

In detail Figure 3 shows training of a machine learning system, with number of training data on the abscissa 10 and performance or success rate on the ordinate axis 11.After an sufficient number 12 of training data the performance of he machine learning may reach an adequate peformance level 13 such that a new functionality based on the machine learning may be activated. The machine learning may continue to learn (18) until after training on even more data 14 the performance level reaches an even greater threshold 15 whereupon a further functionality or upgrade may be triggered. Initially (17) a machine learning performance may be inadequate but rising toward the ideal performance level 16. This shows how crane control systems may utilise new functionality over time based on machine learning improvement.

Doppler LIDAR involves scattering laser radiation from microscopic aerosols or particles including molecules borne on the wind. The reflected laser radiation has a Doppler frequency shift due to the line of sight velocity component of the refecting particle. Therefore Doppler LIDAR is a form of LIDAR which allows wind velocity measurement. Three independent (non-coplanar) LIDAR beam directions can offer three independent velocity components which allows estimation of the full three-dimensional wind velocity. It is possible to assume uniform wind velocity throughout a volume or a locale of space in which case it may be assumed that three independent LIDAR beams measuring anywhere within that volume or locale of space can provide the uniform three-dimensional wind velocity within that volume or locale of space. Since wind is known to vary over space it is preferable that a three-dimensional measurement be made using data fom within a small locale of space, ideally a point-like locale.

The LIDAR may be pulsed LIDAR. Many pulses may be integrated. It is possible to use CW focused LIDAR where the LIDAR measurement is taken to be at the CW focus centre.

LIDAR detection based on solid objects or hard targets can be applied without need for Doppler processing but simply using time of flight or range gate detection of a reflective surface. Therefore hard target LIDAR or laser mapping may be employed which is also familiar to those skilled in the art. The direction of the laser beam and the range gated or time of flight detection is used to build a surface map.

Potentially LIDAR data may be collected from a vehicle, which could be an autonomous vehicle such as a drone.Alternatively LIDAR data may be collected from a stationary LIDAR device, such as a tripod mounted LIDAR, or a crane- mounted LIDAR.A crane mounted LIDAR may be stationary when the crane is stationary but sometimes moving when components of the crane, to which the LIDAR is mounted, do sometimes move.

Doppler LIDAR offers measurement of the wind whilst hard-target LIDAR offers three-dimensional mapping of the local surfaces and both these forms of LIDAR data may be of advantage to conrol systems used with cranes.

It is noted that a LIDAR transmitter and receiver may or may not share optics.A LIDAR may utilise a transmitter and receiver which are co-located, or alternatively ones which are not co-located.

It is noted that LIDAR data collected locally for a given measurement point locally characterise the wind conditions at that point. It is further noted that LIDAR data collected locally at a number of measurement points can be used to characterise the variability of the wind across those points. For instance two points separated in height are typically used to characterise wind shear (changing horizontal wind speed with height) or wind veer (changing horizontal wind direction with height). Furthermore it can be possible to collect LIDAR data from a plurality of points in space in order to map the wind through space. This provides a volumetric map of a given wind statistic scalar field.

Better characterising site conditions with improved wind data (which may be estimated from LIDAR data) allows better selection of crane because the collected wind data can feed into more accurate wind pressure calculations and/or better crane selection to suit the wind conditions.

A crane can be hazardous and can topple or buckle in high winds even when it is in an out-of-service state. Different crane model selections and different crane model configurations can be more stable than others in an out-of-service state. Therefore site characterisation can be beneficial in estimating crane stability when out-of-service in project planning with regard to crane model and crane configuration selection, particularly in windy locations.

Very windy locations include offshore scenarios, including offshore wind turbine construction. Since onshore wind farms are usually sited in more windy locations then these situations are also particularly relevant, along with high bridge construction, crane operation at high locations above sea level, high rise tower construction and coastal crane operations.

Wind turbine construction is a particularly relevant case especially when cranes lift individual rotor blades, or pre-assembled rotors including one or more blades, because the wind turbine blade is designed to be of aerodynamic shape and will "catch the wind" by generating lift forces accordingly when the wind impinges from a certain direction, speed and angle of attack. Therefore LIDAR data indicating wind speed and also direction can be particularly useful for a crane operator or a crane control system or a crane warning/alarm system, when the crane is lifting a wind turbine rotor blade or a component including at least one rotor blade. The same is true when a crane lifts an aeroplane wing, or in general a component of aerodynamic shape which may generate high lift or drag forces when oriented in a certain way.A crane may include locking devices or devices to constrain the orientation of a suspended load and these devices or systems may benefit from wind data provided by LIDAR systems.

Furthermore, wind mapping over an area which includes more than one possible crane deployment location may highlight preferred crane deployment locations where the wind conditions are found to be less harsh within varying wind flow conditions over the region. Therefore, project risk can be reduced by improving the crane deployment site.

Also, if the crane configuration and/or orientation is known to be within a certain range for a given project then these parameters may be taken into account when estimating risk or loads for a given potential crane deployment position.A prevailing wind direction may be found.A crane may be oriented with a certain angle to the prevailing wind or to the overall polar plot of wind direction (which may be refered to as a wind rose) or with regard to the frequency histogram of wind speed and wind direction (indicating the prevailing direction of especially the higher wind speeds).

It would be possible to combine wind LIDAR data with solid target LIDAR situational mapping. It would be possible to feed LIDAR data into a software tool which considers various possible crane deployment locations, configurations and orientations, subject to constraints and wind variation, in order to optimise the crane deployment location, or identify one or more favourable crane deployment locations/configurations/orientations, or to identify one or more favourable range of locations/configurations/orientations. Road access and ground surface data, as well as other relevant project data (such as location of overhead electricity cables or other hazards) may be taken into account. Crane load transport loci may be taken into account.A crane deployment optimisation model may feed into, or form a component module of, a wider project optimisation model or a Building Information Model (BIM).

Multiple scalar fields may form a vector field. For instance wind velocity component in a Cartesian x-direction (vx), wind velocity component in a Cartesian y-direction (vy) and wind velocity component in a Cartesian z- direction (vz) may be combined into 3-dimensional wind velocity (vx, vy, vz). Therefore we may map the three-dimensional variation in space of a three- dimensional quantity. In other words we may map a vector field by evaluating or estimating (for instance by measuring, possibly by use of LIDAR) the vector at a plurality of points in space.

Although three non-coplanar LIDAR beam directions are required for a three- dimensional wind velocity measurement a single LIDAR beam still offers some information on the wind velocity. Namely, a single LIDAR beam line of sight wind speed indicates a minimum magnitude of the three-dimensional velocity. The sign of the Doppler shift along the line of sight also tells whether the velocity vector is aimed somewhere in a hemisphere toward the LIDAR observer or away from the LIDAR observer. This information may be useful, albeit less useful than a three-dimensional measurement, for a control system or for a machine learning system, including when applied to a crane.

It will be appreciated that LIDAR data may feed into a graphical data presentation such as a Graphical user Interface (GUI) or a Human Machine Interface (HMI). It will be appreciated that LIDAR data may feed into a Building Information Model (BIM). For instance a wind velocity LIDAR data map could form a data layer such as a colour coded heat map which could be useful to a crane operator or to a project manager or other user of the system.A data layer may offer arrows with variable direction, variable length, variable size, variable tick marks or other features.Arrows may be similar to arrows which might be found on weather maps.A visual data layer may employ other visual symbols such as circles of variable size.

Interpolation may be employed between data points of a data map. Map projection visualisation may be shown as a coloured heat map or contour map. LIDAR data wind visualisation may be made available to the crane operator or other personnel on a screen within the crane cab or on a mobile device with a display screen or elsewhere.Wind data alarms or warning threshold excedances may be highlighted symbolically or visually within a BIM or data visualisation layer.

Alternative to, or in parallel with, "Doppler LIDAR", LIDAR mapping such as hard target LIDAR mapping can be used to scan the locality of the LIDAR in order to build up a map such as a three-dimensional map of the local built environment. Such a map may be used for collision avoidance or crane operational planning. This data may be used by an automatic control system. This data may feed into a machine learning process. It can be that "hard target LIDAR mapping" is used interchangeably with the phrase "solid target LIDAR mapping", or "laser RADAR mapping", or "LADAR mapping". Similarly "LIDAR" may be referred to as "LADAR".

LIDAR data, including one or more of wind LIDAR data or solid/hard target LIDAR situational mapping, can offer a crane operator additional decision-making information. For instance this data may offer a signal that start up of crane operation is safe, possibly after a period of wind shutdown or other type of pause or shut down of operations, or for operational initiation.Alternatively the LIDAR data could indicate that it would be a good time to initiate a shutdown and take the crane from an in-service state into an out-of-service state.

By better taking into account the wind data or situational data provided by LIDAR it maybe possible to increase the crane operational period since improved awareness of the true operational circumstances may justify reduction of safety margins, without compromising safety. This has economic benefits by increasing safe utilisation of crane assets and crews. For instance it is commonly the case that cranes are more stable against tipping in one direction over another direction and yet a simple spinning cup anemometer may be used which only offers wind speed and no wind direction information. Making use of wind direction information could allow the operatonal envelope to be extended when wind comes from a favourable direction such as within 90 degrees of the most favourable direction, as opposed to within 90 degrees of the least favourable wind direction. Local statistics on likelihood of sudden wind direction changes may also be acocunted for.

Since the local structural environment may change over time, especially in a changing construction site, a control system utilising hard target situational data may re-scan on a regular or irregular time interval in order to update a collision avoidance system or to update its three-dimensional mapping data utilised within a BIM system.

A crane may employ automatic way points or training by an operator to repetitively and smoothly transfer a load from one position to another where LIDAR situational mapping could be used as an automatic check that the situation has not become unsafe or obstructed. Therefore LIDAR data may offer improved situational awareness or collision avoidance as a tool for automatic crane control or other control systems, or indeed for human observers and decision- makers.

Collision avoidance systems may generate audible or visual or electronic or other alarms when two massive bodies approach one another within a minimum alarm threshold. It will be appreciated that "collision avoidance" can imply the same thing as "hazard avoidance", "collision awareness", "hazard awareness" or "situational awareness".

A LIDAR situational map may be repeated within a (preferably short) interval of time in order to calculate the relative velocity of two bodies. Similarly one may use rate of change to calculate relative acceleration or higher time derivatives of position. Therefore apart from proximity measurement it is possible to use relative velocity or relative acceleration data in control and information systems, including the possibility of triggering alarms when magnitude or component of relative velocity or acceleration of two bodies is below or above a chosen threshold.

One may combine multiple signals to form a resultant signal.Apart from integer or real number values a signal may be a logical binary (such as true or false, Q or 1) or non-binary switch or case value (such as red, amber or green).

It is noted that a single Doppler LIDAR beam offers a means to measure (or estimate) the wind velocity component along the line of sight of the Doppler LIDAR beam. Such a beam will measure zero wind velocity component if the LIDAR beam happens to be orthogonal to the wind velocity vector direction. Therefore using a single LIDAR beam in an effort to understand wind velocity is ambiguous. One may however deduce that the full three-dimensional wind velocity magnitude is greater than or equal to the wind velocity component magnitude as measured along a single beam. To obtain a correct measurement of the three-dimensional wind velocity it is necessary to measure that wind velocity from three mutually non-coplanar LIDAR beam directions to obtain three Doppler components.With knowledge of the LIDAR beam directions one may convert the three Doppler measurements into a three-dimensional wind velocity estimate by means of a 3x3 matrix transformation.

The present invention offers new opportunities in look ahead wind prediction, and look ahead warnings and alarms.

A control system may produce an output which is transmitted audibly (possibly as a siren, or as a verbal message communicated audibly, in any chosen language, via loudspeaker.

Wind data can be of benefit to a crane operator to enhance decision-making. For instance in wind turbine construction, when bringing together a blade to be attached and at the rotor hub, it can be important for the crane operator to have confidence that within the coming few minutes there is not expected to be severe gusts, or turbulence, or extreme wind shear, or extreme wind veer, or other potentially adverse wind conditions. Having additional information about the short term wind conditions incident at the construction site provides confidence and assurance in giving the go ahead for a critical contact moment. Provision of look ahead wind prediction offers advantage generally for many types of crane operations, not limited to the given example of wind turbine blade contact and fixing to hub.

The mobile crane standard EN13QQQ states that "Mobile cranes are normally equipped with jib systems which can be lowered quickly and readily.As a result, the hazards due to sudden changes in wind speeds and increases in gust speed at elevated points can be reduced in a short time, e.g.within 5 minutes". Therefore the look ahead wind prediction capability can be used in accordance with such jib systems in order to inform the operator or automatically initiate a lowering of the jib system. The look ahead information could be used to initiate other automatic or manual interventions such as lowering of a suspended load or indeed any other possible control action.

There are many types of crane design including but not limited to gantry crane (optionally on lower or upper rails), flat top tower crane, tower crane with luffing jib, crawler crane, crawler crane with luffing jib,

A derricking motion or a luffing motion may permit a crane jib to rotate around an axis (which is typically a horizontal axis). This has the effect of raising or lowering the tip height of the said crane jib. Simultaneously this has the effect of reducing or increasing the (horizontally measured) load radius or (horizontal) distance of the tip of the crane jib from the said axis.

Commonly a crane is operating on Earth within a vertical gravitational field. In this case a load radius may be considered to be the horizontal separation of the load from a pivot axis.A crane in equilibrium will have force moments balanced.

A force moment may be provided by a suspended load.Another force moment may be provided by a counterweight. Further moments of force may be provided by the weight of the crane structural components including but not limited to the hook block and the jib, auxiliary jibs, the crane operator and the crane operator cab. Further moments of force may be provided by ground reaction forces.

Generally the weight of the crane and any suspended load must be balanced by reaction forces at the ground. It is important in crane operations that the ground does not give way. Frequently in crane construction a hard standing may be constructed upon which a crane may operate. Methods of spreading the force and reducing ground pressure may be employed such as outriggers with large area feet, use of many wheels, or use of large area tracks.

It is noted that a crane may operate in zero or low gravitational field sich as in space on a space vehicle, or on a small planet, moon, asteroid, or comet.A crane may also operate underwater or in a pressurised fluid where upthrust forces may effectively reduce or somewhat counterbalance the suspended load.A crane may also carry a load of very low density such that buoyancy forces are exerted on the crane load and where such buoyancy or upthrust forces are greater than the load weight. In this case a crane may be holding down the buoyant load. For instance a crane may hold an airship in place.An airship may be a cargo carrying airship or a passenger carrying airship.

Cranes frequently employ ropes or cables which may be made from steel or other materials.A rope or cable may be held at a non-vertical angle such that the tension force along the rope or cable is non-vertical and the load moment may be calculated with reference to a non-horizontal radius along a line from the pivot point which is orthogonal to the rope or cable in question.

A control system may perform calculations in order to adjust position of crane sub components, especially the position of one or more variable position counterweights, in order to achieve or maintain stable equilibrium.

A control system may perform calculations in order to estimate or adjust the centre of mass position. Calculations may take into account the centre of mass of the loaded crane or the centre of mass of the unloaded crane or both. It is generally important to avoid the centre of mass moving beyond a tipping point. For instance for a crawler crane it could be important to avoid the centre of mass moving horizontally beyond the horizontal rectangle defined by the crawler footprint. It is possible to employ outriggers in order to increase the footprint of the crane which allows a wider range of movement of the centre of mass. A crane centre of mass may move due to a swinging suspended load including that of a hook block.A crane centre of mass may move due to the raising or lowering of a suspended load.A crane centre of mass may move due to the luffing motion of a jib.A crane centre of mass may move due to a telescopic jib extening or retracting.A crane centre of mass may move due to a hook trolley motion where the hook is suspended from a rail carriage such as on a flat top horizontal boom tower crane.A crane centre of mass may move due to the ground starting to give way.A crane centre of mass may move in case that a component of the crane starts to fail or buckle or deform either elastically (reversibly) or plastically (irreversibly).

Swinging loads may be of particular relevance at container handling ports during inclement weather.A LIDAR system may be used for dyamically and precisely mapping the position and orientation of a suspended load such as a container.A Doppler LIDAR may measure or map wind conditions in relation to suspended loads such as containers. Therefore swinging crane load motion may be damped by an anti-swing or anti-collision control making use of LIDAR data. This may be of particularly useful application in stacking containers, or other activities where precise load location is important.

Data may be employed in order to describe or calculate a swing envelope volume, or a predicted swing envelope. This estimated or predicted swing envelope volume shape, size and position may be used for collision avoidance.

Because of having large surface areas subject to high wind pressure, storm conditions can be of particular relevance to large gantry cranes on rails or on wheels, with or without a suspended load. Such cranes are commonly used in port operations including container handling. Locking systems or other safety sequences may be actuated or triggered in case of particular wind conditions where such wind conditions are measured by LIDAR. Especially with the approach of a typhoon, whilst hours ahead weather forecast may inform the crane operator for a likely need to shutdown very soon, LIDAR data collected locally with perhaps a 5-minute look ahead may allow the crane to shutdown optimally for the chosen and intended shutdown wind conditions, not too early and not too late.

It is important that, when employed, outriggers are strong enough to withstand operational forces. It is important that crane jibs are strong enough to withstand operational forces. It is important that crane foundations or hard- standings are strong enough to withstand operational forces. It is important that cables are strong enough to withstand operational tension forces. It is important that all structural components of a given crane are strong enough to withstand operational forces.

A crane may be a slewing crane, including bottom-slewing crane where the main crane jib is mounted on top of a slew ring allowing rotation around an axis which is often a vertical axis, and including a top-slewing crane where the slew ring is at the top of a structure such as a tower where only the crane portion at the top of the structure or tower will rotate around the slew axis or slew ring.

Cranes may be mounted on vehicles including but not limited to crawler vehicles, wheeled lorries, trucks, trains, aircraft, helicopters, spacecraft, space shuttle, ships, boats and submarines.

Cranes may include one or more counterweights. Counterweights may be at a fixed mounting point on the crane. On a tower crane counterweights may be fixed at the top of the tower. Counterweights may be adjustable in weight and may be mounted on retractable arms which allow adjustment of their load moment.A retractable arm may be of telescopic type or of jointed type such as an elbow.

Counterweights may be fixed to a rail system in order to adjust their position. Alternatively counterweights may be mounted on independently steered vehicles. This allows the counterweight load moment to be adjusted.

It is appreciated that there are various methods by which counterweights may be motorised or transported from one position to another, including but not limited to diesel engine, petrol engine, electric motor, hydraulic ram or pneumatic piston. It will be appreciated that cranes may make use of systems of energy storage such as electrical battery, fuel tank, fly wheel. It will be appreciated that fly wheels or other form of energy storage may capture gravitational energy when loads are lowered. Principles of regenerative braking may be applied for energy capture.

In the case of helicopters we may consider a winch system to be a type of crane. In the case of marine vessels we may consider an anchor to be a type of crane.

There are many different crane applications including container handling, coastal dredging, river dredging, marine services, crew transfer, and equipment transfer.

There are many different working environments for cranes including high rise urban construction, port or harbour operations, offshore vessel, offshore platform with fixed foundation environment, offshore floating platform environment, onshore environment, space environment within Earth orbit, general planetary environments, Moon environment, underwater environment and general fluid environment such as within a fluid chemical vat.

A crane may operate underwater or within another liquid or liquid mixture.A crane may operate on an asteroid or comet or meteorite or on another planet where the atmosphere may be a fluid other than air, of sparse or high density, of varying gas or gas mixture.A solar or stellar wind may be considered as a form of fluid.

Cranes of all types of design, all types of application and in all working environments are commonly working in a fluid, typically air, and due to the fluid pressure exerted on the crane structure and any suspended load the crane operation is commonly limited within a maximum permissible fluid flow speed, typically a maximum permissible wind speed. Since most cranes operate within the fluid of air this text will refer often to wind speed or wind velocity but it will be appreciated that this concept may be generally extended to other fluid speeds or fluid velocities.A fluid can be a liquid or a gas.An ionic plasma may be considered as a fluid which is at least partially ionised. It is also possible for a crane to work in a vacuum or near vacuum (such as very low density gas, such as the upper atmosphere of a planet).

The present invention refers to cranes of all types of design, in all types of application and in all types of environment, not limited to those types explicitly described or provided as examples. The present invention refers to all types of control system, not limited to those types explicitly described or provided as examples. The present invention refers to all types of LIDAR data (or RADAR data, or SODAR data, or SONAR data), not limited to those types explicitly described or provided as examples.

The present invention offers many new opportunities in crane control. Existing control systems may be improved by utilising the LIDAR data.

A control system may adjust one or more counterweight position.A control system may initiate a safety action or sequence of actions. The safety action may involve lowering a suspended load.A control system may initiate an action or sequence of actions which takes the crane from an in-service state to an out-of- service state.A control system may provide an actuator signal for oscillation damping.A control system may provide an actuator signal for constraining to a range one or more aspects of hook or suspended load motion (including rotation angle around any chosen axis, and including position, including hook height).

A control system may set upper or lower limits on one or more jib angle.A control system may output a control set point for jib angle.A control system may set upper or lower limits on the extension of a telescopic jib.A control system may output a control set point for the extension of a telescopic jib.A control system may set upper or lower limits on the slew angle of a jib.A control system may output a control set point for the slew angle of a jib.A control system may set upper or lower limits on the hydraulic pressure within a crane hydraulic system.A control system may output a control set point for the hydraulic pressure within a crane hydraulic system.A control system may set upper or lower limits on the travel range of a jib trolley (including where a jib trolley may suspend a hook and travel on rails).A control system may output a control set point for the travel range of a jib trolley (including where a jib trolley may suspend a hook and travel on rails).

A control system may dynamically adjust control thresholds (such as maximum permissible wind speed threshold) based on the LIDAR data.A control system may estimate or calculate a LIDAR measurement uncertainty which is taken into account when calculating control thresholds. Calculation of LIDAR measurement uncertainty may take into account one or more of (i) LIDAR beam angles, (ii) LIDAR beam angle uncertainty, (iii) Doppler frequency signal processing uncertainty, (iv) alignment angle accuracy.

A control system may provide a "LIDAR-boost" to increase the maximum permissible load or lift radius or operational speed where LIDAR data indicates that conditions are appropriate to do so, whilst maintaining safe operation. It can be possible to directly test a such a "LIDAR-boost" system by measuring loads using load sensors and comparing loads and lift constraints performance when LIDAR boost is activated or disabled. In this way a "LIDAR boost" system may be shown to customers to increase operational utilisation or capability without compromising safety.

Apart from automatic control actions the LIDAR data may provide a human operator with additional decision-making information which may give confidence and improve certainty.

A control system may or may not be of a type referred to as a Rated Capacity Indicator / Limiter (RCI/L) which is well known in the art. The provision of LIDAR data can offer new functionality for RCI/L or other control systems.

Sometimes multiple cranes are employed in a complex lift of a common load. It can be possible for a control system to utilise LIDAR data for governing mutliple cranes within a complex lift. It is possible for an individual crane control system to utilise LIDAR data for participating in a complex lift in cooperation with one or more other crane. It is possible that such control systems include transmission or broadcast of data to one or more other crane control system, or to receive data from one or more other crane control system.

The present invention offers new opportunities in machine learning. Many machine learning methods, including neural network methods, are known by a person skilled in the art and could be applied within the present invention. Machine learning may also be referred to as "Artificial Intelligence" or "AI".

A crane may have a control system which employs a machine learning component whereby LIDAR data is fed as input data into a machine learning model such as a neural network. It will be appreciated that there are many types of machine learning and indeed there are many variants of neural networks.

One embodiment of a neural network is a multi-layer neural network, or a multi layer peceptron. In such a network the neurons are arranged in layers and neural connections are typically between adjacent layers.A multi-layer neural network can be capable to handle non-linear problems whereas a single layer neural network is capable only of linear separation.A multi-layer neural network, especially one with many layers, may be referred to as a "Deep Neural Network" and may be said to be capable of "Deep Learning", which is a subset of machine learning.A "Neural Network"(NN) may be referred to as a "Neural Net" or an "Artificial Neural Network" (ANN).

The number of neurons in a layer or in a neural network can be adjusted. The interconnectedness of the neurons may be adjusted. Neural connections between a first neuron and a second neuron may have associated a weighting factor which may be applied in some way such as multiplicatively to the output of a first neuron en route to input at a second neuron.Activation functions may be applied to the neural processing of data.

Techniques such as backwards propagation are known to a person skilled in the art and may or may not be applied within neural network machine learning.

Methods of supervised learning and unsupervised learning are known to a person skilled in the art.

Supervised learning may involve provision of both input data and the corresponding output data to a machine learning system (possibly a neural network) and repeating the process including a process for evolving the machine learning system (possibly a neural network) weighting factors to account for each additional training case. Testing the predictive success of a machine learning system (possibly a neural network) can be done by providing only the input data and then comparing the corresponding output data (from a real system, or system to be predicted) with the output provided by the machine learning system (possibly a neural network). If they agree then this is a success.

Testing with a population of input and corresponding output sets allows calculation or estimation of a success rate which is the number of successes divided by the number of test cases.Where applicable, in case of a logical output, a false alarm rate may also be estimated which could be the number of false positives divided by the number of test cases.

We may define terminology of a machine learning "performance rate" to refer to either or both of "success rate" and "false alarm rate" for the machine learning system.

Note that we may consider the output provided by the machine learning system (possibly a neural network) to be a predicted output which may be compared with the true output quantity corresponding to the given input case.A "true output" may be an output which is observed in a real system when that real system is subject to an observed set of (real) inputs.

It is noted that observed input or output values may be mis-read since it is possible that an observation may be faulty.

It is noted that the input data for a machine learning system (possibly a neural network) may be an estimate of one or more true value. For instance when we take a measurement of a physical quantity we cannot generally access the true physical quantity and the sensor or measurement device is subject to experimental error. Therefore measured quantities may be considered as estimates of true underlying quantities. Similarly the output data with which we train or test a machine learning system may be subject to measurement or estimation uncertainty and may be considered as an estimate for the output quantity or quantities.

It is noted that measured or estimated quantities may have associated with them uncertainty or error estimates and that these quantities may also be taken into account within machine learning systems. For instance the uncertainties of a first input data vector may constitute a second input data for the machine learning system.

Similarly the output data uncertainties associated with a first vector of outputs may constitute a second vector of outputs. In this way it is possible for a machine learning system (such as a neural network) to predict outputs and also to estimate an uncertainty on those predictions.

Input data and output data may be single data items or vectors or arrays of many data items. Input and output data may be of any data type including but not limited to binary, integer, real number, logical, or case option.

It can be possible to divide an input and corresponding output data set into two or more partitions.One may train a neural network on any or all of the partitions.One may test the success rate or false alarm rate of a machine learning system (possibly a neural network) trained with on a first partition by using the data of a second partition and vice versa.

If a machine learning system (possibly a neural network) is found not to perform adequately then it may be possible to gather further data for further training and then re-test hoping to find improved performance. If a neural network is found to have adequate performance then it is possible to start utilising the neural network for its chosen purpose and to continue gathering input and output data for further training in the hope of improving the performance further beyond "adequate". For instance a crane controller utilising a neural network could invoke new functionality once the neural network success rate was deemed adequate, perhaps with 95% success.The functionality may be to trigger a new type of informational warning for the operator.

After continued data collection during the coming weeks of crane operation it might be found that the machine learning system (possibly a neural network) had reached success rates of 99% at which case the control system may invoke additional functionality such as an operational alarm message and a matching automatic sequence of events such as a safe shutdown.Therefore there may be one or more success thresholds indicating firstly a sufficient level of training and then subsequent upgrades.One may conceive of providing neural network software which will invoke advantageous functionality after an initial training period but continues to gather training data which may be used for further training or further testing and that when the neural network parameters (such as neural pathway weighting factors) reach another level.

Performance rates (including success rates or false alarm rates) may be uncertain such that one may estimate a success rate and one may also estimate a success rate uncertainty.A method for estimating success rate uncertainty can be to partition the overall available test data which was not used for training the network.And then one may calculate different success rates for the different test data partitions such that the average of those success rates may be taken as the overall success rate estimate whereas the standard deviation of those success rates may be taken as the overall success rate uncertainty.

It will be appreciated that checking whether a performance rate is above a performance threshold is equivalent to checking whether the negative of the performance rate is below the negative of the performance threshold where the negative of the performance threshold may be considered to be an alternative performance threshold.

Where the output data may consist of a vector of data items rather than a single data item it will be appreciated that a minimum threshold of machine learning success may be applied to each element of the machine learning output vector. It may be the case that for a given machine learning system (possibly a neural network) version each output element may have its own performance rate which may differ from the performance rate required of other output elements for a given machine learning version release.

It can be possible to undertake machine learning system (possibly a neural network) optimisation for one or some of many outputs by ignoring or "switching off" some or all of the other outputs during training. This is equivalent to setting up the machine learning system (possibly a neural network) for only one or some of the many outputs.When training a machine learning system (possibly a neural network) for a vector of outputs then one may employ a norm or measure of the distance between two vectors within a vector space as a measure of closeness between the machine learning system (possibly a neural network) output vector and the target (training or test data) output vector.

In case of series production of a given crane model it could be possible for a crane manufacturer to evolve a neural network over time and incorporate the neural network processing into successive software version releases or customer upgrades as the performance is improved.

It could be possible that industry standards bodies gather data or require provision of certain data in order to employ machine learning for deciding on new crane structural integrity requirements, design standards or new safety functionality. This could provide a new and additional machine learning mechanism for improving industrial standards. It is noted that adoption of machine learning does not imply that human learning is not beneficial. Crane operator experience contnues to be valuable. Both human and machine learning may be employed by a control system. Human learning can be good at some types of learning and machine learning can be good at other types of learning. In particular machine learning can be advantageous when it comes to fast processing of vast numeric data sets.

One embodiment of a neural network can be a massively interconnected neural network where neurons are not necessarily arranged in layers.After all the human brain neurons are not strictly arranged in layers. It can be the case that when one neuron is activated other "nearby" neurons may also be activated. This may be embodied within a mathematical computing neural network by defining a distance norm defined on individual positions of the respective neurons and by a mathematical processing which adjusts or amplifies the corresponding neural weights.An amplification factor in principle may be any real number.

Fluid flow characteristics estimated from LIDAR data may be provided as input to the machine learning model along with loads data from loads sensors as outputs. The machine learning model may be designed to learn what are the input data characteristics which give rise to greatest loads within the crane structure.

Machine learning may feed into a control system warning or alarm, or initiate an automatic control sequence. Machine learning may feed into a model for improving crane structural design or optimisation

Successful machine learning is achieved when the machine learning system is able to predict the loads arising (or predict that the loads are above a warning/ alarm threshold threshold) with a high level of success (and a low level of false alarm). If the learning is insufficiently successful then more training data may be required. However it may be that the input data is dimensionally insufficient to determine the outputs. In that case it should be recognised that further relevant data attributes may be required for successful machine learning. In general successful machine learning is achieved when the machine learning system is able to predict the outputs based on given inputs with a high level of success. It will be appreciated that any available data could be employed as input data and output data and there can be any number of inputs and any number of outputs. In the tower crane case of a tower crane with luffing jib there are many known important parameters and it is likely that one or more further wind parameters and crane parameters may be needed as input in addition to the LIDAR wind speed in order to successfully train a neural network - height of LIDAR wind data, distance of LIDAR wind measurement, suspended load radius, luffing jib angle, tower height, tower slew direction of luffing jib with respect to north, wind direction with respect to north, wind speed, suspended load height, suspended load weight, turbulence intensity, vertical wind shear.

The example of a tower crane with luffing jib is just one crane example and the principles of machine learning may be applied to many other types of crane scenario. In each case one may need to consider what are the input parameters which can be expected to be significant in the machine learning problem. Therefore some expert or human reasoning in defining the neural network data may be helpful toward efficient machine learning.

There may be different objectives of machine learning. The objective of predicting elevated loads from the LIDAR wind data has been discussed.Another objective of machine learning could be to take look ahead LIDAR wind data from some kilometres away and predict an output which are the wind parameters nearby to the crane at a chosen time duration later.

A database may include data correspdonding to a single crane, or a set of cranes, or a set of cranes denoted by a common model number, or cranes of particular a type, or all cranes in general.A database may be chosen as training data (and test data) for machine learning.

Machine learning could be crane specific.A crane could continue to learn throughout its operational lifetime by keeping a historic log of its relevant parameters. Such historic data logging and machine learning may be especially interesting to the crane owner and operator, or other industry participants.

Alternatively machine learning could be crane model specific where operational data from all crane models of a given type is transmitted to a central database. This may be especially interesting to the crane manufacturer or other industry participants. This information could be used to highlight possible differences or abnormalities of a specific crane from its peer group.

Alternatively machine learning could assist with crane structural design by including in the data logging a generalised description of crane configurations and main structural design parameters. Such a database might be maintained by crane manufacturers or an industry body or another industry participant. In this case machine learning could be applied in order to optimise the structural design of cranes. By including in the data logs also the main parameters defining the application it could be possible for machine learning to learn what crane parameters are most suitable for different categories of application. This could allow improved crane selection for a given application. Such a database and machine learning tool might be maintained by a crane rental company or other industry participant.

It could be possible for a manufacturer or other industry participant to maintain one or more operational cranes with which to gather operational data for machine learning in order to evolve the crane control parameters. This could allow the provision of controller upgrades for other cranes of the appropriate model or type every so often as the manufacturer sees fit or beneficial.

A neural network may have many outputs. Therefore it should be possible to simultaneously train and test a neural network for recognising what LIDAR data gives rise to elevated loads from multiple sensors which may include also elevated vibration levels from vibration sensors or accelerometers or microphones, rope or cable tension sensors may be employed, strain sensors may be employed.

Different types of data logging may be employed such as event based logging when an alarm is raised, or regular time series logging to monitor a given observable over time. In principle machine learning should be able to handle all types of data.

It will be appreciated that there are many possibilities to list here for machine learning data inputs as well as machine learning data outputs as well as machine learning method and these include the use of LIDAR data of one form or another as part of the input data for machine learning within a crane control system.

It is also noted that cranes may benefit from machine learning using data which does not include LIDAR data.

Data, such as LIDAR data, may be collected in order to characterise local site conditions, including where some or all of the LIDAR data is collected prior to deployment of the crane. In one instance characterising site conditions may be aimed at characterising site wind conditions. In another instance characterising site conditions may be aimed at situational mapping of local structures and obstacles.

Data, such as LIDAR data collected in order to characterise site conditions, may be processed in order to calculate statistics relating to the conditions (such as wind conditions) including but not limited to mean, standard deviation, peak position, peak width, peak full width half maximum. Data may be processed including a curve fitting process by various methods including but not limited to least squares fit.A curve fitting function may include any of, but not limited to, Weibull distribution, Rayleigh distribution, Normal distribution, Gaussian distribution, Poisson distribution, polynomial function or exponential function. Data processing may include interpolation or extrapolation of curve fitting beyond the collected data range or within gaps in the collected data range. Data may be bin averaged or arranged in frequency histograms. Some or all of the data may be binned and may be bin-averaged per data bin and standard deviations may be calculated per data bin.

LIDAR or wind data may include one or more of the wind shear distribution, the wind veer distribution, the horizontal wind speed distribution, the wind direction distribution, the wind gust distribution, the turbulence distribution, the non-horizontal wind flow angle distribution, maximum permissible wind speed, wind speed measured or estimated at the crane itself, wind speed measured or estimated at a defined position relative to the crane.

LIDAR or wind data may include any statistical descriptors of wind distributions including statistical mean, statistical standard deviation or variance, peak, or full width half maximum.

Once data, including statistical distribution data, has been gathered in order better characterise conditions, including wind conditions, at a site then it may be possible to select a more appropriate crane for the site, and it may also be possible to generate new alarms or warnings for that crane deployed at the given location. For instance a warning may be generated when the operational height wind speed increases above the wind speed cumulative distribution 95% level with reference to the wind speed frequency distribution. Other data including but not limited to wind direction and crane centre of mass may also be comibined with such warning signal to generate further warnings or alarms.

It is noted that not all warnings need be reported to the crane operator. Different warnings and alarms may be ascribed different severity levels and only the highest severity or otherwise selected warnings and alarms need be reported to the operator or other user of the system.A severity rating may be ascribed a numeric value such as a real number, a natural number or an integer value.A historic data log may be maintained including some or all warnings and alarms, optionally including severity levels, and their date-time-stamps. A historic data log may include other data, such as crane data, or LIDAR data, or wind data.

A look ahead LIDAR system may raise an alarm or warning if a wind attribute (measured at some estimated distance/estimated time away such as 5-minutes away), including but not limited to when IQ-minute wind speed, or 3-second gust level within a 10-minute period, rises above a threshold such as beyond 99% of the usual site distribution for that attribute. This alarm or warning may prompts the operator, or the control system, to shut down an operational crane to an out-of-service state, or to otherwise secure the crane, especially where this shut down or security action is possible within the estimated look ahead time such as 5-minutes. Different look ahead times may be employed for different warning levels. The system could elevate warnings or alarms to higher severity ratings when longer range look ahead observations are confirmed at a shorter range. For instance a 5-minute look ahead observation of a dangerous gust might be confirmed at a 3-minute look ahead observation, implying an increase in warning/alarm severity rating. It may also be possible that a dangerous gust from 5-minutes look ahead is not confirmed, or is seen to reduce in severity, at the 3-minute look ahead location in which case the warning or alarm severity rating may be unchanged or even reduced.

It is noted that more advanced wind data better characterising sites can feed into improved architecture and aerodynamic design of buildings and structures. This may include better solutions for cladding and roofing methods. By accounting more specifically to the local weather and wind conditions one can design more optimally for the local conditions so that the building is engineered appropriately with a long lifetime. One can also avoid over engineering with unnecessarily expensive material if the wind conditions of a given site produce less fatigue than average. Therefore it will be possible to engineer buildings cheaper and it will be possible to engineer buildings to be more robust. Inherent in this is to exprapolate a locally collected wind data distribution to a long term wind data distribution expectation.

Similarly crane structural design or crane selection can also be tuned to the specific site by utilising local site data including locally collected LIDAR data. Therefore crane selection and procurement for a given job can be made cheaper if the site conditions are found, by using new data, to produce less fatigue or less risk of catastrophic failure than otherwise expected.And it will be possible to procure cranes that are more robust, and therefore safer, where site conditions are found to require it.

It is noted that a machine learning system may learn what type of wind inflow gives rise to greatest fatigue loading of a crane. Therefore such a system could eventually be used in order to minimise or reduce the fatigue loading of the crane when viable actions may be initiated by the system in order to reduce the loading. This could enable crane assets to have a longer safe working lifetime and thereby add value to the crane asset (owned by a crane owner) or crane product (as sold by a crane manufacturer).

The LIDAR data or the processed LIDAR data or statistical quantities representative of the LIDAR data may be used in a control system which tunes or adjusts the behaviour of the control system to match the local site conditions.

A control system may employ SODAR data, SONAR data, RADAR data, meteorological mast data, satellite remote sensing or other data which has been collected prior to crane operations so as to characterise the local site conditions. This may be used in addition to or in combination with LIDAR data. LIDAR data may be used to set or limit crane configuration options including but not limited to maximum jib length, maximum luffing angle, maximum safe load, maximum operating radius, maximum lift height, minimum/maximum counterweight radius, reeving or pulley configuration, or to set a crane operating mode.

One or more LIDAR may be mounted to the crane structure itself. For instance the LIDAR may be mounted to a lattice tower or boom.

A data link of any type may be provided for connecting the source of LIDAR data to the crane or its control system or to an associated machine learning system.

A data link may be a wireless data link (such as microwave, RF or mobile phone connection), or an optical fibre data link, or a wired data link such as a coaxial cable link. One or more LIDAR may be mounted on the crane structure, including the uppermost part of the crane structure, with a data link to a control system which is elsewhere within the overall crane structure including the operator cab.A data link may connect one or more LIDAR which is remote from the crane.

A LIDAR may be mounted on a single stage or multiple stage motorised platform such as a Pan Tilt Unit (PTU). If such a motorised platform is mounted to a luffing jib then the motorisation can be used to level the LIDAR platform effectively cancelling the jib angular motion such that the LIDAR field of view remains level or at fixed angle with respect to the horizontal plane.

One or more LIDAR may be mounted on a luffing jib or on a slewing portion of a crane.Angle sensors may be used to automatically sense the luffing jib angle. Angle sensors may automatically sense a slew angle.

Various methods for measuring or sensing angular orientation include but are not limited to GPS/ satellite positioning, angle encoders including optical angle encoders and magnetic angle encoders, or sensors utlising one or more of accelerometers, gyroscopes and magnetometers.

A LIDAR may employ beam steering to aim at a chosen point and the beam steering control system may be provided with slew angle from a slew angle sensor (which may be from a slew angle encoder, or from a GPS/satellite positioning based orientation sensor, or other angle sensor). Such a system may employ GPS or satellite positioning data including where satellite positioning data is used for providing orientation.

It will be appreciated that using LIDAR for measuring the wind velocity at a defined point or locale in space requires accurately aiming the LIDAR beam through the chosen point or locale. Therefore beam steering systems may take into account the target displacement with respect to the LIDAR beam steering unit position. The beam steering unit must be carefully aligned with respect to some reference basis or axis system. Then the beam steering system may divert the LIDAR beam according to demanded angles with respect to the beam steering reference axes. If the target locale is very small or very distant then this implies a limit on beam steering angle uncertainty. From another point of view one may say that the beam steering angle uncertainty implies a limit on the LIDAR measurement range if we set a requirement to make a LIDAR beam measurement within a locale of limited size.

There may be more than one contributor to the beam steering angle uncertainty. For instance any angle sensor has its own angular resolution. One may use measured angles for calculating beam steering demand angles.Any beam steering system will have a limited resolution with which it is able to fulfill a demanded angle which is an additional source of error in the overall beam steering. If the beam steering system is mounted on a motorised platform then the motorised platform will have its own resolution with which it may fulfill the demanded angle, perhaps using motor angle encoders. Therefore accurate beam steering requires accurate sensors, accurate calculation and accurate actuators.

It should be noted that a crane will generally have its own control system. It should be noted that a LIDAR may have its own control system. It may also be the case that a LIDAR beam steerer has its own control system. One should not confuse these control systems each of which may have its own specific purpose. For instance a crane control system may have an objective purpose of maintaining safe crane operations within operating constraints, whereas a LIDAR control system may have the purpose of producing a LIDAR measurement, whereas a beam steering control system may have the purpose of steering the (LIDAR) beam to a demanded angle with respect to a reference basis. Unless otherwise specified "control system" within the claims will refer to a crane control system.

It can be possible that the control system offers functionality to manually enter, or over-ride or type in data values or settings. For instance a crane operator could type in the mass and aerodynamic drag coefficient of a load to be suspended.

A control system such as a crane control system may make use of weather forecast data, meteorological mast data, satellite remote sensing data, crane operational data, historic data, crane specification data, crane-specific data, crane model- specific data or crane category-specific data.Any type of data may be transmitted to the control system from another device. It can be possible that the control system offers a communication interface to receive data values or settings provided by another device. For instance the control system may have an interface to receive data transmitted from local weather stations, or from locally deployed LIDARs equipped with suitable data communications capability.

It can be possible for a crane control system to operate during crane installation as the crane is itself constructed from sub components and also during decommissioning as the crane is being dismantled. The installation and decommissioning of cranes can be a complex process which is sensitive to wind conditions and the utilisation of LIDAR data in controlling such activities can be of safety benefit.

A control system such as a crane control system may utilise a static or dynamic model of calculated or measured quantities including any one or more of: loads, hydraulic pressure, temperature, ground pressure, tension, hook load, centre of mass position, swinging load position, swinging load orientation, crane component position, crane component orientation, aero-dynamic / fluid-dynamic lift and drag forces, wind pressure, finite element model parameters, bending displacements, bending angles, vibration levels, spectral response, resonances, load sensor data , vibration data, position sensor data, angle sensor data, crane configuration data, number of reeves employed or pulley configuration, suspended load weight data, suspended load shape data, suspended load aerodynamic drag coefficient, crane structure data, jib configuration data, telescopic jib data, telescopic extension data, slew angle, counterweight weights, or counterweight positions.

A control system such as a crane control system may utilise any type of crane data.A control system may utilise any type of wind data.A control system may utilise any type of environmental data including but not limited to meteorological data, situational mapping data.

A control system such as a crane control system may utilise specification data such as crane specification parameters and/or load specification parameters.

A control system such as a crane control system may utilise standard or regulatory parameters or limits. A control system such as a crane control system may employ a Look Up Table where one dimension of the Look Up Table employs either the LIDAR data or else a quantity produced by processing the LIDAR data (for instance wind shear calculated from LIDAR, or wind veer calculated from LIDAR).

Similarly a Look Up Table may employ in a given dimension data corresponding to any one of: maximum load, maximum load radius, load, hydraulic pressure, temperature, ground pressure, tension, hook load, centre of mass position, swinging load position, swinging load orientation, crane component position, crane component orientation, aero-dynamic / fluid-dynamic lift and drag forces, wind pressure, finite element model parameters, bending displacements, bending angles, vibration levels, spectral response, resonances, load sensor data , vibration data, position sensor data, angle sensor data, crane configuration data, number of reeves employed or pulley configuration, suspended load weight data, suspended load shape data, suspended load aerodynamic drag coefficient, crane structure data, jib configuration data, telescopic jib data, telescopic extension data, slew angle, counterweight weights, or counterweight positions.

A Look Up Table may be considered to be a type of static model or dynamic model. A Finite Element Model may be considered to be another type of static or dynamic model.

It is noted that "control set point" can refer to a "control demand" or a "control output" which terminology may be used interchangeably without limitation. It is also noted that a "control limit" may be a "control upper limit" or a "control lower limit", including both physical or numeric limits.A control limit may be considered as a type of control set point.

It is noted that one or more LIDAR may be remote from the crane structure itself, such as on a ground mounted tripod or on a nearby rooftop.

It will be appreciated that LIDAR data may be substituted by one or more of SONAR data or SODAR data or RADAR data.

The claimed invention covers both devices (or apparatus) and corresponding methods. The claimed invention also covers corresponding computer systems and computer programs or instruction sets.

There are many types of crane including but not limited to: tower crane, flat top tower crane, luffing jib tower crane, fixed angle jib tower crane, top slewing tower crane, bottom slewing tower crane, mobile crane, crawler crane, wheeled crane, crane on rails, gantry crane, port crane, harbour crane, container handling crane, barge crane, dredging vessel crane, offshore jack-up vessel crane, marine crane, offshore platform crane.

A crane may be a crew transfer platform or bridge, or an equipment transfer platform or bridge.A crane may include a motion compensation system such as a sea motion compensation system on an offshore vessel when transferring crew or equipment from one vessel to another platform (which may be fixed or also in motion).

A crane may incorporate a hose or pipe transmitting a fluid such as water, or another liquid, or oil, or air, or hydrocarbon gas, or inert gas, or another gas, or fluid cement, or fluid concrete.

An air-to-air refuelling system, incorporating a retractable fuel pipe, may employ a crane or may be considered as a type of crane between two moving vehicles.A fluid transfer pipe arranged between two vehicles such as between two ships at sea may employ a crane or may be be considered as a type of crane. Generally a retractable arm which transfers a load fom one position to another may be considered as a type of crane. It is also noted that a "crane" may or may not include one or more telescopic jib, one or more luffing jib, one or more counterweight. It is also noted that a "crane" may or may not be of self-erection or climbing type. It is also noted that a crane may or may not include one or more jib made from modular sections and which one or more jib can be adjusted in length by use of varying numbers of modular section pieces.

It is known that "wind data" may refer to any one or more of:wind speed, wind velocity component, wind velocity, gusts, wind direction, horizontal wind direction, horizontal wind speed, turbulence, turbulence intensity, horizontal wind shear, horizontal wind veer, vertical wind shear, vertical wind veer, wind pressure; as well as statistics thereof (including but not limited to arithmetic mean, standard deviation, IQ-minute temporal average, 3-second temporal average) and statistical distributions thereof.

The term "LIDAR data" may refer to any type of "wind data" as measured by a LIDAR, or it may refer to any type of "LIDAR system data" or "LIDAR system parameter".

It is known that "crane data" may include any one or more of, but not limited to, the following quantities: loads data, length data, lift radius data, load moment, hydraulic pressure, temperature, ground pressure, tipping angle, tension, hook load, centre of mass position, swinging load position, swinging load orientation, crane component position, crane component orientation, aero dynamic / fluid-dynamic lift and drag forces, wind pressure, finite element model parameters, bending displacements, bending angles, vibration data, spectral response data, resonance data, position sensor data, angle sensor data, crane configuration data, number of reeves employed or pulley configuration, suspended load weight data, suspended load shape data, suspended load cross- sectional area, equivalent area, suspended load aerodynamic drag coefficient, crane structure data, jib configuration data, telescopic jib data, telescopic extension data, slew angle, counterweight weights, counterweight positions; as well as statistics thereof (including but not limited to arithmetic mean, standard deviation, 10-minute temporal average, 3-second temporal average) and statistical distributions thereof.

It is noted that data such as crane data or wind data may be arrived at either through calculation or by measurement sensor.

Any data may be referred to as a parameter.An item of crane data may be referred to as a crane parameter.An item of LIDAR data may be referred to as a LIDAR parameter.An item of wind data may be referred to as a wind parameter.An item of load data may be referred to as a load parameter.An item of environmental data may be referred to as an environmental parameter.An item of machine learning data may be referred to as a machine learning parameter.An item of neural network data may be referred to as a neural network parameter.

Claims

Claims:

1. A device comprising one or more crane provided with one or more data set.

2. the preceding claim 1 where the data set constitutes or includes LIDAR data.

3. The preceding claim 1 where the device is further equipped with a means to undertake machine learning.

4. Any preceding claim where the device is equipped with a means to undertake machine learning where such machine learning is used to provide a proposed set point for one or more parameter of the device.

5. Any preceding claim where the device is equipped with a means to undertake machine learning and one or more machine learning performance rate is further provided.

6. Any preceding claim where the device is further equipped with a means to undertake machine learning, where machine learning is used to provide a proposed set point for a parameter of the device, where a machine learning performance threshold is provided, and where the parameter set point is only updated when the machine learning performance rate is found to be above the performance threshold, or alternatively when the machine learning performance rate is found to be below the performance threshold.

7. Any preceding claim where machine learning is used to provide an update adjustment for a parameter of the device and where that parameter is a control parameter of the device, including where the control parameter may be a numeric value, a logical value, an electrical signal, an electronic signal, a voltage, a current, a hydraulic pressure setting, a length setting, an optical signal, a warning, an alarm, or a graphical display.

8. Any preceding claim where at least three LIDARs are arranged such that their beams intersect within a measurement locale

9. Any preceding claim where at least one LIDAR includes a means of scanning or switching the beam angle.

10.Any preceding claim where LIDAR data is collected from measurements at more than one locale in space.

11.Any preceding claim where the LIDAR employs scattering from hard solid targets (such as nearby buildings, other cranes or plant working nearby, suspended loads, local terrain, or temporary structures - for purpose of mapping its surroundings), or alternatively where the LIDAR employs scattering from molecules, aerosols or other microscopic particles borne in a fluid (such as when Doppler LIDAR is employed to measure wind speed within a fluid which is air).

12.Any preceding claim where the device provides a control set point, or an adjustment, or a control limit, for any one or more of counterweight position, suspended load height, jib angle, telescopic jib extension, slew angle, hydraulic pressure, winch rotational speed, trolley travel range.

13.Any preceding claim where the device includes a control system which initiates a safety action or sequence of actions or takes the crane from an in- service state to an out-of-service state or initiates oscillation damping, optionally including where the control system provides an actuator signal for constraining to a range one or more hook motion or suspended load motion (including rotational motion around any chosen axis, and also including translational motion, including hook height motion).

14.Any preceding claim where the device includes a control system which dynamically adjusts control thresholds based on the LIDAR data.

15.Any preceding claim where the device includes a control system which calculates a LIDAR measurement uncertainty which is taken into account when calculating control thresholds.

16.Any preceding claim where the device includes a control system which employs a first LIDAR measurement at a first estimated position in order to estimate a second LIDAR measurement at a second estimated position after a particular duration of time has passed.

17. The previous claim 15 where an uncertainty estimate is also calculated so as to indicate an uncertainty with which is provided the estimated LIDAR measurement value at the second estimated position.

18. Either of the previous two claims 15 or 16 where the first LIDAR measurement is a look ahead wind measurement which is used as an estimate of the expected wind conditions impinging on the crane and any suspended load at an estimated position and time in the future.

19. The previous claim 17 where a plurality of LIDAR wind measurements are used in order to estimate one or more wind field characteristic at an estimated position and time in the future.

20.Any preceding claim where the device employs a machine learning component whereby LIDAR data is fed as input data into the machine learning model.

21.Any preceding claim where the device employs machine learning with training data based on LIDAR data inputs and crane sensor outputs.

22.Any preceding claim where the device employs machine learning where one or more item of LIDAR data, wind data, crane data, crane sensor data, meteorological data, crane configuration data, crane specification data, suspended load specification data, regulatory data, standards data, environmental data or site characterisation data (which may or may not include LIDAR data) is provided to the machine learning model either as input data or as output data during training.

23.Any preceding claim where LIDAR data is collected in order to characterise local site conditions, where some or all of the LIDAR data is collected prior to deployment of the crane.

24.Any preceding claim where the LIDAR data is used in order to inform choice of crane selection or crane configuration or crane operational limits.

25.Any preceding claim where the LIDAR data is provided as a data layer into a Building Information Model (BIM) or a data visualisation tool.

26.Any preceding claim where the device is provided with a static or dynamic model of calculated or measured quantities including any one or more of: loads, hydraulic pressure, temperature, ground pressure, cable tension, hook load, centre of mass position, suspended load position, suspended load orientation, crane component position, crane component orientation, aero-dynamic / fluid- dynamic lift and drag forces, wind pressure, finite element model parameters, bending displacements, bending angles, vibration levels, spectral response, resonances, load sensor data , vibration data, position sensor data, angle sensor data, crane configuration data, number of reeves employed or pulley configuration, suspended load weight data, suspended load shape data, suspended load aerodynamic drag coefficient, crane structure data, jib configuration data, telescopic jib data, telescopic extension data, slew angle, counterweight weights, counterweight positions, or any statistics derived thereof (for instance a standard deviation derived thereof, or an arithmetic mean derived thereof) .

27. Any preceding claim where the device is provided with a Look Up Table where one dimension of the Look Up Table employs either the LIDAR data or else a quantity produced by processing the LIDAR data (for instance wind shear calculated from LIDAR, or wind veer calculated from LIDAR) , or any statistics derived thereof (for instance a standard deviation derived thereof, or an arithmetic mean derived thereof) .

28. Any preceding claim where one or more LIDAR employs timing gates or range gates in order to make a measurement at one or more range .

29. Any preceding claim where one or more LIDAR is mounted on the crane structure itself, optionally incorporating motorised mounting stages .

30. Any preceding claim where one or more LIDAR is remote from the crane structure itself, such as on a ground mounted tripod or on a nearby rooftop .

31. A method for operating the device of claim 1, the method comprising : providing a crane with one or more LIDAR.

32. A method for operating a device in accordance with any preceding claim .

33. A computer system or computer program or instruction set for providing a device in accordance with any preceding claim .