Multi-machine collaborative hoisting track planning method for full-rotation crane ship
Through the PTP theory and polynomial expression method, the problem of poor coordination in the coordinated lifting of multiple lifting ships is solved, and efficient, safe and intelligent operation of coordinated lifting of all-rotary cranes is achieved.
Patent Information
- Application Number
- CN202410450195.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-04-15
- Publication Date
- 2024-12-20
AI Technical Summary
The prior art has coordination problems in the coordinated lifting of multiple lifting ships, resulting in reduced operational safety, reduced efficiency and increased energy consumption, and lacks an effective multi-machine coordinated lifting trajectory planning method.
The multi-machine collaborative lifting trajectory planning method based on PTP theory is adopted. The optimal trajectory is expressed through polynomials and the control curve of each crane is reversed to ensure the coordination and efficiency of multi-machine collaborative lifting.
The intelligence level of multi-machine coordinated lifting operation has been improved, the dependence on manual operation is reduced, the operation efficiency is optimized, the safety is improved, and the stable and efficient lifting operation can be maintained under complex sea conditions and meteorological conditions.
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Figure CN119165768A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of ship lifting control, and in particular to a multi-machine collaborative lifting trajectory planning method for a fully revolving crane ship based on PTP theory. Background Art
[0002] With the rise of large platforms such as offshore wind power, a single fully revolving crane vessel is faced with the difficulty of completing heavy and large lifting operations. In many large-scale lifting operations, multiple crane vessels are required to work together. However, the current collaborative lifting of multiple crane vessels usually relies on the experience of relevant technicians through manual operation. This method has coordination problems and is easily affected by factors such as personnel experience, sea conditions, and weather conditions, resulting in a series of problems such as reduced operation safety, reduced efficiency, and increased energy consumption.
[0003] In order to solve these problems, the intelligentization of multi-machine collaborative lifting operations on fully revolving crane vessels is a reliable solution. The core of realizing intelligence lies in a reliable and efficient multi-machine collaborative lifting trajectory planning method. At present, there are relatively few studies on trajectory planning technology for multi-machine collaborative lifting operations on fully revolving crane vessels, and similar related technologies have problems such as long decision-making time and relatively poor accuracy.
[0004] For example, the Chinese patent application with application publication number CN114237048A discloses an intelligent operation trajectory planning system for polar ship unmanned cranes. This invention is a trajectory planning method proposed for a single crane, and its solution ideas, methods and models are not suitable for the trajectory planning process of multi-machine collaborative lifting operations. If this method is directly applied to multi-machine collaborative lifting, it will lead to problems such as mismatch of multi-machine collaborative design parameters, lack of key multi-machine collaborative constraints, and difficulty in solving multi-machine collaborative problems. Summary of the invention
[0005] In view of the shortcomings of the prior art, the present invention provides a trajectory planning method for multi-machine collaborative lifting on a full-revolving crane ship, which mainly uses polynomials to express the optimal trajectory, and inversely solves the control curve of each crane through the known trajectory curve to ensure good coordination during multi-machine collaborative lifting.
[0006] The technical means adopted by the present invention are as follows:
[0007] A method for planning a multi-machine collaborative lifting trajectory for a fully revolving crane vessel, comprising:
[0008] S1. Obtaining the information of the multi-machine collaborative lifting task of the full-revolving crane ship, including the position information at the beginning and end of the lifting, the target load information and the parameters of the crane ship;
[0009] S2. Establish a dynamics model of the multi-machine system of a fully revolving crane ship, including establishing a dynamics model of each crane ship crane separately;
[0010] S3. Based on the PTP theory, a trajectory planning model for the coordinated lifting of multiple machines on a fully revolving crane ship is established and solved to obtain an intermediate model of the trajectory for the coordinated lifting of multiple machines on a fully revolving crane ship;
[0011] S4. Based on the intermediate model of the trajectory of the multi-machine collaborative lifting operation of the full-revolving crane ship, the boom amplitude angle constraint, the sling length constraint, the dynamic constraint, the drive device power constraint and the trajectory range constraint during the multi-machine collaborative lifting operation of the full-revolving crane ship are considered, and the optimal dynamic control parameter time history process corresponding to each crane is obtained by inverse solution;
[0012] S5: If the solution converges, the optimal lifting trajectory solution is output; otherwise, S3 is executed to update the trajectory design variables and continue to perform iterative solution.
[0013] Compared with the prior art, the present invention has the following advantages:
[0014] 1. The trajectory planning method for the multi-machine collaborative lifting operation of a fully revolving crane vessel provided by the present invention aims to improve the intelligence level of the multi-machine collaborative lifting operation of a fully revolving crane vessel, reduce the reliance on manual operation, optimize the efficiency of the multi-machine collaborative operation, and improve the safety of the operation. Through this trajectory planning method, it is possible to better adapt to the complex and changeable sea conditions and meteorological conditions, and realize the stable and efficient lifting operation of the multi-machine collaborative lifting operation of a fully revolving crane vessel.
[0015] 2. Use polynomials to express the optimal trajectory, and inversely solve the control curve of each crane through the known trajectory curve to ensure good coordination when multiple cranes are working together. In addition, the solution efficiency of this method meets the needs of intelligence and can ensure rapid response during collaborative operations. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.
[0017] Figure 1 This is the multi-machine collaborative lifting trajectory planning optimization framework based on PTP theory in the present invention.
[0018] Figure 2 A schematic diagram is established for the coordinate system of the present invention.
[0019] Figure 3 A schematic diagram of establishing a coordinate system in an embodiment of the present invention.
[0020] Figure 4 It is a simplified schematic diagram of a crane vessel in an embodiment of the present invention.
[0021] Figure 5 Schematic diagram of the common hoisting range in the embodiment of the present invention, wherein (a) is a three-dimensional view of the hoisting range, and (b) is a top view of the hoisting range.
[0022] Figure 6 This is the optimal trajectory diagram in the embodiment of the present invention.
[0023] Figure 7 1 is the crane control curve of crane No. 1 in the embodiment of the present invention. Among them, (a) is the rotation angle curve, (b) is the rotation speed curve, (c) is the luffing angle curve, (d) is the luffing speed curve, (e) is the sling length curve, and (f) is the sling speed curve.
[0024] Figure 8 2 crane control curves in the embodiment of the present invention. Among them, (a) is a slewing angle curve, (b) is a slewing speed curve, (c) is a luffing angle curve, (d) is a luffing speed curve, (e) is a sling length curve, and (f) is a sling speed curve. DETAILED DESCRIPTION
[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.
[0026] In order to fill the existing technical gap in the collaborative lifting trajectory planning of multiple machines of a full-revolving crane ship and better improve the collaborative operation performance of multiple machines of a full-revolving crane ship, the present invention proposes a fully-revolving crane ship multi-machine collaborative lifting trajectory planning method based on the PTP (Point to Point) theory with high efficiency and high collaboration.
[0027] The essence of PTP theory is to transform the optimal control problem of solving the differential equations of position, velocity and acceleration of the dynamic system into a nonlinear constraint problem of solving the corresponding high-order polynomial coefficients. This method can solve the dynamic state variables at any time point during the optimization calculation process, including displacement, velocity and acceleration, that is,
[0028]
[0029] Where s(t) represents the position function, represents the speed function, represents the acceleration function. In the PTP method, s(t), They can all be expressed by n-degree polynomials:
[0030]
[0031] Where b n Represents the polynomial coefficient, where n is a non-negative integer.
[0032] Generally speaking, the system starts from a stationary state and stops at a specific final position. Therefore, the system dynamics state variables at the initial moment are:
[0033]
[0034] At the end time T, the system dynamics state variables are:
[0035]
[0036] In the formula, K represents the position of the system at the final moment. At this time, the expressions of coefficients b3, b4, and b5 can also be solved:
[0037]
[0038]
[0039]
[0040] From the above derivation process, we can see that the coefficient b in the equation group is n Once determined, the position, velocity and acceleration of the dynamic system at any time can be obtained.
[0041] Figure 1The complete optimization framework of ballast water allocation for a fully revolving crane vessel is presented: (1) Determine the task information of the multi-machine collaborative lifting task of a fully revolving crane vessel, including the position information at the beginning and end of the lifting, the target load information, and the crane vessel parameters; (2) Establish a dynamic model of the multi-machine system of a fully revolving crane vessel. At this stage, the dynamic models of each crane on the crane vessel are established to lay the foundation for the subsequent collaborative operation trajectory optimization; (3) Establish and solve the trajectory planning model of the multi-machine collaborative lifting of a fully revolving crane vessel to obtain the intermediate model of the multi-machine collaborative lifting trajectory of a fully revolving crane vessel; (4) Based on the intermediate model of the multi-machine collaborative lifting operation trajectory of a fully revolving crane vessel, fully consider the multi-machine interference constraints, multi-machine dynamic constraints, working condition constraints, performance constraints, and operation safety constraints during the multi-machine collaborative lifting operation of a fully revolving crane vessel, and inversely solve the time history process of the dynamic optimal control parameters corresponding to each crane; (5) If the solution converges, the optimal lifting trajectory solution is output. Otherwise, the trajectory design variables are updated to continue the iterative solution.
[0042] Furthermore, the hoisting trajectory of the full-revolving crane ship is usually defined as the curve swept by the load center of gravity in space. In the scenario of multi-machine collaborative hoisting, in order to accurately represent the position coordinates of the load center of gravity at any time during the hoisting process, a corresponding spatial coordinate system is established, such as Figure 2 As shown. The coordinate system takes the intersection of the rotation centerline of the column of crane ship No. 1 and the deck plane as the coordinate origin O. The rotation axis of the column of crane ship No. 1 is the z-axis. Along the depth direction of the ship, upward is positive and downward is negative. The intersection of the plane passing through the z-axis and perpendicular to the centerline plane of the crane ship and the deck surface is the x-axis. Along the width direction, it is positive to the starboard and negative to the port. The intersection of the deck surface and the centerline plane of crane ship No. 1 is the y-axis. Along the length direction of the ship, it is positive to the bow and negative to the stern. According to industry requirements, the slings of each crane remain vertical during the lifting process, and ensure that the hoisted cargo remains horizontal without rotation and deviation until the lifting operation is completed. Therefore, based on the obtained multi-machine collaborative operation trajectory, the rotation angle θ of the base of each crane can be inversely calculated r1 (rad),θ r2 (rad),…,θ rk (rad) The boom angle of each crane vessel θ v1 (rad),θ v2 (rad),…,θ vk (rad), where 1, 2, 3, ..., k is the number of the full-revolving crane ship. The corresponding expression is as follows:
[0043]
[0044]
[0045] In the formula, θ r1 (rad),θ r2 (rad),…,θ rk(rad) is the rotation angle of each crane base, θ v1 (rad),θ v2 (rad),…,θ vk (rad) is the boom angle of each crane ship, 1, 2, 3, ..., k is the number of the full-revolving crane ship, d x (m),d y (m),d z (m) are the displacements of the lifting trajectory in the x, y, and z directions in the coordinate system, N xk (m) and N yk (m) is the deviation between the fixed position of the sling of crane ship k and the center of gravity of the hoisted cargo in the x and y directions; D xk (m) and D yk (m) are the x and y coordinates of the rotation center of crane ship No. k; H m (m) is the height of the hoisted cargo; l k (m) is the length of the sling of crane ship No. k; L k (m) is the boom length of crane k; h k (m) is the height of the slewing platform of crane vessel No. K.
[0046] In order to effectively define the dynamic state variables of the multi-machine system, it is crucial to choose the appropriate polynomial degree. As the polynomial degree increases, the polynomial can achieve richer curve forms, that is, it can more flexibly express the dynamic change process of the trajectory, but the increase in the polynomial degree will introduce more design variables to be determined, resulting in an increase in the optimization calculation cost. Compared with polynomials of other orders, the sixth-order polynomial can significantly improve the solution efficiency of the optimization program and take into account the flexibility of the trajectory obtained by the solution. Therefore, as a preferred embodiment of the present invention, this embodiment determines the selection of a sixth-order polynomial to determine the trajectory of the lifting operation of the full-revolving crane ship. It should be noted that the trajectory planning method for the collaborative lifting of multiple machines of the full-revolving crane ship of this patent invention is not limited to the use of sixth-order polynomials, but can also be extended to higher-order polynomials. In order to illustrate the problem, a sixth-order polynomial is selected to describe in the present invention.
[0047] The hoisting operation trajectory of the full-revolving crane ship is a spatial trajectory. For the convenience of description, it is defined from the three directions of x, y, and z, as follows:
[0048]
[0049] Where b x0 , b x1 , …, b x6 b is the 0-6th order polynomial coefficient of the x-coordinate of the hoisting trajectory; y0 , b y1 , …, b y6 b is the 0-6th order polynomial coefficient of the y coordinate of the hoisting trajectory;z0 , b z1 , …, b z6 is the 0-6th order polynomial coefficient of the z coordinate of the hoisting trajectory; d x0 (m), d y0 (m), d z0 (m) is the initial coordinate of the trajectory in the x, y, and z directions.
[0050] In order to reliably and effectively inversely calculate the working posture of multiple cranes while satisfying the dynamic laws of the multi-machine system, it is necessary to optimize the extension and contraction amount of the sling of each crane ship. Therefore, the PTP theory can be used to describe it as follows:
[0051]
[0052] Where b k0 , b k1 , …, b k6 is the 0-6th order polynomial coefficient of the kth crane cable expansion and contraction, l k0 (m) is the initial length of the sling of crane ship No. K.
[0053] The solution of PTP theory requires the introduction of relevant boundary conditions. At the initial and final state moments, the starting and ending positions of the trajectory of the multi-machine collaborative operation of the fully revolving crane ship depend on the starting and ending positions set by the actual offshore lifting task. In addition, the offshore lifting process has high requirements for operational safety, so it is required that the fully revolving crane ship should not produce rigid or flexible impacts during the rotation, amplitude change, and lifting process. Therefore, the initial and final state variables of the multi-machine collaborative offshore lifting operation should be limited to zero. In summary, the known boundary conditions are substituted into the above formulas, and the corresponding coefficients are solved as follows:
[0054]
[0055] In the formula, β j represents the final state value of the state quantity marked by j (j = x, y, z, 1, 2, ..., k), α j represents the initial time value of the state quantity marked as j, t f (min) represents the hoisting time. From the above formula, we can see that only b j6 To be solved, so b should be used in solving the optimal trajectory j6 is selected as the design variable. In addition, the lifting time t f (min), the final length of the sling α1, α2, …, α k (m) should also be selected as the design variable. In summary, the corresponding trajectory planning design variable x in multi-machine collaborative lifting trajectory planning is i Determined as:
[0056] x i=[b j6 ,α1,α2,…,α k ,t f ] (j=x,y,z,1,2,…,k)
[0057] The polynomial coefficient b j6 The upper and lower boundaries are selected between negative infinity and positive infinity. The final length of the sling is α1, α2, ..., α k (m) The determination of the upper and lower boundaries depends on the length limit of the crane ship's sling. Lifting time t f The determination of the upper and lower bounds of (min) requires selecting a time interval sufficient to complete the lifting task based on the experience of relevant operators. Therefore, the corresponding upper and lower bounds of the design variables in multi-machine collaborative lifting trajectory planning are shown in Table 1.
[0058] Table 1 Upper and lower boundaries of design variables for multi-machine collaborative lifting trajectory planning optimization model
[0059]
[0060] When multiple machines are working together on a fully revolving crane ship for lifting, it is necessary to impose constraints and restrictions in terms of operational safety, dynamic constraints, extreme working conditions, collaborative interference, etc., in order to ensure the coordination and safety of multiple machines working together for lifting.
[0061] 1. Boom luffing angle constraints
[0062] In the actual lifting process, the boom's luffing angle is a key factor in determining the load's outboard span, which should be neither too large nor too small. The larger the boom's luffing angle, the smaller the load's outboard span. Conversely, the smaller the boom's luffing angle, the larger the load's outboard span. Too small a boom's luffing angle will result in excessive torque caused by the load, which may increase the load on the ballast system or even cause the risk of hull imbalance. On the contrary, although too large a boom's luffing angle reduces the burden on the ballast system, it may cause interference between the load and the ship's buildings or the crane itself. Therefore, the boom's luffing range in the actual lifting process should be determined based on the characteristics of the full-revolving crane vessel:
[0063]
[0064] In the formula, θ vkmin (rad) represents the minimum threshold value of the k-th crane boom angle, θ vkmax (rad) represents the maximum threshold of the boom luffing angle allowed for crane ship No. k.
[0065] 2. Sling length constraint
[0066] The change of the vertical position of the crane load is mainly determined by the descent and rise of the sling, where the length change range of the sling depends on the length limit of the sling in the crane ship, and its change range is:
[0067]
[0068] In the formula, l kmin (m) represents the minimum extension length of the sling of crane ship No. K, l kmax (m) indicates the maximum extension length of the sling of crane ship No. K.
[0069] 3. Dynamic constraints
[0070] The offshore multi-machine lifting operation process is completed by multiple fully rotating crane ships in coordination, and the models and lifting capacities of the crane ships may be different. Therefore, ensuring that the key dynamic parameters of the crane operation process of each crane ship meet the multi-body dynamics laws of the system is the key to ensuring the coordinated operation of multiple machines. To this end, it is necessary to impose dynamic constraints based on the multi-body dynamics model of the multi-machine system according to the limit speed of the rotation, amplitude change and lifting movement of each crane under the performance constraints of the drive system itself, that is:
[0071]
[0072] In the formula, (rad / s) represents the maximum column rotation speed under the performance constraints of the crane ship K under the coordination of multiple machines. (rad / s) represents the maximum boom speed under the performance constraints of crane ship k under multi-machine coordination. (m / s) represents the maximum speed of the sling under the performance constraints of the crane ship K under the coordination of multiple machines. At the same time, in order to prevent the load from swinging sharply due to sudden changes in speed, the corresponding acceleration also needs to be limited to a reasonable range:
[0073]
[0074] In the formula, (rad / s 2 ) represents the maximum acceleration threshold of the column rotation of crane ship k under safe and stable lifting. (rad / s 2 ) represents the maximum acceleration threshold of the boom amplitude change of the column of crane ship k under safe and stable lifting. (rad / s 2 ) represents the maximum acceleration threshold of the lifting and lowering of the column sling of crane ship k under safe and stable lifting.
[0075] 4. Drive device power constraints
[0076] The maximum instantaneous power of the crane's drive device during slewing, luffing and lifting cannot exceed the rated power of the corresponding drive components:
[0077]
[0078] Where P k1 (kW), P k2 (kW) and P k3 (kW) represents the maximum instantaneous power consumed by the driving device of crane No. k during rotation, luffing and lifting and lowering. k1max (kW), P k2max (kW) and P k3max (kW) represents the rated power of the driving device of crane No. K during rotation, luffing and lifting and lowering movements.
[0079] 5. Trajectory range constraints
[0080] In order to make the multi-machine collaborative lifting trajectory within the range of the two cranes, the trajectory coordinates need to be constrained, which can be simplified to the distance between the boom vertex coordinates and the slewing platform vertex coordinates and the boom length, that is:
[0081]
[0082] Among them, D k (m) represents the distance between the coordinates of the top of the crane arm of crane ship No. k and the center of the slewing platform, L k (m) represents the length of the boom of crane ship No. k. Where:
[0083]
[0084] In summary, all the inequality constraints G and equality constraints H in the trajectory planning optimization model of multi-machine collaborative lifting of a fully revolving crane ship are summarized as follows:
[0085]
[0086]
[0087] In the whole multi-machine collaborative lifting process, the minimum lifting energy consumption is the most critical indicator, which can directly reflect the efficiency and energy saving of the optimal trajectory. Therefore, under the premise of ensuring the safety of the multi-machine collaborative lifting operation of the full-revolving crane ship, minimizing the operation energy consumption is the optimization goal of the trajectory planning optimization model of the multi-machine collaborative lifting of the full-revolving crane ship established by the present invention.
[0088] Specifically, the energy consumption E (kJ) of the hoisting operation of the lifting system is composed of two parts: time cost and actuator operating power, namely:
[0089]
[0090] Where M k1 (N·m), M k2 (N·m) and F k (N) respectively represent the driving torque of the base rotation of the k-th crane, the driving torque of the boom luffing motion and the lifting force during the lifting process of the sling. Δt (min) represents the equal time interval after the hoisting operation time domain is discretized:
[0091]
[0092] In the formula, λ represents the number of discretized time domains.
[0093] Through the above design variables, objective functions and constraints, the trajectory planning optimization model of multi-ship collaborative lifting of full-revolving crane vessels can be established as follows:
[0094] find x i =[b j6 ,α1,α2,…,α k ,t f ] (j=x,y,z,1,2,…,k)
[0095]
[0096]
[0097] The following is a further explanation of the method and effect of multi-machine lifting trajectory planning for a fully revolving crane vessel of the present application through a specific application example.
[0098] This example will evaluate the feasibility of multi-machine hoisting trajectory planning for a fully revolving crane ship based on PTP theory through a practical application case. Two identical crane ships are selected as the research objects, and a coordinate system is established with crane ship No. 1 as the center, such as Figure 3 As shown. The coordinates of the rotation center of the column of crane No. 2 are (63, 0, 0), and the lifting weight is 800t. The starting angle of the boom is 60°, and the initial length of the sling is 15m. The starting position is set to (24, -32, 65), and the target coordinates are set to (43, 38, 65). The height of the weight H m is 4m, the length of the weight is L m The width of the object is 10m. m is 4m.
[0099] The simplified schematic diagram of the crane vessel is as follows: Figure 4 Specifically, the crane includes a base that performs slewing motion, an adjustable boom that controls the span size, and a sling responsible for lifting operations. In addition, the key structural and performance parameters of the crane vessel are also listed in Table 2.
[0100] Table 2 Key structural and performance parameters of the crane vessel
[0101]
[0102] The present invention selects genetic algorithm to solve the trajectory planning model of multi-machine collaborative lifting of full-revolving crane ship. The relevant parameters are set as follows: population size, iteration number, crossover rate and mutation rate are set to 100, 50, 0.8 and 0.01 respectively. The computer configuration is as follows: equipped with Windows 10 operating system, equipped with 2.20GHz Intel(R) Core(TM) i7-8750H processor and 16.0GB memory.
[0103] Figure 5 The lifting range within which two crane vessels can perform lifting operations simultaneously when the distance between them is 63m is demonstrated. Figure 6 The optimal lifting trajectory of the multi-machine collaborative lifting operation of the fully revolving crane ship obtained by the method proposed in the present invention is shown. Given the starting and target positions of the lifting, the optimal lifting trajectory planned by the present invention is smooth, continuous and has no mutation characteristics, which is conducive to ensuring the stability of the multi-machine collaborative lifting operation. Figure 7 The optimal control curve of the No. 1 fully revolving crane ship is shown. Figure 8 The optimal control curve of the No. 2 fully revolving crane ship crane is shown. As can be seen from the figure, the optimal control curve obtained by the trajectory planning method proposed in the present invention is smooth and has no mutations, which ensures that the operation process of the crane is stable and the corresponding control speed is also within a reasonable range.
[0104] Table 3 lists the design variable optimization results obtained in the solution process of the multi-machine collaborative lifting trajectory planning method based on PTP theory, which helps to better understand the performance of this method in solving. In summary, in the given application case, the trajectory planning method proposed in the present invention successfully formulated the optimal trajectory control scheme for multi-machine collaborative lifting of a full-revolving crane ship. The operation process is smooth and continuous with high coordination and meets safety requirements, which fully proves the feasibility of this method in solving.
[0105] Table 3 Optimization results of PTP method
[0106]
[0107] Table 4 shows the optimization results of the optimal trajectory solved by the method proposed in the present invention after the lifting starting posture, starting position and target position are given. The total energy consumption of the two cranes in the collaborative lifting operation is 131.02 (10 4kJ), the hoisting operation time is 17.39 (min), and the decision time is only 0.61 (min), which shows that after the initial conditions are determined, the method proposed in the present invention can respond quickly.
[0108] Table 4 Optimization results of trajectory planning for multi-machine collaborative lifting of fully revolving crane vessels
[0109]
[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for planning the trajectory of multi-machine collaborative lifting of a fully revolving crane vessel, characterized in that: include: S1. Obtaining the information of the multi-machine collaborative lifting task of the full-revolving crane ship, including the position information at the beginning and end of the lifting, the target load information and the parameters of the crane ship; S2. Establish a dynamics model of the multi-machine system of a fully revolving crane ship, including establishing a dynamics model of each crane ship crane separately; S3. Based on the PTP theory, a trajectory planning model for the coordinated lifting of multiple machines on a fully revolving crane ship is established and solved to obtain an intermediate model of the trajectory for the coordinated lifting of multiple machines on a fully revolving crane ship; S4. Based on the intermediate model of the trajectory of the multi-machine collaborative lifting operation of the full-revolving crane ship, the boom amplitude angle constraint, the sling length constraint, the dynamic constraint, the drive device power constraint and the trajectory range constraint are considered during the multi-machine collaborative lifting operation of the full-revolving crane ship, and the optimal dynamic control parameter time history process corresponding to each crane is obtained by inverse solution; S5: If the solution converges, the optimal lifting trajectory solution is output; otherwise, S3 is executed to update the trajectory design variables and continue to perform iterative solution.
2. The method for planning the trajectory of multi-machine collaborative lifting of a fully revolving crane vessel according to claim 1 is characterized in that: Establish the dynamic model of the multi-machine system of the fully revolving crane vessel, including: Determine and select any first crane from among the cranes, and establish a coordinate system based on the first crane, including setting the intersection of the rotation centerline of the first crane ship column and the deck plane as the coordinate origin O, the rotation axis of the crane ship column as the z-axis, the intersection of the plane passing through the z-axis and perpendicular to the centerline plane of the crane ship and the deck surface as the x-axis, and the intersection of the deck surface of the first crane ship and the centerline plane as the y-axis.
3. The method for planning the trajectory of multi-machine collaborative lifting of a fully revolving crane vessel according to claim 1 is characterized in that: Based on the PTP theory, a multi-machine collaborative lifting trajectory planning model for a fully revolving crane vessel is established, including: Obtain the constraints of the task from beginning to end according to the dynamics model of the multi-machine system of the fully revolving crane ship, and establish a polynomial according to the number of constraints, wherein the polynomial is used to describe the collaborative lifting trajectory of the multi-machine system of the fully revolving crane ship; The known boundary conditions are introduced into the polynomial to determine the trajectory planning design variables in the multi-machine collaborative lifting trajectory planning.
4. The method for planning the trajectory of multi-machine collaborative lifting of a fully revolving crane vessel according to claim 2 is characterized in that: Based on the multi-machine collaborative lifting trajectory, the following formula is used to inversely calculate the base rotation angle and boom luffing angle of each crane: Where k represents the number of cranes, θ r1 (rad), θ r2 (rad),…,θ rk (rad) is the rotation angle of each crane base, θ v1 (rad), θ v2 (rad),…,θ vk (rad) is the boom luffing angle of each crane ship, where 1, 2, 3, ..., k is the number of the full-revolving crane ship, d x (m), d y (m), d z (m) are the displacements of the lifting trajectory in the x, y, and z directions in the coordinate system, N xk (m) and N yk (m) is the deviation between the fixed position of the sling of crane ship k and the center of gravity of the hoisted cargo in the x and y directions; D xk With D yk are the x and y coordinates of the rotation center of crane ship No. k; H m (m) is the height of the hoisted cargo; l k (m) is the length of the sling of crane ship No. k; L k (m) is the boom length of crane k; h k (m) is the height of the slewing platform of crane vessel No. K.
5. The method for planning the trajectory of multi-machine collaborative lifting of a fully revolving crane vessel according to claim 1 is characterized in that: The boom luffing angle constraint is defined as: Where k represents the number of cranes, θ vkmin (rad) represents the minimum threshold value of the k-th crane boom angle, θ vkmax (rad) represents the maximum threshold of the k-th crane boom angle, θ vk (rad) represents the boom luffing angle of crane ship No. K.
6. The method for planning the trajectory of multi-machine collaborative lifting of a fully revolving crane vessel according to claim 1 is characterized in that: The sling length constraint is defined as: Among them, l kmin (m) represents the minimum extension length of the sling of crane ship No. K, l kmax (m) represents the maximum extension length of the sling of crane ship No. k, l k (m) represents the extended length of the sling of crane ship No. K.
7. The method for planning the trajectory of multi-machine collaborative lifting of a fully revolving crane vessel according to claim 1 is characterized in that: The dynamic constraints are defined as: in, It represents the maximum speed of the column rotation under the performance constraints of the crane ship K under the coordination of multiple machines. It represents the maximum boom speed under the performance constraints of crane ship k under multi-machine coordination. It represents the maximum speed of the sling under the performance constraints of the crane ship K under the coordination of multiple machines. It indicates the rotation speed of the column of crane ship No. K under the coordination of multiple machines. (rad / s) represents the boom luffing speed of crane ship k under multi-machine coordination. It indicates the maximum speed of the hoisting cable of crane ship No. k under multi-machine coordination; in, It represents the maximum acceleration threshold of the column rotation under the performance constraints of the crane ship k under safe and stable lifting. It represents the maximum acceleration threshold of the boom amplitude change under the performance constraints of the crane ship k under safe and stable lifting. It represents the maximum acceleration threshold of the column sling lifting and lowering under the performance constraints of the crane ship K under safe and stable lifting. (rad / s 2 ) represents the rotation acceleration of the slewing platform of crane ship No. k under multi-machine coordination, represents the acceleration of the boom of crane ship k under multi-machine coordination, It represents the extension and retraction acceleration of the lifting cable of crane ship No. K under the coordination of multiple machines.
8. The method for planning the trajectory of multi-machine collaborative lifting of a fully revolving crane vessel according to claim 1 is characterized in that: The drive power constraint is set as: Among them, P k1 (kW), P k2 (kW) and P k3 (kW) represents the maximum instantaneous power consumed by the driving device of crane k during rotation, luffing and lifting and lowering movements, P k1max (kW), P k2max (kW) and P k3max (kW) represents the rated power of the driving device of crane No. K during rotation, luffing and lifting and lowering movements.
9. The method for planning the trajectory of multi-machine collaborative lifting of a fully revolving crane vessel according to claim 1, characterized in that: The trajectory range constraints are set as: Among them, D k (m) represents the distance between the coordinates of the top of the crane arm of crane ship No. k and the center of the slewing platform, L k (m) represents the boom length of crane ship No. K.