Intelligent hoisting system and method based on crawler crane

Abstract

The invention discloses an intelligent hoisting system and method based on a crawler crane and belongs to the field of crawler crane hoisting. The intelligent hoisting system comprises a path planning module, an anti-swing control module, a precise positioning module and an information interaction module. Intelligent hoisting operation is achieved, the adaptability is good, the operation safety of workers is guaranteed, and the whole hoisting work is fast, accurate and stable. Unmanned and intelligent operation is realized to a certain extent, and a worker only needs to learn to use an unmanned aerial vehicle to shoot an environment and learn to use and operate upper computer software, so that an automatic operation process can be realized. No additional mechanical device needs to be installed on the crane, the original structure of the crane is not damaged, and safety and high efficiency are achieved. And in case of emergency, the hoisting procedure can be immediately jumped out, emergency stop is realized, and the working safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of lifting operations of crawler cranes, and particularly to the path planning of lifted objects, the intelligent anti-sway control system and control method during the lifting process, and the method for accurately positioning the lifted object. Background Art

[0002] At present, with the rapid development of the economy, the market for national infrastructure construction is getting larger and larger. Crawler cranes are widely used in industries such as wind turbine lifting, power construction, petrochemical industry, bridge and water conservancy, and port ships due to their excellent performance such as large lifting weight, high lifting height, and ability to travel with load.

[0003] When a crawler crane is performing a lifting operation, the working environment is relatively complex, and various obstacles are inevitable. Moreover, the facilities and buildings at the target point are also a type of obstacle. Considering the flexibility of the steel wire rope and the external forces existing during the movement of the boom, the lifted object will inevitably exhibit spatial sway. Due to the large slenderness ratio structure of the truss boom of the crawler crane, the rigid body movement, elastic vibration of the boom, and the sway of the lifted object exist simultaneously, seriously affecting the safety and efficiency of the crane operation. Currently, the lifting operation of crawler cranes is usually completed by experienced workers. The above method can effectively avoid the collision between obstacles and the lifted object, reduce the sway of the lifted object to a certain extent, and ensure a certain positioning accuracy. However, it overly relies on the experience of the operators, resulting in low efficiency and threatening the safety of the workers. The traditional lifting scheme can no longer meet the current increasingly strict lifting operation standards. Therefore, the present invention proposes a more advanced intelligent lifting method for crawler cranes. Summary of the Invention

[0004] The object of the present invention is to solve the problems of excessively low manual efficiency and over-reliance on the experience of workers during the lifting process of crawler cranes, and propose an intelligent lifting scheme for crawler cranes to improve the safety and working efficiency of the lifting operation.

[0005] The technical solution of the present invention:

[0006] An intelligent lifting system based on a crawler crane, the intelligent lifting system includes a path planning module, an anti-sway control module, an accurate positioning module, and an information interaction module.

[0007] The path planning module is used to plan the optimal lifting path of the crawler crane, so that the crawler crane works according to the optimal lifting path.

[0008] The anti-sway control module uses the feedback adjustment method to feedback and adjust the data monitored by each sensor, so as to achieve the purpose of anti-sway during the operation of the crawler crane.

[0009] The described information interaction module uses a wireless data transceiver device to wirelessly transmit the data of the sensors required by each module to the industrial control computer, enabling the industrial control computer to detect and control each sensor.

[0010] The described precise positioning module: When the path planning module plans a path, it needs the coordinate information of the starting point, the target point, and the crane's slewing center. This coordinate information is given by the precise positioning module. The specific implementation process is as follows:

[0011] Precise positioning module

[0012] Step 1: Sensor installation. Install GNSS integrated navigation systems above the hook, the target point, and directly above the crane's slewing center respectively, ensuring that there is no obstruction above the installation position of the satellite signal receiving antenna in the GNSS integrated navigation system. Install DC power supplies at the above positions to supply power to the GNSS integrated navigation systems, and install data transmission units DTU to connect with the GNSS integrated navigation systems to ensure the wireless transmission of positioning data.

[0013] Install a laser rangefinder sensor and a vision sensor at the target point, ensuring that the laser rangefinder sensor and the vision sensor are oriented in the same direction and the installation positions are close enough. Install a lower computer at the same position to receive the data of the laser rangefinder sensor, the vision sensor, and the GNSS integrated navigation system, and process the images output by the vision sensor. Install a bridge on one side of the lower computer to establish data communication between the lower computer at the target point and the upper computer in the cockpit.

[0014] Step 2: Initial positioning. Before the lifting operation, obtain the starting coordinates of the hook through the GNSS integrated navigation system installed on the hook, manually measure the vertical distance from the hook to the center of the bottom of the lifted object, and subtract this vertical distance from the elevation positioning data of the hook coordinates to obtain the coordinate information of the starting position of the lifted object. Obtain the coordinate information of the target point through the GNSS integrated navigation system placed at the target point. Obtain the coordinate information of this point through the GNSS integrated navigation system set directly above the crane's slewing center, measure the vertical distance from this point to the crane's slewing center, and subtract this vertical distance from the elevation data of this point to obtain the positioning coordinates of the crane's slewing center.

[0015] Step 3: Precise positioning. Paste a target with a black circle on a white background on the object to be lifted. The orientation of the target is opposite to that of the vision sensor. After the crane completes the hoisting operation of initial positioning, the vision sensor takes an image of the installation surface of the object to be lifted. The lower computer identifies and locates the center of the black circle target in the image to obtain the pixel coordinates of the center of the black circle target. The lower computer combines the distance measurement data of the installation surface of the object to be lifted by the laser distance sensor, the coordinate data of the GNSS integrated navigation, and the attitude data to convert the pixel coordinates of the center of the black circle target into the world coordinate system, obtaining the coordinate information of the center of the black circle, and indirectly obtaining the coordinate information of the center of the installation surface of the object to be lifted.

[0016] The working process of the intelligent hoisting system is as follows: The path planning module plans the most suitable movement path of the object to be lifted in the hoisting environment according to the starting point coordinates, target point coordinates of the object to be lifted, and the crane slewing center coordinates output by the GNSS integrated navigation system determined by the initial positioning. The initial path is converted into a crane action sequence, and the crawler crane completes the hoisting after the initial positioning according to the action sequence. During this process, the anti-sway control module ensures the stable movement of the object to be lifted. When the vision sensor can detect the target on the object to be lifted, the linear distance and relative position coordinates of the installation surface of the object to be lifted relative to the target point are obtained through the laser distance sensor and the vision sensor at the target point respectively. The path planning module plans the path again according to the newly obtained coordinate information of the object to be lifted and converts it into a crane action sequence, and realizes the final precise hoisting positioning with the assistance of the anti-sway control module.

[0017] The advantages of the present invention are as follows: It realizes intelligent hoisting operations, has good adaptability, ensures the safety of workers' operations, and enables the entire hoisting work to achieve fast, accurate, and stable effects. To a certain extent, it realizes unmanned and intelligent operations. Workers only need to learn to use the drone to photograph the environment and learn to use and operate the upper computer software to realize the automated operation process. There is no need to install additional mechanical devices on the crane, which does not damage the original structure of the crane, and is safe and efficient. In case of an emergency, the hoisting program can be immediately exited and an emergency stop can be made, improving the work safety. Description of the Drawings

[0018] Figure 1 It is a flowchart of the present invention.

[0019] Figure 2 It is a diagram of the sensor installation positions of the present invention.

[0020] Figure 3 It is a rigid body model diagram of the crane according to an embodiment of the present invention.

[0021] Figure 4 It is a crane motion relationship diagram according to an embodiment of the present invention.

[0022] Figure 5This is the flowchart of the path planning for the embodiments of the present invention. Detailed implementation manners

[0023] The following further elaborates on the content of the present invention through examples in conjunction with the accompanying drawings, so as to facilitate understanding by those skilled in the same industry:

[0024] An intelligent hoisting system based on a crawler crane, considering the path planning after obstacle avoidance of the crawler crane, the anti-sway precision control of the lifted object during the hoisting process, and the in-place precision control of the lifted object. The intelligent hoisting system includes the following:

[0025] The intelligent hoisting system includes a path planning module, an anti-sway control module, a precise positioning module, and an information interaction module.

[0026] The path planning module is used to plan the optimal hoisting path of the crawler crane, so that the crawler crane works according to the optimal hoisting path.

[0027] Specifically: it includes the path planning module and the unmanned aerial vehicle (UAV) of the loading workstation; it is realized by the environment reconstruction method based on the UAV and the improved Rapidly-exploring Random Trees (RRT) algorithm. The steps are as follows:

[0028] Step 1: Construct the rigid body model of the crawler crane

[0029] According to the structure and working principle of the crawler crane and the lifted object, each structure of the crane hoisting system is abstracted into 5 rigid bodies, including abstracting the lower vehicle and the upper slewing platform into a cuboid block; abstracting the boom and the lifted object into a cylindrical block; abstracting the steel wire rope into a virtual rigid body straight line that can move up and down (as Figure 3 shown).

[0030] Step 2: Construct the kinematic model of the crawler crane

[0031] Establish the kinematic model according to the rigid body model of the crawler crane. The motion relationship diagram is as Figure 4As shown in the figure. Place the local coordinate system {0} of the lower part of the crane at the center of the bottom of the lower part, set the local coordinate system {1} of the slewing platform at the position where it intersects with the slewing center on the upper part of the slewing platform, set the local coordinate system {2} of the main boom amplitude change at the amplitude change center, and place the local coordinate system {6} of the hook at the center of the bottom of the lifted object. In the actual crane structure, the steel wire rope is not connected at the center of the main boom head, but is connected by a pulley block that is offset a certain distance from the center axis of the main boom. Therefore, when establishing the kinematic model, the local coordinate system {3} of the boom head is set at the axis of the boom head, the local coordinate system {4} of the jib is set at the axis of the jib head. If the crane only has a main boom, the coordinate system is set at the axis of the boom head, and the local coordinate system {5} of the steel wire rope at the boom head is set at the center of the pulley block for lowering the steel wire rope. Three coordinate systems are used to simulate the actual connection position of the steel wire rope. Therefore, the pose of the crane lifting system can be expressed as P{θ1, θ2, θ3, θ4, θ5, d6}; where, θ1 is the slewing angle of the slewing platform, θ2 is the amplitude change angle of the boom, θ3 is the angle between the extension line of the boom and the horizontal direction, θ4 is the angle between the normal direction of the boom and the horizontal direction, θ5 is the angle between the negative normal direction of the boom and the horizontal direction, and d6 is the elongation distance of the steel wire rope;

[0032] Let d1 be the total height of the slewing platform and the lower part of the crawler crane, a2 be the distance between the upper slewing axis and the boom amplitude change axis, a3 be the length of the main boom, a5 be the offset distance of the pulley block, and d6 be the elongation distance of the steel wire rope.

[0033] From Figure 4 The kinematic model expresses the pose of the end of the lifted object as:

[0034]

[0035] Among them, is the pose transformation matrix from the local coordinate system {0} to the local coordinate system {1}, T1 2 is the pose transformation matrix from the local coordinate system {1} to the local coordinate system {2}, T2 3 is the pose transformation matrix from the local coordinate system {2} to the local coordinate system {3}, T3 4 is the pose transformation matrix from the local coordinate system {3} to the local coordinate system {4}, is the pose transformation matrix from the local coordinate system {4} to the local coordinate system {5}, is the pose transformation matrix from the local coordinate system {5} to the local coordinate system {6}, is the pose transformation matrix from the local coordinate system {0} to the local coordinate system {6}.

[0036] During the crane lifting process, the steel wire rope always remains vertically downward. Therefore, the algebraic relationship between θ2, θ3, θ4, and θ5 can be obtained:

[0037]

[0038] Express θ3, θ4, and θ5 in terms of θ2, and substitute each transformation matrix into Equation to obtain:

[0039]

[0040] Given the coordinates (P x , P y , P z ) at the end of the object to be lifted, the data of each joint angle variable can be obtained by inverse solution. Therefore, the pose transformation matrix of the object to be lifted can be set as and then θ1, θ2, and d6 can be obtained by inverse solution:

[0041]

[0042] where t represents

[0043]

[0044] According to the forward and inverse kinematic formulas obtained, the pose parameters of the crane can be obtained by inverse solution through the movement of the object to be lifted.

[0045] Step 3: Based on the environmental model obtained by UAV scanning and the kinematic relationship of the crane, implement path planning for the crawler crane based on the improved RRT. As Figure 5 shown, it includes the following steps:

[0046] S1: The UAV conducts aerial photography of the environment, uses the visible light camera to collect a sufficient number of photos, and inputs the collected photos into the environmental modeling software. The environmental modeling software can automatically generate a three-dimensional model according to the collected photos. Use the bounding box algorithm to envelope the obstacles in the three-dimensional model of the lifting environment with regular geometric bodies, and expand the obstacles in the environment according to the size of the object to be lifted, so that the lifting object can be represented by points during planning, and the bounding box algorithm extracts the three-dimensional space information into a grid map in the form of a two-dimensional array.

[0047] S2: Start path planning, initialize the search tree, and set the information contained in each node in the search tree as the three action numbers from the previous crane pose to the current crane pose, and record the previous node of this node, that is, the parent node. The action number refers to numbering the three actions of slewing, luffing, and hoisting. Set the root node of the search tree as the starting point of lifting, and set the initial action of the root node according to the configuration information.

[0048] S3: Select a random point P in the planning space.

[0049] S4: Traverse all nodes in the search tree, select a node as the parent node of the random point, and the selection rule is that the direction from the parent node to the random point P is close to the direction towards the target point and the distance is the shortest.

[0050] S5: According to the crane action information contained in the parent node selected in step S4, select the action to reach the random point, set this action as m, and the selection rule is judged according to K(m); where K(m) = |V(m)·Vp| + F(m), it is set that only one action number movement is performed for each action, and there are three action numbers for action m in total, so three K(m) can be generated, and the action with the largest K(m) value is selected as the extension action. V(m) is the direction vector corresponding to the action m that may be extended, Vp is the direction vector from the parent node to the random point P, and F(m) is the action selection correction parameter, which is used to reduce the number of action switches in the path and accelerate the search when generating the path. When action m is the same as the parent node action, F(m) = C1*Rand, where C1 is the preset action holding parameter and Rand is a random number between 0 and 1; when action m is different from the parent node action, if the posture corresponding to the parent node action is already the same as the in-place posture, then F(m) = C1*Rand, otherwise F(m) = 0.

[0051] S6: Along the action selected in step S5, intercept with a step size t to generate the next node. At this time, according to the conversion formulas of θ1, θ2, and d6 calculated in step two, substitute the coordinates of the end of the lifted object at this node (P x ,P y ,P z ) and then calculate the three action number information of the crane pose at this node. The step size t is the set movement distance parameter.

[0052] S7: Judge whether the new node is valid. The judgment condition is that the corresponding crane posture is valid and there is no collision between the connection line between the new node and the parent node. The crane posture being valid means that there is no collision of the whole vehicle when the crane is operating and each action number is within the set threshold range.

[0053] S8: Judge whether the end point is reached. The judgment condition is that the distance from the node to the end point is less than the threshold. If the end point is reached, jump out of the loop and enter S10.

[0054] S9: Repeat steps S3 - S8 until the number of repetitions reaches the set value or jumps out in step S8.

[0055] S10: Organize the path according to the search tree nodes. The path includes the positions of all nodes in the search tree and the action number information of each node.

[0056] S11: Repeat steps S2 - S10 multiple times to obtain multiple paths. Since each search path is independent, parallel mode can be used to accelerate.

[0057] S12: Evaluate the quality of each path according to E(p) = L(p) + Sw(p) * C2, and output the path with the lowest E(p). Here, p represents the path, L(p) represents the length of path p, Sw(p) represents the number of switching actions in path p, and C2 is a manually set action switching penalty coefficient.

[0058] Thus, the optimal path has been planned. Import this path into the industrial control computer to automatically control the motion system of the crane, thereby realizing the automatic hoisting of the crane.

[0059] The anti-sway control module uses the feedback adjustment method to feedback and adjust the data monitored by each sensor, and further realizes the purpose of anti-sway during the operation of the crawler crane.

[0060] The anti-sway control module includes a programmable logic controller, a solenoid valve, an industrial control computer, a wire rope length detection module, an angle detection module, and a communication module; an anti-sway algorithm operation module and a motion control module are loaded on the industrial control computer;

[0061] The wire rope length detection module consists of two sets of GNSS modules, which are respectively installed at the boom head position and the hook position of the crane; by calculating the position coordinates of the two GNSS modules, the length information of the wire rope can be obtained; the GNSS modules at the boom head and hook positions are each connected to a LoRa wireless communication module as a data sender to transmit the coordinate information to the industrial control computer; the industrial control computer is equipped with two LoRa modules as receivers to receive the positioning data of the GNSS modules from the boom head and the hook respectively;

[0062] The angle detection module consists of a slewing angle sensor, a luffing angle sensor, and a dynamic inclination sensor; the slewing angle sensor is installed at the slewing center position to detect the slewing angle of the boom; the luffing angle sensor is installed on the boom structure to measure the luffing angle of the boom; the dynamic inclination sensor is installed at the hook to monitor the yaw angle of the lifted object; among them, the slewing angle sensor and the luffing angle sensor are connected to the industrial control computer through wired optical fiber communication, while the dynamic inclination sensor uses Zigbee wireless communication technology to transmit data to the industrial control computer;

[0063] The anti-sway algorithm operation module is implemented through the PID input shaping control algorithm, and includes a position PID control unit and an input shaping unit; where:

[0064] The position PID control unit, based on the real-time data collected by the wire rope length detection module and the angle detection module, including the boom slewing angle, the luffing angle, and the wire rope length, ensures that the crane can accurately lift the lifted object from the initial position to the target position through closed-loop control; the position PID control unit calculates and outputs the boom slewing speed, the luffing speed, and the wire rope lifting speed in real time; among them, the three key parameters of the position PID controller, namely the proportional coefficient Kp, the integral time Ki, and the derivative time Kd, need to be determined through on-site actual debugging;

[0065] The input shaper unit is responsible for shaping the three motion speeds output by the position PID control unit; for the wire rope lifting motion, theoretically the lifted object should not produce yaw, but in actual operation, the vibration of the boom will cause the lifted object to yaw, so a single-mode ZVD input shaper is designed to suppress the boom vibration, thereby eliminating the yaw of the lifted object; for the boom slewing and luffing motions, since there is both the yaw of the lifted object caused by the boom vibration and the yaw effect generated by the motion itself, another single-mode ZVD shaper is specifically configured to suppress the yaw of the lifted object caused by the slewing and luffing motions; the specific design process is as follows:

[0066] The pulse amplitude and time delay of each single-mode ZVD input shaper are:

[0067]

[0068] Among them, ξ is the damping ratio during the yaw process of the lifted object or the vibration process of the crane boom, ω n is the natural frequency during the yaw process of the lifted object or the vibration process of the crane boom, A i is the pulse amplitude, t i is the time delay; among them, the damping ratio during the yaw process of the lifted object or the vibration process of the crane boom is obtained by actually measuring the free decay response curve of the vibration, and the damping ratio is obtained by using the free decay response curve of the vibration;

[0069] The spatial yaw model of the lifted object is set as a simple pendulum model, so the yaw frequency of the lifted object is:

[0070]

[0071] In the formula, l is the wire rope length, g is the acceleration due to gravity, ω is the yaw frequency of the lifted object, taking g = 9.8 N / kg, π = 3.1416;

[0072] The single-mode ZVD shaper for the yaw of the lifted object is designed based on the yaw frequency of the lifted object;

[0073] The boom vibration frequency is obtained through finite element simulation analysis, and a single-mode ZVD shaper for boom vibration is designed accordingly; this shaper shapes the wire rope lifting speed output by the position PID unit;

[0074] The dual-mode ZVD input shaper is obtained by convolution calculation of the single-mode ZVD shaper for boom vibration and the single-mode ZVD shaper for the swing of the lifted object; it is used to shape the slewing speed during the slewing motion of the crane boom and the luffing speed during the luffing motion of the crane boom obtained by the position PID control unit;

[0075] The described motion control module is used to convert the speed data calculated by the anti-sway algorithm operation module into corresponding handle signals;

[0076] The described programmable controller is used to convert the handle signal into a current signal to drive the solenoid valve to operate the hydraulic valve.

[0077] Before starting the hoisting operation, the system is initialized first. The wire rope length detection module and the angle detection module start to monitor the wire rope length, the slewing angle of the boom, the luffing angle of the boom, and the spatial swing angle of the lifted object, and transmit the monitored data to the anti-sway algorithm operation module of the industrial control computer; the operator inputs the slewing target angle, luffing target angle, and wire rope target length of the crane boom into the system; the anti-sway algorithm operation module in the industrial control computer calculates the slewing speed, luffing speed, and wire rope lifting speed after shaping processing by using the monitored data and the input parameters; the motion control module converts the shaped speed signal into a handle control signal and transmits it to the programmable controller through the communication module; the programmable controller converts the signal into a current command and outputs it to the solenoid valve, and the solenoid valve adjusts the hydraulic valve flow according to the output command of the programmable controller to achieve precise control of the slewing speed, luffing speed, and wire rope lifting speed of the boom, and finally achieves the purpose of suppressing the swing of the lifted object.

[0078] The described information interaction module uses wireless data transceiver devices to wirelessly transmit the data of the sensors required by each module to the industrial control computer, realizing the detection and control of each sensor by the industrial control computer.

[0079] The described precise positioning module: The path planning module needs to use the coordinate information of the starting point, target point, and the slewing center of the crane when planning the path, and this coordinate information is given by the precise positioning module. The specific implementation process is as follows:

[0080] Precise positioning module

[0081] Step 1. Sensor installation. Install the GNSS integrated navigation system at the hook, the target point, and directly above the crane slewing center respectively, ensuring that there is no obstruction above the installation position of the satellite signal receiving antenna in the GNSS integrated navigation system. Install a DC power supply at the above positions to supply power to the GNSS integrated navigation system, and install a data transmission unit DTU to connect with the GNSS integrated navigation system to ensure wireless transmission of positioning data.

[0082] Install a laser range finder sensor and a vision sensor at the target point, ensuring that the orientations of the laser range finder sensor and the vision sensor are the same and the installation positions are close enough. Install a lower computer at the same position to receive data from the laser range finder sensor, the vision sensor, and the GNSS integrated navigation system, and process the images output by the vision sensor. Install a bridge on one side of the lower computer to establish data communication between the lower computer at the target point and the upper computer in the cockpit.

[0083] Step 2. Initial positioning. Before the lifting operation, obtain the starting coordinates of the hook through the GNSS integrated navigation system installed on the hook, manually measure the vertical distance from the hook to the center of the bottom of the lifted object, and subtract this vertical distance from the elevation positioning data of the hook coordinates to obtain the coordinate information of the starting position of the lifted object. Obtain the coordinate information of the target point through the GNSS integrated navigation system placed at the target point. Obtain the coordinate information of this point through the GNSS integrated navigation system set directly above the crane slewing center, measure the vertical distance from this point to the crane slewing center, and subtract this vertical distance from the elevation data of this point to obtain the positioning coordinates of the crane slewing center.

[0084] Step 3. Precise positioning. Paste a target with a black circle on a white background on the lifted object, with the orientation of the target opposite to that of the vision sensor. After the crane completes the initial positioning lifting operation, take an image of the installation surface of the lifted object through the vision sensor. The lower computer identifies and locates the center of the black circle target in the image to obtain the pixel coordinates of the center of the black circle target. The lower computer combines the distance measurement data of the laser range finder sensor for the installation surface of the lifted object, the coordinate data and attitude data of the GNSS integrated navigation, and converts the pixel coordinates of the center of the black circle target into the world coordinate system to obtain the coordinate information of the center of the black circle, and indirectly obtain the coordinate information of the center of the installation surface of the lifted object.

[0085] The working process of the intelligent hoisting system is as follows: The path planning module plans the most suitable movement path of the object to be lifted in the hoisting environment according to the starting point coordinates, target point coordinates of the object to be lifted and the crane slewing center coordinates output by the GNSS integrated navigation system determined by the initial positioning. The initial path is converted into a crane action sequence, and the crawler crane completes the hoisting after the initial positioning according to the action sequence. During this process, the anti-sway control module ensures the stable movement of the object to be lifted. When the vision sensor can detect the target on the object to be lifted, the linear distance and relative position coordinates of the installation surface of the object to be lifted relative to the target point are obtained through the laser range finder and the vision sensor at the target point respectively. The path planning module plans the path again according to the newly obtained coordinate information of the object to be lifted and converts it into a crane action sequence, and with the assistance of the anti-sway control module, the final precise hoisting positioning is achieved.

Claims

1. An intelligent lifting system based on a crawler crane, characterized in that The described intelligent hoisting system includes a path planning module, an anti-sway control module, a precise positioning module, and an information interaction module loaded into an industrial control computer; The path planning module is used to plan the optimal hoisting path of the crawler crane, enabling the crawler crane to work according to the optimal hoisting path; The anti-sway control module uses a feedback adjustment method to perform feedback adjustment on the data monitored by each sensor, thereby achieving the purpose of anti-sway during the operation of the crawler crane; The information interaction module uses wireless data transceiver devices to wirelessly transmit the data of the sensors required by each module to the industrial control computer, realizing the detection and control of each sensor by the industrial control computer; The precise positioning module is used to give the coordinate information of the starting point, the target point, and the crane slewing center required by the path planning module when planning the path; the specific implementation process is as follows: Step 1: Sensor installation; Install GNSS integrated navigation systems above the hook, the target point, and directly above the crane slewing center respectively, ensuring that there is no obstruction above the installation position of the satellite signal receiving antenna in the GNSS integrated navigation system; Install DC power supplies at the above positions to supply power to the GNSS integrated navigation systems, and install data transmission units DTU to connect with the GNSS integrated navigation systems to ensure wireless transmission of positioning data; Install a laser range finder sensor and a vision sensor at the target point, ensuring that the orientations of the laser range finder sensor and the vision sensor are the same and the installation positions are close enough; Install a lower computer at the same position to receive the data of the laser range finder sensor, the vision sensor, and the GNSS integrated navigation system, and process the images output by the vision sensor; Install a bridge on one side of the lower computer to establish data communication between the lower computer at the target point and the upper computer in the cab; Step 2: Initial positioning; Before the hoisting operation, obtain the starting coordinates of the hook through the GNSS integrated navigation system installed on the hook, manually measure the vertical distance from the hook to the center of the bottom of the lifted object, and subtract this vertical distance from the elevation positioning data of the hook coordinates to obtain the coordinate information of the starting position of the lifted object; Obtain the coordinate information of the target point through the GNSS integrated navigation system placed at the target point; Obtain the coordinate information of this point through the GNSS integrated navigation system set directly above the crane slewing center, measure the vertical distance from this point to the crane slewing center, and subtract this vertical distance from the elevation data of this point to obtain the positioning coordinates of the crane slewing center; Step 3: Precise positioning; Paste a target with a white background and a black circle on the lifted object, with the orientation of the target opposite to that of the vision sensor. After the crane completes the initial positioning hoisting operation, take an image of the installation surface of the lifted object through the vision sensor, and the lower computer identifies and locates the center of the black circle target in the image to obtain the pixel coordinates of the center of the black circle target. The lower computer combines the distance measurement data of the laser range finder sensor to the installation surface of the lifted object, the coordinate data and attitude data of the GNSS integrated navigation, and converts the pixel coordinates of the center of the black circle target into the world coordinate system to obtain the coordinate information of the center of the black circle, and indirectly obtain the coordinate information of the center of the installation surface of the lifted object.

2. An intelligent hoisting method based on a crawler crane is carried out by using the intelligent hoisting system based on a crawler crane described in claim 1, characterized in that, The working process of the intelligent hoisting system is as follows: The path planning module plans the most suitable movement path of the object to be lifted in the hoisting environment according to the starting point coordinates, target point coordinates of the object to be lifted and the crane slewing center coordinates output by the GNSS integrated navigation system determined by the initial positioning; The initial path is converted into a crane action sequence, and the crawler crane completes the hoisting after the initial positioning according to the action sequence. During this process, the anti-sway control module ensures the stable movement of the object to be lifted; When the vision sensor can detect the target on the object to be lifted, the linear distance and relative position coordinates of the installation surface of the object to be lifted relative to the target point are obtained through the laser range finder and the vision sensor at the target point respectively. The path planning module plans the path again according to the newly obtained coordinate information of the object to be lifted and converts it into a crane action sequence, and with the assistance of the anti-sway control module, the final accurate hoisting positioning is achieved.

Images