A shore crane global trajectory planning method based on minimum jerk

Abstract

The present application relates to a kind of shore bridge global trajectory planning method based on Minimum jerk, belong to port automation technical field, including the following steps: utilize Lagrange equation to construct the dynamics model of multi-rope shore bridge system;Through trapezoidal time allocation strategy, n piece polynomial curve through n+1 trajectory point is allocated initial time;Based on Minimum jerk method, the parameter of n piece polynomial curve that meets time allocation is planned;If the trajectory section after optimization intersects with obstacle, by adding trajectory point, polynomial curve is re-optimized, and the safe trajectory without collision is generated.Compared with prior art, the present application can dynamically segment and optimize trajectory, which not only adapts to the dynamic characteristics of multi-rope shore bridge system, but also effectively avoids obstacles, ensures to generate efficient and safe planning trajectory quickly in the environment full of obstacles.

Description

Technical Field

[0001] This invention relates to the field of port automation technology, and in particular to a global trajectory planning method for quay cranes based on Minimum Jerk. Background Technology

[0002] In automated port operations, container loading and unloading using quay cranes is a high-frequency, high-difficulty, and highly mechanized task. The main objective is to move containers to their target location quickly and accurately, minimizing sway. Currently, most crane systems in practical applications rely on manual operation, but this method is inefficient, has poor sway reduction effects, a high rate of misoperation, low safety, and a high accident rate. Therefore, with the development of automatic control technology, automatic sway reduction control has become one of the research focuses both domestically and internationally, and is also one of the key technologies for intelligent ports.

[0003] However, the control degrees of freedom of a crane system are lower than those of the controlled object, making it a typical underactuated system, and it is susceptible to external disturbances such as friction and wind. The strong coupling and significant nonlinearity between the states of the crane system increase the difficulty of designing anti-sway control methods. Currently, several trajectory planning methods have effectively utilized the dynamic coupling effect of underactuated systems to adjust the motion state and successfully reduce sway. However, fast and smooth global trajectory planning and obstacle avoidance techniques for quay cranes currently lack sufficient research and attention. Summary of the Invention

[0004] The purpose of this invention is to provide a global trajectory planning method for quay cranes based on Minimum Jerk, which takes minimizing jerk and tracking error as optimization objectives. By dynamically segmenting and adjusting the trajectory, it can adapt to the motion characteristics of the quay crane lifting system and avoid obstacles, thereby achieving safe and efficient load transportation.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A global trajectory planning method for quay cranes based on Minimum Jerk includes the following steps:

[0007] S1. A dynamic model of the multi-rope quay crane lifting system is constructed using the Lagrange equation.

[0008] S2, using a trapezoidal time allocation strategy, allocates initial time to n segments of polynomial curves passing through n+1 trajectory points;

[0009] S3, based on the Minimum jerk method, plans the parameters of n polynomial curves that conform to the time allocation;

[0010] S4. If the optimized trajectory segment intersects with an obstacle, the polynomial curve is further optimized by adding trajectory points to generate a collision-free safe trajectory.

[0011] The multi-rope quay crane lifting system includes multiple trolleys, main beams, wire ropes, spreaders, and containers. Each trolley moves horizontally on the main beam and is connected to the spreader via a wire rope, which controls the movement of the container.

[0012] In step S1, the following assumptions are made regarding the multi-rope quay crane lifting system:

[0013] 1) Treat the spreader and container as a single effective load, assuming that the mass distribution of the effective load is uniform, and that the effective load will not rotate when the trolley is running on the main beam track;

[0014] 2) Ignore the flexibility and elasticity of the wire rope and treat it as a rigid body with negligible mass; assume that the wire ropes suspended on the same side are located on the same plane and are uniformly distributed.

[0015] Step S1 specifically involves: constructing a dynamic model of the multi-rope quay crane lifting system based on the Lagrange equations, and representing it in matrix form:

[0016]

[0017] Where: q=[x,l,θ1] T Let M(q) = N represent the system state variables. T M1N represents the inertia matrix. Let G(q) = N represent the Coriolis matrix. T G1 represents the gravity matrix, u = N T u1 represents the driving force matrix, and its specific form is as follows:

[0018]

[0019] Among them, S i =sinθ i C i =cosθ i S i+j =sin(θ) i +θ j ), C i+j =cos(θ) i +θ j ), S i-j =sin(θ) i -θ j ), C i-j =cos(θ) i -θ j), i,j=1,2,3, θ1, θ2 are the angles between the two wire ropes and the Y-axis, θ3 is the angle between the load and the Y-axis; m t It's the quality of the trolley, m p Here, g is the mass of the load, x is the acceleration due to gravity, x is the displacement of the trolley in the X-axis direction, and l is the length of the wire rope; the spacing of the wire rope on the trolley is 2a, the spacing of the wire rope on the lifting device is 2c (a > c), and b is the distance between the center of gravity of the load and the center of suspension; f x For the trolley's driving force, f l For the tension of the steel wire rope, Let be the first and second partial derivatives of the function g(l,θ1) with respect to θ1, respectively. These are the first-order and second-order partial derivatives of the function h(l,θ1) with respect to θ1, respectively.

[0020] For a multi-rope quay crane lifting system, only one variable among θ1, θ2, and θ3 is independent. To establish the relationship between them, assume θ2 = g(l, θ1) and θ3 = h(l, θ1), and differentiate them to obtain: Let h(l,θ1) be the first-order partial derivative of the function with respect to l.

[0021] In step S2, a trapezoidal allocation method is used for the initial time allocation, that is, accelerating from 0 to the maximum speed v with a constant acceleration a. max , keep v max After a period of time, it decelerates to 0 at a constant deceleration of -a.

[0022] In step S3, a fifth-order polynomial is used to fit the trajectory:

[0023] p(t) = k0 + k1t + k2t 2 +k3t 3 +k4t 4 +k5t 5

[0024] Where k i Let i∈[0,5] represent the parameters of the polynomial; define the parameter vector p=[k0,k1,k2,k3,k4,k5] T Thus, the polynomial can be expressed in vector form as: p(t) = [1, t, t] 2 ,t 3 ,t 4 ,t 5 ]·p;

[0025] The trajectory is divided into multiple time periods, and the motion within each time period is described by a polynomial:

[0026]

[0027] Where n represents the number of trajectory segments, p i =[k i0 ,k i1 ,k i2 ,k i3 ,k i4 ,k i5 ] T , i∈[1,n].

[0028] In step S3, an appropriate cost function is set and solved to obtain the ideal polynomial parameters, wherein the cost function is defined as follows:

[0029]

[0030] Where λ is the weight of the guiding term, p d For the desired path;

[0031] The constraints in the solution process are set as follows:

[0032]

[0033]

[0034] p i (t i ) = p i+1 (t i ),

[0035] p min ≤p(t i )≤p max ,

[0036] Where, p i (t i ) represents the polynomial of the i-th segment in t i The position of time, p i+1 (t i ) represents the polynomial of the (i+1)th segment in t i The position of time, p min p max v min v max a min a max Let p(t) represent the upper and lower limits of position, velocity, and acceleration, respectively. i ) indicates the trajectory at t i The position at that moment;

[0037] Furthermore, to optimize hardware constraints, a rectangular feasible path is defined for each trajectory point, and inequality constraints regarding position, velocity, and acceleration are applied through the feasible path:

[0038] p(t i )-r1≤p(t i )≤p(t i )+r1

[0039]

[0040] The side lengths of the feasible rectangular channels for position, velocity, and acceleration are 2r1, 2r2, and 2r3, respectively.

[0041] In step S3, the optimization problem containing equality and inequality constraints is modeled as a quadratic programming problem, as follows:

[0042] minf(p)

[0043] stA eq p = b eq

[0044] A ieq p = b ieq

[0045] Where, matrix A eq and b eq The equality constraints for each trajectory point are encoded in matrix A. ieq and b ieq The inequality constraints for each trajectory point are encoded.

[0046] Since the motion of the quay crane lifting system in the X and Y coordinate directions is independent, trajectory fitting is performed on each of them, and the two one-dimensional trajectories are directly merged to form a complete spatial trajectory.

[0047] Step S4 specifically involves:

[0048] During trajectory optimization, if a collision is found between a trajectory segment and an obstacle, an additional point is inserted between the two endpoints of that trajectory segment, and the trajectory is divided accordingly. The coordinates of the two endpoints are set to (x, y, y) and (x, y, y). k ,y k ) and (x k+1 ,y k+1 The coordinates of the midpoint are set to ((x) k +x k+1 ) / 2,min(y k ,y k+1Based on the working mode of the dock crane, the midpoint must be collision-free; the trajectory is optimized by adding a midpoint as an additional point in the trajectory segment; this step is repeated until a safe and collision-free trajectory is constructed.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] I. This invention employs the Minimum Jerk method to design a global trajectory planner. This method describes the motion trajectory using a polynomial function and adjusts the polynomial coefficients to meet the constraints of the starting and ending points of the motion, as well as position, velocity, and acceleration, thereby generating a smooth and natural motion trajectory. This approach can effectively reduce vibration and flutter during the execution of tasks by the quay crane lifting system, improving the efficiency and stability of load transportation.

[0051] II. To better align with the ideal operating mode of the quay crane lifting system, this invention divides the trajectory into multiple stages, with each stage represented by a fifth-order polynomial. For time allocation, a trapezoidal allocation method is employed to adapt to actual requirements. Simultaneously, minimizing jerk and tracking error is taken as the optimization objective. To more effectively implement hardware constraints, a rectangular feasible channel is set for each trajectory point, introducing inequality constraints on position, velocity, and acceleration.

[0052] Third, considering the complexity of the surrounding environment and the potential for large container stacks on ship decks in real-world applications, integrating obstacle avoidance capabilities into the trajectory planning process is crucial. This invention, after optimizing the trajectory, if a specific trajectory segment intersects with an obstacle, adds an additional trajectory point between the two endpoints, dividing the segment in two. The polynomial is then re-optimized using this additional trajectory point, and this process is repeated until a safe, collision-free trajectory is obtained, achieving efficient obstacle avoidance. Attached Figure Description

[0053] Figure 1 This is a flowchart of the method of the present invention;

[0054] Figure 2 This is a schematic diagram of the dynamic model of the quay bridge of the present invention;

[0055] Figure 3 This is a schematic diagram illustrating the obstacle avoidance behavior of the quay crane trajectory planner of the present invention;

[0056] The attached diagram is labeled as follows: 1. Trolley; 2. Main beam; 3. Wire rope; 4. Lifting device; 5. Container; 6. Track point; 7. Additional track point; 8. Effective track; 9. Obstacle. Detailed Implementation

[0057] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0058] This embodiment provides a global trajectory planning method for quay cranes based on Minimum Jerk, such as... Figure 1 As shown, it includes the following steps:

[0059] S1. A dynamic model of the multi-rope quay crane lifting system is constructed using the Lagrange equation.

[0060] S2, using a trapezoidal time allocation strategy, allocates initial time to n segments of polynomial curves passing through n+1 trajectory points;

[0061] S3, based on the Minimum jerk method, plans the parameters of n polynomial curves that conform to the time allocation;

[0062] S4. If the optimized trajectory segment intersects with an obstacle, the polynomial curve is further optimized by adding trajectory points to generate a collision-free safe trajectory.

[0063] The multi-rope quay crane lifting system includes multiple trolleys 1, main beams 2, wire ropes 3, spreaders 4, and containers 5. Each trolley 1 moves horizontally on the main beam 2 and is connected to the spreader 4 via a wire rope 3. The spreader 4 controls the movement of the container 5.

[0064] In this invention, the following assumptions are made regarding quay cranes and containers:

[0065] 1) Treat the spreader and container as a single payload. Assume the payload has a uniform mass distribution and that the payload will not rotate when the trolley is running on the track.

[0066] 2) Ignore the flexibility and elasticity of the wire rope and treat it as a rigid body with negligible mass. Assume that the wire ropes suspended on the same side are located on the same plane and are uniformly distributed.

[0067] The design concept of this invention is as follows: A dynamic model of the multi-rope quay crane lifting system is constructed using the Lagrange equation, and a trapezoidal time allocation strategy is employed to design and plan the trajectory using multiple polynomial curves. The minimum jerk method is applied to calculate the parameters of each polynomial curve segment, thereby achieving global trajectory planning. If a collision is detected in the planned trajectory, the system selects a new intermediate point between two safe trajectory points and repeats this optimization process until a collision-free safe trajectory is formed.

[0068] 1. Establish a dynamic model of the multi-rope quay crane lifting system.

[0069] like Figure 2 As shown, by applying the Lagrange equations, the dynamic equations of the multi-rope quay crane lifting system are derived and expressed in matrix form:

[0070]

[0071] Where: q=[x,l,θ1] T Let M(q) = N represent the system state variables. T M1N represents the inertia matrix. Let G(q) = N represent the Coriolis matrix. T G1 represents the gravity matrix, u = N T u1 represents the driving force matrix. These matrices have the following specific forms:

[0072]

[0073] To make the description more concise and clear, the following symbols are used for simplified expression: S1 = sinθ1, C1 = cosθ1, S 1+2 =sin(θ1+θ2), C 1+2 =cos(θ1+θ2), S 1-2 =sin(θ1-θ2), C 1-2 =cos(θ1-θ2). And so on. Where, m t It's the quality of the trolley, m p ρ is the mass of the load, g is the acceleration due to gravity. x is the displacement of the trolley along the X-axis, l is the length of the wire rope, θ1 and θ2 are the angles between the wire ropes on both sides and the Y-axis, and θ3 is the angle between the load and the Y-axis. The spacing of the wire ropes on the trolley is 2a, and the spacing of the wire ropes on the lifting device is 2c (a > c). b is the distance between the center of gravity of the load and the center of suspension. f x For the trolley's driving force, f l For the tension of the steel wire rope, Let be the first and second partial derivatives of the function g(l,θ1) with respect to θ1, respectively. These are the first-order and second-order partial derivatives of the function h(l,θ1) with respect to θ1, respectively.

[0074] For a multi-rope quay crane lifting system, only one variable among θ1, θ2, and θ3 is independent. To establish the relationship between them, assume θ2 = g(l, θ1) and θ3 = h(l, θ1). Differentiating them, we get: Let h(l,θ1) be the first-order partial derivative of the function with respect to l.

[0075] At this point, the model derivation of the quay crane lifting system is complete. The above equations will be used in the subsequent trajectory planning system based on the Minimum jerk method.

[0076] 2. Design a trajectory planning system.

[0077] The previous section completed the mathematical model construction of the quay crane lifting system. In common quay crane operation scenarios, only information about the initial and target states is available. Therefore, trajectory planning becomes the primary task. This section, based on the mathematical model of the quay crane lifting system, extends the Minimum Jerk method into a trajectory planning algorithm and designs an obstacle avoidance strategy. The design details of the trajectory planner will be described in detail below.

[0078] As shown in equation (1), the dynamic equations of the multi-rope quay crane lifting system involve three-order state variables: position, velocity, and acceleration. To construct a continuous acceleration trajectory, this embodiment uses a fifth-order polynomial for trajectory fitting:

[0079] p(t) = k0 + k1t + k2t 2 +k3t 3 +k4t 4 +k5t 5 (2)

[0080] Where k i Let i ∈ [0, 5] represent the parameters of the polynomial. Define the parameter vector p = [k0, k1, k2, k3, k4, k5]. T Thus, the polynomial can be expressed in vector form as: p(t) = [1, t, t] 2 ,t 3 ,t 4 ,t 5 Considering that the quay crane trajectory involves the horizontal movement of the trolley and the load, as well as the vertical movement of the load, using a single polynomial to represent the entire trajectory would be overly simplistic. Therefore, this embodiment divides the trajectory into multiple time periods, and the motion within each time period is described by a polynomial.

[0081]

[0082] Where n represents the number of trajectory segments, p i =[k i0 ,k i1 ,k i2 ,k i3 ,k i4 ,k i5 ] T, i∈[1,n]. For the trajectory represented by the above multi-segment polynomial, there are two objectives to be optimized. First, optimize the time allocation method for each stage, which directly affects the quality of the planning results. Referring to the ideal operating mode of the quay crane lifting system, this embodiment adopts a trapezoidal allocation method for the initial time allocation, that is, accelerating from 0 to the maximum speed v with a constant acceleration a. max , keep v max After a period of time, the system decelerates to 0 at a constant deceleration of -a. Next, the solution method for the polynomial parameters of each segment is optimized. By setting an appropriate cost function and solving for it, the ideal polynomial parameters are obtained. To enable the system to quickly and smoothly plan the reference signal, this embodiment defines the cost function as follows:

[0083]

[0084] Where, p d The desired path is given by equation (4). The first term in equation (4) is the acceleration minimization term, which aims to ensure a smooth generated trajectory and avoid thrust discontinuities. The second term is the planned trajectory and the desired path p. d The guiding term (t) ensures that the planned trajectory more closely follows the desired path. Here, λ is the weight of the guiding term, used to balance the smoothness of the trajectory with accurate tracking along the desired path. The larger the weight, the higher the degree of conformity between the trajectory and the desired path.

[0085] When optimizing the trajectory, it is also necessary to consider satisfying a series of constraints, such as setting the position, speed and acceleration of the starting and ending points.

[0086]

[0087] To ensure the smooth operation of the quay crane lifting system, it is necessary to guarantee that the state of intermediate trajectory points at the junctions of two trajectory segments achieves third-order continuity. Therefore, the following constraints are set to meet the requirements of the trajectory design.

[0088]

[0089] Where, p i (t i ) represents the polynomial of the i-th segment in t i The position of time, p i+1 (t i ) represents the polynomial of the (i+1)th segment in t i The position at any given moment. Finally, given the constantly changing state of the intermediate point, such as its position, velocity, and acceleration, practical hardware constraints are essential during the planning process to address this uncertainty. Otherwise, it may lead to unstable tracking performance or the inability to complete the tracking task within the specified time.

[0090]

[0091] Where, p min p max v min v max a min a max These represent the upper and lower limits of position, velocity, and acceleration, respectively. p(t) i ) indicates the trajectory at t i Position at any given moment. To optimize hardware constraints, this embodiment defines a rectangular feasible channel for each trajectory point and applies inequality constraints regarding position, velocity, and acceleration through these feasible channels. While adhering to these hardware constraints, the midpoint of the trajectory can dynamically move within the feasible channel, thereby adjusting the time allocation of adjacent polynomial segments to optimize trajectory planning.

[0092]

[0093] The side lengths of the feasible rectangular channels for position, velocity, and acceleration are 2r1, 2r2, and 2r3, respectively.

[0094] Therefore, optimization problems involving equality and inequality constraints can be modeled as quadratic programming problems, as follows:

[0095]

[0096] Where, matrix A eq and b eq The equality constraints for each trajectory point are encoded in matrix A. ieq and b ieq Inequality constraints for each trajectory point were encoded. Given that the motion of the quay crane system in the X and Y coordinate directions is independent, trajectory fitting was performed separately for each. Then, these two one-dimensional trajectories were directly merged to form a complete spatial trajectory.

[0097] 3. Design obstacle avoidance behaviors.

[0098] In practical applications, given that ship decks may be densely packed with containers and face complex surrounding environments, integrating obstacle avoidance capabilities into trajectory planning becomes extremely critical. Simultaneously, for safety reasons, it is essential to ensure a safe distance between containers and obstacles to reduce the risk of approach. Therefore, establishing safe distance standards becomes a key means of preventing such risks. The obstacle avoidance strategy proposed in this embodiment includes:

[0099] During trajectory optimization, if a trajectory segment is found to conflict with an obstacle (including a safety distance), an additional point is inserted between the two endpoints of that segment, and the trajectory is split accordingly. The coordinates of the two endpoints are set to (x, y, y) respectively. k,y k ) and (x k+1 ,y k+1 The coordinates of the midpoint are set to ((x) k +x k+1 ) / 2,min(y k ,y k+1 Based on the operating mode of the dock crane, this midpoint is clearly collision-free. Using the midpoint as an additional point on the trajectory, and through optimization of these additional points and repeated steps, a safe and collision-free trajectory is constructed, as shown below. Figure 3 As shown, in a port environment, where obstacles are relatively few, adding a small number of trajectory points can effectively avoid collisions. This method not only finds a solution after a limited number of optimization iterations, but also has a very small impact on the computational complexity of each iteration.

[0100] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A Minimum jerk based global trajectory planning method for shore cranes, characterized in that, The method comprises the following steps: S1, constructing a dynamics model of the multi-rope quay crane load system by using Lagrange equation; S2, distributing initial time to n polynomial curves passing through n+1 trajectory points by using trapezoidal time allocation strategy; S3, planning parameters of the n polynomial curves meeting the time allocation based on Minimum jerk method; S4, if the optimized trajectory segment intersects with an obstacle, re-optimizing the polynomial curve by adding a trajectory point to generate a collision-free safe trajectory; In the step S1, the following assumptions are made for the multi-rope quay crane load system: 1) regarding the spreader and the container as an integral effective load, assuming that the mass distribution of the effective load is uniform and the effective load does not rotate when the trolley runs on the track of the main beam; 2) ignoring the flexibility and elasticity of the steel wire rope and regarding it as a rigid body with negligible mass, assuming that the steel wire ropes on the same side are located on the same plane and are uniformly distributed; Record , is the angle between the two sides of the wire rope and the Y-axis direction, is the angle between the load and the Y-axis direction, is the length of the wire rope, , , In the above equations, only one variable is independent; in order to establish the relationship between them, it is assumed that and ; In the step S3, a quintic polynomial is used for trajectory fitting: wherein , denote parameters of the polynomial; define a parameter vector In this way the polynomial is expressed in vector form as: ; The trajectory is divided into multiple time segments, and the motion in each time segment is described by a polynomial: wherein denotes the number of trajectory segments, , ; In the step S3, an appropriate cost function is set and solved to obtain ideal polynomial parameters, wherein the cost function is defined as follows: wherein, is the weight of the guide item, is the expected path; The constraint conditions in the solving process are set as follows: ， ， ， ， ， ， ， ， wherein, represents the position of the th polynomial at the th time point, represents the position of the th polynomial at the th time point, , , , , , respectively represent upper and lower limits of the position, velocity, and acceleration, represents the position of the trajectory at the th time point; In addition, in order to optimize the hardware constraints, a rectangular feasible channel is set for each trajectory point, and inequality constraints on position, velocity and acceleration are imposed through the feasible channel: Wherein, the length of the position, velocity, acceleration rectangle feasible channel is respectively , , .

2. The Minimum jerk-based global trajectory planning method for a shore-based crane according to claim 1, wherein, The multi-rope quay crane load system comprises multiple trolleys, a main beam, steel wire ropes, a spreader and a container, wherein each trolley moves horizontally on the main beam, is connected with the spreader through a steel wire rope, and controls the movement of the container through the spreader.

3. The Minimum jerk-based global trajectory planning method for a shore-to-ship container crane according to claim 1, wherein, The step S1 is specifically: constructing a dynamics model of the multi-rope quay crane load system based on Lagrange equation and representing it in matrix form: wherein: represents a system state variable, represents an inertia matrix, represents a Coriolis matrix, represents a gravity matrix, represents a driving force matrix, which has the following specific form: ， ， ， ， wherein, , , , , , , i , j = 1, 2, 3, , is the angle between the two sides of the wire rope and the Y-axis direction, is the angle between the load and the Y-axis direction; is the mass of the trolley, is the mass of the load, is the acceleration of gravity, is the displacement of the trolley in the X-axis direction, is the length of the wire rope; the distance between the wire rope on the trolley is the distance between the wire rope on the trolley , , , is the distance between the center of gravity of the load and the suspension center; is the driving force of the trolley, is the tension of the wire rope, , are the first-order partial derivative and the second-order partial derivative of the function with respect to , respectively, , are the first-order partial derivative and the second-order partial derivative of the function with respect to , respectively.

4. The Minimum jerk-based global trajectory planning method for a shore-to-ship container crane according to claim 3, wherein, For multi-rope shore-to-ship crane loading systems, , , In the case of a single variable, only one variable is independent; to establish a relationship between them, assume and , differentiate to get , , , , The first-order partial derivative of the function with respect to l , , .

5. The Minimum jerk-based global trajectory planning method for a shore-based crane according to claim 1, wherein, In said step S2, the initial time allocation is performed using a trapezoidal distribution, i.e. with a constant acceleration from 0 to maximum speed , maintaining for a certain time and then with a constant deceleration decelerating to 0.

6. The Minimum jerk-based global trajectory planning method for a shore-based crane according to claim 1, wherein, In the step S3, the optimization problem containing equations and inequality constraints is modeled as a quadratic programming problem, which is specifically as follows: where the matrix and encodes the equality constraints for each trajectory point, the matrix and encodes the inequality constraints for each trajectory point; Based on the fact that the motion of the multi-rope quay crane load system in X and Y coordinate directions is independent of each other, one-dimensional trajectories are fitted respectively, and two one-dimensional trajectories are directly fused to form a complete spatial trajectory.

7. The Minimum jerk-based global trajectory planning method for a shore-based crane according to claim 1, wherein, The step S4 is specifically: In the trajectory optimization process, if a trajectory segment is found to have a conflict with an obstacle, an extra point is inserted between the two endpoints of the trajectory segment and the trajectory is divided accordingly, wherein the coordinates of the two endpoints are set as and , and the coordinate of the midpoint is set as ; based on the working mode of the terminal crane, the midpoint is necessarily collision-free; the trajectory is optimized by adding the midpoint in the trajectory segment as an extra point; this step is repeatedly performed until a safe and collision-free trajectory is constructed.

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