Crane and method for the automated positioning and / or moving of the load-receiving means of a crane of this type

Abstract

The present invention relates to a crane and to a method for the automated positioning and / or automated moving of the load-receiving means of a crane of this type, comprising the following steps in a calibration phase: - collecting training data that characterizes positioning errors of the load hook in various regions of the working region of the crane and for various lifting loads on the load-receiving means, by means of the following steps: o moving to various intended positions and / or traveling various intended paths of the load-receiving means by controlling the drives of the crane by means of a crane controller, o sensing the actual positions actually moved to and / or the traveled actual path of the load-receiving means by means of a position-sensing device in absolute coordinates, - determining a crane model which describes the positioning errors of the crane over its working region for various lifting loads, by regression of the collected training data, and the following steps in a compensation and / or working operation phase: - sensing a lifting load received and / or to be received on the load-receiving means, - correcting the intended target position and / or the intended path of a load lift on the basis of the determined crane model according to the sensed lifting load, and - controlling the drives of the crane by means of the crane controller on the basis of the corrected intended target position and / or the corrected intended target path.

Description

[0001]000822-24 T / wb Liebherr-Werk Biberach GmbH ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ The present invention relates to a crane, in particular in the form of a tower crane, with a boom which can be pivoted about an upright axis, and with a hoist rope which runs from the boom, carries a load-handling device such as a load hook and can be lowered and raised by a hoist. The invention further relates to a method for the automated positioning and / or movement of the load-handling device of a crane, in which the load-handling device is moved from a starting position to a target position and / or is moved automatically along a path.The invention particularly also relates to a method and a system for correcting positioning errors and / or deviations in the path of the load-handling device in the absolute coordinates of a construction site. For the automated construction of buildings, it is important to be able to carry out load hoists by a crane automatically, whereby the load attached to the load-handling device must be set down at the desired target position and / or moved along a desired lifting path with sufficiently high precision. The load-handling device must also be able to pick up the load at the desired starting position with sufficiently high precision. It is important that the transported loads can be precisely positioned or moved in the absolute coordinates of the construction site, since the planning of a construction site is carried out in a defined, georeferenced construction site coordinate system.On the other hand, a crane usually works in its own crane coordinate system. In order to move the load hook or lifting device to a target position or to travel along a specific lifting path or to calculate this in advance, the crane control system specifies target coordinates or lifting path coordinates as drive coordinates in the crane coordinate system to the crane drives, so that the crane drives are then actuated accordingly and the load hook moves to the lifting path points or the target position. For a tower crane, these drive coordinates typically include the angle of rotation around the upright tower axis, the trolley position along the boom and the rope length. For a mobile crane with a luffing telescopic boom, they also include the angle of rotation around the upright axis, the luffing angle of the boom and the rope length, as well as the telescope length if applicable. By measuring the crane position on the construction site, the crane coordinate system can in principle be synchronized with the georeferenced construction site coordinate system orthe absolute coordinate system and, for example, a target position of a load in the georeferenced construction site coordinates is transformed into crane coordinates. In practice, however, significant positioning errors or deviations from the desired lifting path often occur due to various circumstances. Firstly, positioning errors of the load hook or lifting device are caused by deformations of the crane structure. Cranes used on construction sites, such as tower cranes or mobile cranes with telescopic booms, are designed with ambitious lifting capacities and have long, slender structures that deform considerably due to the comparatively high lifting capacities and also due to the not insignificant dynamic loads. In the case of a tower crane, deformations of the tower, which can move forwards or backwards, occur in particular.in a radial direction, i.e. in an upright longitudinal center plane, when a load is taken up on the boom, which leads to a kind of pitching movement of the boom and influences the lowering depth or lifting height of the load hook. The boom itself also bends under the load or the lifting forces introduced by the hoist rope. In addition, there is transverse bending perpendicular to the upright longitudinal center plane of the crane, which runs through the longitudinal axis of the tower and the longitudinal axis of the boom, due to dynamic loads that occur, for example, when the slewing gear is braking or accelerating, or due to wind loads. The dynamic loads can have a multi-axial effect if the load sways or is accelerated by operating the hoist rope. On the other hand, positioning errors also occur system-related due to the control of the hoist gear.The crane operator typically calibrates the lowering depth to "zero" by pressing a button when the load hook is directly below the boom, or 1 m or 2 m below the boom, and also the ground level when the load hook is fully lowered, for example -60 m. Between these two reference points, the crane control system often calculates the lowering depths using linear interpolation and controls the hoist drive accordingly. In practice, however, this is not exactly accurate and can result in significant errors, as one revolution of the cable winch drum does not produce the same pay-out or retraction length across the entire lowering depth range. This is caused by multiple layers of winding of the hoist rope on the drum, which changes the actual winding radius. Path errors due to errors in sensor evaluation or sensor deviations can also occur when the crane hook position is taken into account in a fixed georeferenced construction site system.These systematic deviations result in positioning errors in the absolute construction site coordinate system, which pose a problem for automated crane lifts. Without correction, positioning errors of significant magnitude can occur, for example, at a lowering depth of 1 m or even 2 m, which can have dramatic consequences when setting down a load. Modern cranes are equipped with sway control systems that use comprehensive sensor technology and sensor evaluation to monitor crane movements and intervene to correct the control of the crane drives, in particular the control of the slewing gear, which rotates the boom around its upright axis, the trolley drive, which can move a trolley along the boom, and the hoist, which can raise and lower the load hook.It has also been proposed to consider structural deformations of the crane for sway control, particularly the deflection of the tower and boom (see, for example, EP 3784616 A1). However, the positioning errors mentioned in the georeferenced construction site system, as described above, cannot be eliminated. These systematic positioning errors in the absolute coordinate system have so far been counteracted by monitoring the actual, absolute crane hook position in georeferenced construction site coordinates. For this purpose, special position sensors are used, for example, a GNSS sensor module on the crane hook or a tachymeter or even local sensor tags operating in the ultra-wideband range, which can include one or more tags at calibrated positions on the construction site as well as one or more tags on the crane.However, such absolute position detection systems are very expensive and can be prone to error under certain operating conditions, such as adverse weather or environmental conditions or interfering objects such as steel structures or transmitters in the immediate vicinity. This often requires the installation of peripheral devices and cables, which, depending on the construction site, can be difficult or even impossible to implement. The present invention is therefore based on the object of creating an improved crane and an improved method of the type mentioned above that avoid the disadvantages of the prior art and advantageously develop the latter. In particular, automated lifts should be enabled with no or only negligible positioning errors or deviations from the lift path, without the need for permanent monitoring by expensive absolute sensor systems.According to the invention, the stated object is achieved by a method according to claim 1, a crane according to claim 12, a correction method according to claim 13 and a correction system according to claim 14. Preferred embodiments of the invention are the subject of the dependent claims. It is therefore proposed that the crane be operated in a calibration phase before regular crane operation with automated lifts, in which the positions actually approached or traveled to during the movement of the load handling device are monitored using a sensor system operating in georeferenced absolute coordinates and compared with the positions recorded and / or predetermined in the crane coordinate system in order to determine any positioning errors that occur in the georeferenced absolute coordinates. A model is determined from the training data recorded in the calibration phase, with the help of which positioning errors can also be estimated or calculated for positions and / or lift paths.can be determined for which no position data from the sensors working in absolute coordinates are available, so that the crane can work in a later regular crane operation phase with automated lifts without monitoring by the absolute sensors and a correction of the positioning errors can be made on the basis of the positioning errors estimated using the model.The following steps can be used in the calibration phase and in the regular operation phase: In the calibration phase, training data is collected that characterizes the positioning errors of the load hook in various sections of the crane's working range and for various lifting loads on the load handling device. To collect the training data, various target positions are approached and / or various target paths are followed with the load handling device by controlling the crane's drives via the crane control system, and the actual positions actually approached and / or the actual paths actually followed are recorded using a position recording device operating in absolute coordinates. Based on the collected training data, a crane model is then determined by regression of the aforementioned training data. This model describes the positioning errors of the crane across its working range for various loads.During regular operation with automated load lifts, the lifting load picked up or to be picked up by the load handling device is recorded by sensors in order to correct the target position and / or the target path of the load handling device for the automated load lift depending on the recorded lifting load and based on the specific crane model. The crane control system then controls the crane drives based on the thus corrected target position and / or the target lift path. The crane model determined by regression of the training data makes it possible to estimate the positioning error occurring in the georeferenced construction site coordinates across the entire working area of ​​the crane and thus also to determine it for positions or sections of the working area where no measurements were previously taken with the absolute sensors during the calibration phase.Regardless of whether a position approached during the calibration phase for training purposes or a section of the work area traversed for training purposes is to be approached or traveled away from, the crane can operate in regular crane operation with automated lifts without the monitoring of a position detection sensor operating in absolute coordinates and still determine the systemic positioning error or the positioning error caused by deflections in absolute coordinates and correct the target position to be approached or the lifting distance to be traveled based on the estimated or determined positioning error. The present invention thus achieves the significant advantage that, after a calibration phase, the positioning accuracy of the crane in georeferenced coordinates can be improved without additional and usually very expensive measuring systems. This eliminates the need for permanent and often cumbersome installation of such measuring systems.The calibration and compensation phases mentioned can generally be applied to any crane type, but are particularly advantageous for cranes with long, slender, and ambitiously designed structures that exhibit significant deformation under load and can operate with long hoist rope lengths. Furthermore, the system itself can achieve increased safety and further advance the automation of building construction planned within a fixed construction site coordinate system. In a further development of the invention, in the calibration phase, to collect training data, the lifting loads picked up by the load-handling device and the target positions approached or the lifting paths followed are varied over large ranges within the load-bearing capacity and working range of the crane in order to obtain the most meaningful training data possible.For example, different lifting loads can be recorded and the crane's radius can be changed, for example by moving the trolley drive, in order to represent at least 75% of the load capacity range as training data. Alternatively or additionally, lifting paths can be traversed and / or target positions can be approached that represent at least 75% of the radial radius. Alternatively or additionally, lifting paths can be traversed and / or target positions can be approached that represent at least 75% of the lifting height, i.e., loads are lifted, for example, from ground level to near the boom or lowered vice versa. Large areas can also be traversed in the horizontal plane, with training runs preferably being able to cover at least the edge areas of the crane's working range.In a further development of the invention, during the calibration phase, the position signal of the position sensor operating in absolute coordinates and, on the other hand, sensor signals and / or evaluated sensor signals and / or control signals are recorded in real time, which are stored as training data in crane coordinates by the crane control system and / or sensors installed on the crane. In principle, various crane data can be collected as training data, in particular position data that determine the respective position of the crane drives and / or the crane components driven by them, for example, the angle of rotation of the boom around the upright axis, the trolley position along the boom, the unwinding length of the hoist rope and / or the rotational position of the hoist rope drum.Depending on the sensors installed on the crane, other crane operating parameters can also be collected as training data, for example, strains in the crane structure such as strains in the tower's longitudinal chords, bending deformations of the tower and / or the boom in one or more axes, and in particular, travel speeds of the aforementioned crane drives, i.e., rotational speeds around the upright axis and / or trolley travel speeds and / or hoist rope speeds. Alternatively or additionally, accelerations can also be recorded by sensors and / or derived from other sensor-recorded variables and collected as training data, for example, hoist rope accelerations, load hook accelerations, trolley accelerations and / or rotational accelerations around the upright axis. The sensors used in the calibration phase, which operate in absolute coordinates, can fundamentally be designed in different ways.In an advantageous development of the invention, at least one GNSS sensor can be used, for example, attached to the load hook or the load handling device. Alternatively or additionally, such GNSS sensors can also be attached to other crane components such as the trolley and / or boom tip. Alternatively or in addition to such GNSS sensors, a tachymeter system can also be used to record the position of the load handling device of the crane and possibly also other crane components such as the trolley in absolute coordinates, wherein optical and / or electronic tachymeters can be used here. Preferably, tachymeters with automatic target detection can be used. Alternatively or additionally, the absolute sensor system can also work with optical detection systems such as cameras, which record the position of the load hook and possiblyalso other relevant crane components such as the trolley in absolute coordinates. Alternatively or additionally, the absolute sensor system can also have local, wireless signal transceiver devices that can, for example, record signal propagation times and / or signal strengths. For example, ultra-wideband tags can be provided, which are mounted on the one hand at a location measured in the georeferenced coordinate system or at several georeferenced positions and on the other hand are attached to one or more positions on the crane, for example on the load hook and at one or more points on the boom, in order to determine the absolute position of the load-handling device from the signals exchanged between the tags, their propagation times and / or their received signal strengths. When regressing the training data, a linear regression can be carried out in a further development of the invention, preferably with non-linear basis functions.Preferably, trolley positions can be taken into account in the regression to the second power and / or third power in order to achieve better accuracy, in particular to better represent deformations of the boom. Squared and cubic trolley positions allow a more precise prediction of bending of the boom in the vertical direction, since the bending curve of the boom can be described by a third-order polynomial, at least if one assumes that there is no load distribution or that all distributed loads across the boom are neglected. In particular, a so-called Lasso algorithm, i.e. "least absolute shrinkage and selection operator", can be used in the regression to select the most relevant parameters or properties of the training data. In the compensation phase, the positioning error can be corrected in various ways.For example, path points defining a lifting path can be corrected by shifting the positions or coordinates defining the lifting path point by point. Alternatively or additionally, a correction of the trajectory characterizing the desired path of the load hook can also be made during the positioning correction. The invention is explained in more detail below using a preferred exemplary embodiment and associated drawings. The drawings show: Fig. 1: a side view of a crane according to an advantageous embodiment of the invention in the form of a tower crane, Fig. 2: a schematic, perspective view of the crane from Fig. 1 to illustrate the degrees of freedom and the crane coordinate system, Fig. 3: a representation of a lifting path determined by a lifting path planner along several path points, Fig.4: a schematic side view of the crane from Figures 1 and 2 to illustrate the positioning errors that occur, Fig. 5: a perspective view of a lifting path around various objects, with working boundaries shown by a black frame, Fig. 6: a view of a lifting path that is traveled during the calibration phase with a GNSS sensor attached to the load hook, Fig. 7: a view of the positioning error in polar coordinates in measured absolute coordinates compared to the positioning error estimated by the crane model, Fig. 8: a view of the load hook positions in the fixed crane coordinate system, with the correction paths shown in dashed lines next to the target path and the target path measured in absolute coordinates, and Fig. 9: a view of the positioning error in polar coordinates corresponding to the views in Fig. 8.As Figure 1 shows, the crane 1 can be designed as a tower crane, the tower 2 of which carries a boom 3 that can be rotated about an upright axis 4, in particular the longitudinal axis of the tower, by a slewing gear. A trolley 5 can be moved along the boom 3 by a trolley drive cable 6 in order to change the outreach of the load hook 7. The trolley travel cable 6 can be adjusted by a corresponding trolley travel drive. The load hook 7 can be lowered or raised from the boom 3 by a hoist cable 9 in order to be lowered and raised along the upright axis 10, see Figure 1. The planning level of the construction site usually works with a locally fixed, georeferenced coordinate system, which requires absolute positioning of the transported payload from automated tower cranes.However, the error, which includes bending displacements, sensor offset, and sensor evaluation errors, cannot yet be compensated for, even by modern control systems that only work with relative sensors on the tower crane. The corresponding path error causes the payload to deviate from the desired path, which can lead to collisions with obstacles. The degrees of freedom and the crane coordinate system K are highlighted in Figure 2. The tower crane 1 transports payloads with mass ^^^^. ^^^^ ^^^^ by attaching it to the crane hook 7. The tower crane 1 can be equipped with at least three drive systems. The slewing drive operates a joint below the tower crane cabin or, in the case of a bottom-slewing crane, slews the entire tower 2 including boom 3, which determines the slewing angle γ of the boom. The trolley drive controls the trolley position ^^^^ ^^^^ ^^^^ and the lifting drive changes the rope length ^^^^ ^^^^. Moving the drives can cause the payload to swing with the swivel angles ^^^^ ^^^^ in vertical direction and ^^^^ ^^^^ in the radial direction of the boom. The origin of the crane coordinate system ^^^^ is located above the cabin as shown in Figure 2. For the tower crane 1, a known sway-damping controller and a trajectory tracking controller can be provided to carry out automated load hoists. In this case, the elastic degrees of freedom that correspond to the bending of the tower 2 in the radial direction q vx ∈ R ^^^^ and in tangential direction ^^^^ vy as well as that of boom 3 in tangential direction ^^^^ wy and in vertical direction ^^^^ wz In the following, the vector containing all degrees of freedom of the tower crane is considered as follows where the state vector of the tower crane is defined as ^^^^ =[ ^^^^, ^^^^ ]T , while the corresponding target state trajectory is denoted as follows: ^^^^ ^^^^ . Sensors of the drive systems measure the positions ^^^^ d = [γ, ^^^^ tr , ^^^^ r ] T and the speeds ^^^^ T ḋ r =�γ̇, ^^^^ t ̇ r , ^^^^ ṙ �. The target positions and speeds of the drive systems are determined by ^^^^^^^^ ^^^^, ^^^^and ^^^^ dr ̇ ,d, the control inputs to the drive systems are indicated with ^^^^. The fully automatic payload transport or automated payload lifting is prepared by planning a collision-free crane hook path to a specified destination, which further corresponds to the lifting path, and calculating the corresponding trajectory in the coordinate system ^^^^. A known algorithm can be used for path planning. The path planning algorithm preferably calculates a sequence of ^^^^ p so-called track points ^^^^ ^^^^ , which represent a collision-free path from a starting point to a destination with respect to the common georeferenced ground coordinate system G and can be optimized according to certain criteria. The lifting path can be calculated using a known algorithm between each consecutive point pair ^^^^ ^^^^ and ^^^^ ^^^^+1be calculated, where the hook position can correspond to the flat output ^^^^ of the system. From the desired lifting trajectory and its derivatives ^^^^ ^ ∗ ^ ^^ = the derivation of the target state trajectory ^^^^ ^^^^ = ψ ^^^^ ( ^^^^ ^^^^ , … , ^^^^ ^^^^ ) and the feedforward control input trajectory ^^^^ ^^^^ ^^^^ = ψ ^^^^ ( ^^^^ ^ ∗ ^ ^^ ) using the flat parameterizations ψ ^^^^ ,ψ ^^^^ To ensure continuous movement of the hook, a sphere with a certain radius around the sphere center ^^^^ ^^^^ is defined for all ^^^^ = 1, … , ^^^^p. As soon as the crane hook cuts through the sphere while following the target trajectory ^^^^ ^^^^ tracked, the crane determines an updated lifting path at ^^^^ ^^^^+1. An example hoist path is shown in Figure 3. Overall, the crane operates in its own crane-centered coordinate system ^^^^, but all other operations on the construction site are planned by the planning level with respect to the common georeferenced ground coordinate system ^^^^. In a previous step, the transformation between the ground coordinate system ^^^^ and the crane system ^^^^ is therefore determined by measurements, preferably for the case where no load is hanging on the hook, the hoist rope is as short as possible, and the trolley is close to the tower under the boom. Here, ^^^^ can be the local UTM projection. Within ^^^^, the actual hook position can by the forward kinematics ^^^^ ^^^^( ^^^^, ^^^^ d) based on the nominal rigid body model and an error that can arise from the bending ^^^^^^^^ ^^^^ ^^^^ ^^^^ of the tower 2 and the boom 3 of the tower crane 1 as well as the elongation of the hoist rope 5, sensor deviations ^^^^^^^^ ^^^^ ^^^^ and sensor evaluation errors ^^^^^^^^ ^^^^ ^^^^. These error sources are shown in Figure 4. The path error is then defined as The path error ^^^^ also includes the control error ^^^^ ^^^^� ^^^^ ^^^^ , ^^^^^^^^ ^^^^, ^^^^� − ^^^^ ^^^^( ^^^^, ^^^^) of the trajectory follower controller. The sensor deviations cause the drive systems to stop at constantly shifted positions. Furthermore, the sensor evaluation errors cannot be neglected, because validation measurements have shown that the oscillation angle in the radial direction is incorrectly evaluated by up to 3°, which leads to a trajectory error, especially during transitions. To compensate for the aforementioned trajectory error ^^^^, a first step can be to predict this trajectory error. The prediction of the error ^^^^ is carried out using a regression model. The three components Δγ ^^^^ ,Δ ^^^^^^^^ ^^^^, ^^^^,Δ ^^^^ ^^^^ � ^^^^ representing ^^^^ in polar coordinates are considered as the results of the regression model. The error of the rotation angle Δγ ^^^^ is calculated with respect to the origin of the coordinate system attached to the tower crane. Based on a linear regression, the output ^^^^ of the regression model is therefore defined as follows where for ∈ ( γ, ^^^^ ^^^^ ^^^^ , ^^^^ ) . The constants describe the number of features used to predict each component. The coefficients Θ ξare determined by a regression fitting algorithm applied to the training data. The training data preferably includes measurements of features and the respective errors Δγ ^^^^ , Δ ^^^^^^^^ ^^^^, ^^^^and Δ ^^^^ ^^^^ . Possible features are the target state trajectory ^^^^ ^^^^ , the state vector ^^^^ , which is calculated by the sensor evaluation, the positions ^^^^ ^^^^ ^^^^ as well as the speeds ^^^^ ^^^ ̇ ^ ^^^^ the drive systems of the tower crane, the payload measured by the load sensor ^^^^ ^^^^ ^^^^ and the control commands calculated by the implemented controller. Further features include the quadratic and cubic trolley position ^^^^ ^ 2 ^ ^^ ^^^^ and ^^^^ ^ 3 ^ ^^ ^^^^ as well as the approximate payload fluctuation distances ^^^^ ^^^^ ϕ ^^^^ and ^^^^ ^^^^ ϕ ^^^^. The quadratic and cubic trolley position can enable a more accurate prediction of the boom deflection in the vertical direction, since the boom deflection curve is a third-order polynomial in ^^^^ ^^^^ ^^^^ assuming that there is no distributed load. All features considered are summarized as follows with 48 entries. To identify the most relevant features for each output and avoid overfitting, a so-called LASSO regularization is performed for each output Ψ ∈�Δγ ^^^^ ,Δ ^^^^^^^^ ^^^^, ^^^^,Δ ^^^^ ^^^^ � calculated using training data. More precisely, the optimization problem ^^^^ ^^^^ ^^^^�ω ξ 1 ^^^^ ∑ ^ ^ ^ ^ ^ ^ ^ ^ =1�Ψ ^^^^ − [ 1, ^^^^ ^^^^ ] for different values ​​of λ using Matlab-Simulink. The index ^^^^ represents the time step of the training data. The coefficients ω ξ ∈ ^^^^ ^^^^ ^^^^ and its components ω ξ, ^^^^ denote the unknown coefficient vector when all 48 features and a constant offset are considered. The higher the value for λ , the more coefficients are zero, while the remaining coefficients sufficiently minimize the squared error between the measured output and the outputs of the regression model, thus specifying the features best suited for reconstructing the respective output. After examining the non-zero coefficients of the solution for several values ​​of λ, the following features are obtained for predicting the errors by regression. The features (10a) for the prediction Δγ ^^^^ do not include the payload ^^^^ ^^^^ ^^^^. The influence of the bending of the boom in the tangential direction ^^^^ ^^^^ ^^^^ exceeds the influence of ^^^^ ^^^^ ^^^^ . The pendulum angle ϕ ^^^^ and the target angle ϕ^^^^, ^^^^are both relevant characteristics, because their difference directly determines Δγ ^^^^ which is also an angle. The features (10b) for predicting Δ ^^^^^^^^ ^^^^, ^^^^do not contain the product ϕ ^^^^ ^^^^ r and contain the pendulum angle in the radial direction ϕ ^^^^ and the crane rope length ^^^^ ^^^^ instead. The radial bending of the tower ^^^^ ^^^^ ^^^^ is more important than the vertical bend ^^^^ ^^^^ ^^^^ of the boom. With respect to Δ ^^^^ ^^^^ the most important features in (10c) are the load ^^^^ ^^^^ ^^^^ and the cubic cat position which represents the deflection curve as a function of the chassis position. In a further step of deriving the regression model, the coefficients Θ ξusing the iteratively reweighted least squares fitting algorithm. The result of the regression model is thus formulated as follows: To compensate for path or positioning errors, several approaches to calculating a corrective stroke path can be used. and the corresponding lifting trajectory torie ^ � ^^^ ^ ∗ ^^^ be applied, whereby the target state trajectory and forward control ^^^�^ ^^^^ ^^^^ of the tower crane can be converted into georeferenced coordinates for more precise tracking of the planned trajectory. The corrected trajectories are denoted by ^�^^^ ^^^^ , and ^^^�^ ^^^^ ^^^^ The correction and the measurement results of a load stroke using two different approaches are shown in Figure 4. The corrected hook position is highlighted in red and blue dashed lines and results from shifting the target hook position ^^^^ ^^^^ by the predicted path error ^^^^ as a result of (11). The tower crane control aims to position the hook at the compensation position ^�^^^ ^^^^ Due to the remaining path error, the hook position will be equal to the desired hook position ^^^^ ^^^^ (black in Figure 4), which is the overall goal. The path calculated with the path planning algorithm [8] consists only of a sequence of points in the tower crane's working space. Therefore, the only known features of (10) are the positions of the drive systems ^^^^ ^^^^ ^^^^ and the permanently measured load ^^^^ ^^^^ ^^^^at these points. Therefore, in a first approach (blue in Figure 4), the regression model is evaluated at the position of each path point under the assumption that the hook is in a stationary state. The remaining features required for prediction by the regression model are unknown. Suppose the path calculated by the path planning algorithm used is represented as a set ^^^^ … , ^^^^ ^^^^ ^^^^ � where the ^^^^ Points that describe the trajectory points in polar coordinates in ^^^^, the corrected trajectory points are obtained by ^�^^^ ^^^^ = ^^^^ ^^^^ + ^^^^�0, ^^^^ ^^^^ , 0, ^^^^ ^^^^ ^^^^ , 0,0, ^^^^ ^^^^ , 0, ^^^^ ^ 3 ^ ^^ ^^^^ �. (12) In it, the drive systems ^^^^ ^^^^ ^^^^ set to Neglecting all other features reduces the quality of the predicted error ^^^^ in (12) and the regression model therefore only describes static deflections and sensor displacements. The corrected stroke path and the corresponding �∗ Lift trajectory ^^^^ ^^^^,1are obtained by applying the previously described algorithm for generating trajectories to the set of adjustment trajectory points used. The flat parameterizations ψ ^^^^ ,ψ ^^^^ then result in the target state trajectory and the forward control input , which are fed to the trajectory follower controller. Alternatively or additionally, the corrected lift path can be used as a second approach ^^^�^ ^^^^,2 and the lifting trajectory ^^^ � ^ ^ ∗ ∗ ^^^,2 based on the desired lifting path of the hook ^^^^ ^^^^ be calculated. The state trajectory ^^^^ ^^^^This includes the expected pendulum angles, elastic degrees of freedom, and travel speeds. Assuming that the stroke trajectory is perfectly tracked, ^^^^ = ^^^^ ^^^^ and ^^^^ = ^^^^ ^^^^ ^^^^ . Then the sensor values ​​of the positions of the drive systems ^^^^ ^^^^ ^^^^ = ^^^^^^^^ ^^^^, ^^^^and the speeds ^^^^ ^^^ ̇ ^ ^^^^ = ^^^^ ^^^^ ^^ ̇ ^^, ^^^^ also equal to the values ​​of the corresponding target values. The corrected path points can then be calculated according to However, it should be noted that only the position of the hook is corrected in polar coordinates. The derivatives ^^^^ ^^̇^^ , ^^^^ ^^̈^^ , do not agree with the compensated position ^�^^^ ^^^^. Therefore, the derivatives ^̃^^^ ^^^^ ̇ ,2 , ^̃^^^ ^^^^ ̈,2 , of the corrected stroke path ^^^�^ ^^^^,2 is calculated by reusing the existing trajectory generation algorithm for a new set of trajectory points defined as follows where the time steps are evenly distributed over the time span that describes the transition between the starting and destination locations. The time steps are thus determined by ^^^^ = ^^^^Δ ^^^^ with Δmax� ^^^^γ, ^^^^ ^^^^ ^^^^, ^^^^ ^^^^�^ ^^^ ^^^^ = ^^^^ ^^^^ , where ^^^^ γ , ^^^^ ^^^^ ^^^^ , ^^^^ ^^^^ the transition time in each coordinate of ^^^�^ ^^^^,2. The application of the trajectory algorithm to delivers ^^^ � ^ ^ ∗ ^ ^^,2 and thus the target state trajectory ^^^�^ ^^^^,2as well as the feedforward control input ^^�^^^^^^ ^^^^,2 after using the flat parameterizations. To validate the regression model (11) using experimental data and to investigate the performance of the presented error compensation approaches, a real Liebherr 154 EC-H Litronic 6 large tower crane, as shown in Figure 1, was used to move a payload weighing 1000 kg from a start point to a destination point within the working range of the tower crane 1. The start and destination points are given by ^ ^^^1 = [ 202°, ​​24.5 ^^^^, 16 ^^^^ ] ^^^^ , ^^^^105 = [ 29°, 34 ^^^^, 17 ^^^^ ] ^^^^ . (15)The path planning algorithm calculates path points between the start and destination locations, taking into account that the payload must not be moved through restricted areas within the tower crane's working area, and minimizes the total path length. The resulting path points consist of 105 points, which are shown in Figure 5. Note that between the last two points ^^^^ 104 and ^^^^ 105 There is a vertical transition of 1 m. The desired hook path ^^^^ ^^^^is calculated using the trajectory algorithm. The tower crane's trajectory follower is used for trajectory tracking and dampens payload sway. The training data can be obtained by performing several benchmark payload transitions, preferably at the edge of the tower crane's working range. The features (10) and the trajectory tracking error (4) are recorded while a GNSS sensor is attached to the hook 7 of the tower crane 1. The georeferenced position of the GNSS sensor is determined using real-time kinematics that achieves an accuracy of up to 2 cm. The benchmark transitions consist of a slewing transition, a radial transition, a combination of a slewing and a radial transition, and a combination of a slewing, a radial, and a vertical transition. The target hook position of all benchmark transitions is highlighted in red in Figure 6.Considering the training data as a whole, points 1 to 6 are only performed for two different rope lengths and transported payloads of 0 t and 2.25 t. The latter is the maximum payload that can be lifted with the test crane at a trolley position of 35 m. Figure 7 shows the error predicted by the regression model (11) and the error ^^^^ reconstructed from the measured data. While ^^^^ ∈. [ 70,215 ] the boom bends horizontally against the swivel direction, so that Δγ ^^^^increases. The error Δ ^^^^^^^^ ^^^^, ^^^^becomes negative at the beginning of the automated load hoist because the radial target hook position becomes smaller than the actual radial hook position. This is caused by an incorrect determination of the actual hook position by the tower crane's sensor evaluation, since the sensor evaluation of the slewing angle in the radial direction has a smaller value than the actual one. During the slewing movement, Δ ^^^^^^^^ ^^^^, ^^^^remains constant, which means that the position of the trolley 5 is shifted. If the trolley 5 is moved away from tower 2, the same sensor evaluation error results as when the trolley 5 moves towards tower 2. Since the direction of movement is changed, Δ ^^^^^^^^ ^^^^, ^^^^becomes even larger at ^^^^ ≈ 215 ^^^^. The vertical error Δ ^^^^ ^^^^is dominated by the static bending of boom 3 and the elongation of the crane rope. For long-reaching trolley positions, the bending moment acting on boom 3 is greater, which is why the payload hangs lower and the error thus increases. Since the scenario is terminated by lowering the payload by 1 m, the target height decreases. Since the hoist drive is moved with a short delay, the target height is equal to the actual height, causing Δ ^^^^ ^^^^ =0 for a short period of approximately ^^^^ ≈ 303 ^^^^. The mean absolute errors (MAEs) between the regression model result and the measured error are given by ^^^^ ^^^^ ^^^^ ∘γ = 0.24 , ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ =5.4 ^^^^ ^^^^, and ^^^^ ^^^^ ^^^^ ^^^^ = 3 ^^^^ ^^^^. All MAEs are influenced by the measurement accuracy of the GNSS sensors, which is approximately 2 cm in the best case.Since the MAEs have almost the same values ​​as the measurement accuracy, the prediction by the regression model works impressively well. The remaining MAEs result from only including measurements for different transitions and different payloads, or random disturbances such as wind or the bearing play of the rotary drive. Figure 8 shows the desired lift path ^^^^. ^^^^ (black), the corrected stroke paths ^^^�^ ^^^^,1 , ^^^�^ ^^^^,2 (dashed) and the GNSS position of the crane hook (solid), measured while tracking the desired lift path (green) and while tracking the lift path corrected by the first (blue) and second (red) approaches mentioned above. The time grid for determining the compensation trajectory by the second approach ^^^^ ^^^^,2 is calculated with ^^^^ ^^^^= 105. It can be observed that the height of the dashed compensation trajectories increases the further the trolley is from the tower. This is important because the bending deviation of the tower and boom increases the farther the trolley is from the tower. Therefore, the green line slopes downwards, while the solid red and blue lines remain in the same horizontal plane. The bending in the vertical direction is not completely eliminated, as there is an offset between the black and the solid blue and red lines, respectively. This offset is within the range of the MAE determined in the previous subsection and is also justified by the negatively affected measurement accuracy of the GNSS sensors.The results also show that the accuracy of the GNSS sensors used is significantly affected by the crane's metal structure: Although Real Time Kinematics is used, large peaks in the solid green, red, and blue lines occur in the middle and at the end of the slewing process. Figure 9 shows the components of the trajectory error. It should be noted that correcting the individual trajectory points results in a different transition time between the individual trajectory point pairs. Thus, the overall path obtained by approaches 1 and 2 has a different transition time than the original black path. To evaluate the trajectory tracking error, the components Δγ ^^^^ , Δ ^^^^^^^^ ^^^^, ^^^^, and Δ ^^^^ ^^^^ by formulating the vector connecting each GNSS measurement to the nearest point on the target path in polar coordinates. Consequently, the errors Δγ ^^^^and Δ ^^^^^^^^ ^^^^, ^^^^equal to zero while ^^^^ ∈ [83,187] or ^^^^ ∈ [20,66], and ^^^^ ∈ [211,294], which is due to the fact that the components are determined by the nearest point on the desired lifting path that has the same swivel angle or the same chassis position. The errors Δγ ^^^^ and Δ ^^^^^^^^ ^^^^, ^^^^are already small due to the good performance of the tracking controller and are also dominated by unexpected disturbances that lead to only a slight improvement in the results. These disturbances include gusts of wind, a permanently occurring oscillation when moving the payload, and bearing play. During the long rotation in the time interval ^^^^ ∈ [ 70,210 ]The offset in the landing gear position Δ ^^^^^^^^ ^^^^, ^^^^is eliminated by both approaches. The trajectory error resulting from following the compensation trajectory generated with the second approach results in relatively large peaks at the end of the long curve at ^^^^ ≈ 190 ^^^^ and after lowering the payload at ^^^^ ≈ 295 ^^^^. These peaks are caused by the application of the trajectory generation algorithm, which introduces spheres with a radius of 1 m. Due to the spheres, the hook executes the transitions earlier than the desired trajectory. Compensation with both approaches is most worthwhile with regard to the error Δ ^^^^ ^^^^ in the vertical direction. The uncompensated error in the example considered increases with further extending trolley positions and shows an average error of 22 cm in Δ ^^^^ ^^^^while the compensation keeps the error almost at zero during the entire transition and the average trajectory error in Δ ^^^^ ^^^^ to 4.3 cm and 5.1 cm, respectively. Overall, the proposed compensation works well in the vertical direction and improves tracking performance in the horizontal direction, primarily by eliminating offsets. Thus, the advantage of using the corrected travel path is ^^^�^ ^^^^,2 for compensation instead is low. In the example considered, the MAE is reduced from 24 cm to 9 cm and 12 cm, respectively, which corresponds to an average error reduction of 50%. Both compensation approaches are particularly suitable for increasing the positioning accuracy of the tower crane by eliminating offsets and deviations due to bending. The MAE at the target position of the presented experiment is reduced from 44 cm to 10 cm and 5 cm, respectively.

Claims

000822-24 T / sw Liebherr-Werk Biberach GmbH ^ ... Controlling the drives of the crane (1) by a crane control,o Recording the actual positions actually approached and / or the actual path traveled by the load-handling device (7) by means of a position detection device in absolute coordinates, - Determining a crane model that describes the positioning errors of the crane over its working range for different lifting loads by regression of the collected training data, 2 / 6 in a compensation and / or operating phase: - detecting a lifting load picked up and / or to be picked up on the load-handling device (7), - correcting the target position and / or the target path of an automated load lift based on the specific crane model as a function of the detected lifting load, and - controlling the crane drives via the crane control system based on the corrected target position and / or the corrected target path.

2. Method according to the preceding claim, wherein, during the collection of the training data in the calibration phase, the actual positions and / or the actual path are recorded in absolute coordinates in real time, and load-handling device positions are recorded in crane coordinates. 3.Method according to one of the preceding claims, wherein the position detection device operating in absolute coordinates is only used in the calibration phase, and in the operating phase with automated load hoists the crane (1) is operated without said position detection device operating in absolute coordinates.

4. Method according to one of the preceding claims, wherein in the calibration phase operating parameters of the crane drives including setting positions and / or setting speeds and / or target positions are recorded in crane coordinates as training data.

5. Method according to one of the preceding claims, wherein in the calibration phase sensor and / or control command data of a sway damping system of the crane including the positions ( ^^^^) are used as training data. ^^^^ ^^^^ ) and the speeds ( ^^^^ ^^^ ̇ ^ ^^^^ ) of the drive systems of the crane (1), the payload measured by the load sensor , control commands of the crane control, the square and cubic trolley positions ( ^^^^ ^ 2 ^ ^^ ^^^^ ) and ( ^^^^ ^ 3 ^ ^^ ^^^^ ) and the approximate payload fluctuation distances ( ^^^^ ^^^^ ϕ ^^^^ ) and ( ^^^^ ^^^^ ϕ ^^^^ ) are recorded. 3 / 6 6. Method according to one of the preceding claims, wherein, when determining the crane model by regression of the training data, a linear regression with non-linear basis functions is carried out.

7. Method according to one of the preceding claims, wherein, when determining the crane model by regression of the training data, position data ( ^^^^ ^ 2 ^ ^^ ^^^^ ) and ( ^^^^ ^ 3 ^ ^^ ^^^^) of a trolley (5) of the crane (1) are processed to the second power and / or the third power.

8. Method according to one of the preceding claims, wherein during the regression of the training data, the number of independent variables is reduced and a subgroup of variables is selected for the crane model, wherein in particular a Least Absolute Shrinkage and Selection Operator (LASSO) is applied.

9. Method according to one of the preceding claims, wherein the actual positions and / or the actual path of the load handling device (7) are recorded in absolute coordinates by means of at least one GNSS sensor and / or a tachymeter system and / or a local ultra-wideband tag system.

10. Method according to one of the preceding claims, wherein the calibration phase is carried out on the construction site on which the work operation phase is also carried out. 11.Method according to one of claims 1-9, wherein the calibration phase is carried out on a test site and the working operation phase is carried out with automated load lifts on a construction site different from the test site, wherein in the calibration phase on the test site a reference location is determined and the crane model is related thereto and the installation location of the crane (1) in the construction site for the working operation phase is selected as a function of the said reference point and a reference point of the georeferenced construction site coordinate system. 4 / 6 12. Crane, in particular a tower crane, with a boom (3) which can be pivoted about an upright axis (4), and a hoist rope (9) which runs from the boom (3), carries a load-carrying means (7) such as a load hook and can be lowered and raised by a hoist, and an electronic crane control system for controlling the drives of the crane, in particular a slewing gear, a trolley drive and a hoist, characterized in that the crane control system has a calibration module and a compensation module, wherein the calibration module is designed, in a calibration phase: - to collect training data which characterize positioning errors of the load hook (7) in different areas of the working range of the crane and at different lifting loads on the load-carrying means (7),by means of the following steps: o Approaching various target positions and / or following various target paths of the load-handling device (7) by controlling the drives of the crane (1) via a crane control system, o Collecting position data of the actual positions actually approached and / or the actual path followed by the load-handling device (7), provided by a position detection device in absolute coordinates, - Determining a crane model that describes the positioning errors of the crane over its working range for various lifting loads by regression of the collected training data, and the said compensation module is designed, in a working operation phase: - Detecting a lifting load picked up and / or to be picked up on the load-handling device (7), - Correcting the target position and / or the target path of an automated load lift using the determined crane model depending on the detected lifting load, and, 5 / 6 - Control of the crane's drives by the crane control system based on the corrected target position and / or the corrected target path.

13. A method for correcting positioning errors and / or deviations in the path of a load-handling device (7) of a crane in georeferenced coordinates of a construction site, comprising the following steps: - Collecting training data that characterizes positioning errors of the load hook (7) in various areas of the crane's working range and at various lifting loads on the load-handling device (7), by means of the following steps: o Approaching various target positions and / or following various target paths of the load-handling device (7) by controlling the drives of the crane (1) via a crane control system, o Detecting the actual positions actually approached and / or the actual path followed by the load-handling device (7) using a position detection device in absolute coordinates, - Determining a crane model,which describes the positioning errors of the crane across its working range for different lifting loads, including for sections of the working range in which no target positions and / or target paths were approached, by regression of the collected training data.

14. A system for correcting positioning errors and / or deviations in the path of a load-handling device (7) of a crane (1) in georeferenced coordinates of a construction site, comprising a calibration module and a compensation module, wherein the calibration module is designed, in a calibration phase: - to collect training data characterizing positioning errors of the load hook (7) in different areas of the crane's working range and for different lifting loads on the load-handling device (7), by means of the following steps: 6 / 6 o Approaching various target positions and / or following various target paths of the load handling device (7) by controlling the drives of the crane (1) by a crane control system, o Recording the actual positions actually approached and / or the actual path followed by the load handling device (7) by means of a position recording device in absolute coordinates, - Determining a crane model that describes the positioning errors of the crane over its working range for different lifting loads by regression of the collected training data, and the said compensation module is designed to carry out the following in a working operation phase: - Recording a lifting load picked up and / or to be picked up on the load handling device (7), - Correcting the target position and / or the target path of an automated load lift using the determined crane model as a function of the recorded lifting load.

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