Bridge crane obstacle avoidance trajectory planning method and system based on segmented time polynomial

Abstract

The invention provides a bridge crane obstacle avoidance trajectory planning method and system based on a segmented time polynomial, and is applied to the technical field of cranes. Path planning of a bridge crane is carried out in an environment with obstacles; and planning the track of the bridge crane through a segmented time polynomial based on the planned path. According to the invention, after path planning is carried out, a piecewise time polynomial trajectory optimization technology is adopted to generate a smoother obstacle avoidance trajectory, so that the energy loss of the system is effectively reduced, efficient and smooth trajectory planning and tracking can be realized in an obstacle environment, and the operation performance and energy utilization efficiency of the bridge crane are improved.

Description

Technical Field

[0001] The present application relates to the field of crane technology, and in particular to a method and system for planning obstacle avoidance trajectories for a bridge crane based on piecewise time polynomials. Background Art

[0002] Underactuated bridge cranes are widely used in ports, factories and warehouses. As an important means of transporting heavy objects, they still play a key role in cargo loading and unloading. Bridge cranes use a trolley to pull a wire rope to drive the load to move and transport it to a designated location. However, during the movement of the load, the load may swing due to inertia. In addition, if an obstacle is encountered during the movement, the sudden deceleration will aggravate the swing, which will not only reduce the transportation efficiency, but also pose a safety hazard. Therefore, the study of obstacle avoidance planning has important practical significance and wide application value for cranes.

[0003] However, in complex scenarios, especially when frequent acceleration or deceleration is required, the speed of the crane may change suddenly, which not only makes the load swing more violently, but also increases the load on the system, which may cause unstable operation and equipment damage.

[0004] Based on this, a new technical solution is needed. Summary of the invention

[0005] In view of this, an embodiment of the present specification provides a method and system for obstacle avoidance trajectory planning for a bridge crane based on a piecewise time polynomial.

[0006] The embodiments of this specification provide the following technical solutions:

[0007] An embodiment of the present specification provides a bridge crane obstacle avoidance trajectory planning method based on a piecewise time polynomial, comprising: performing path planning of the bridge crane in an environment where obstacles exist; and performing trajectory planning of the bridge crane based on the planned path through a piecewise time polynomial.

[0008] Preferably, a fast-expanding random tree star algorithm is used to perform path planning in an environment with obstacles; wherein, after randomly sampling points in space, the parent node with the minimum cost in the neighborhood is found by searching, and the cost needs to take into account the path length from the initial point to the target point, to generate a path from the starting point to the target point.

[0009] Preferably, a path avoiding obstacles is planned in space according to the initial position, the final position and the position of the obstacle of the target point;

[0010] Discretize the path into several path points, arrange them in the order of the motion path, and form a trajectory path point sequence;

[0011] The initial position is: P0 = (x0, y0);

[0012] Wherein, P0 represents the initial position, x0 represents the abscissa of the initial position, and y0 represents the ordinate of the initial position;

[0013] The end position is: P g =(x g ,y g );

[0014] Among them, P g Indicates the end position; x g Indicates the horizontal coordinate of the end position; y g The vertical coordinate indicating the end position;

[0015] The obstacle position is: O I =(x I ,y I ,l I ,h I )I=0,1,2,...;

[0016] Among them, O I represents the spatial position of the Ith obstacle, x I is the lower left axis horizontal coordinate of the Ith obstacle, y I Indicates the lower left axis ordinate of the Ith obstacle, l I represents the length of the first obstacle, h I Indicates the width of the first obstacle.

[0017] Preferably, when obstacles and / or target points change, the rapidly expanding random tree star algorithm is reused for path planning to update the path.

[0018] Preferably, a smooth trajectory is generated based on the path according to given constraints; wherein, according to the path planned, discrete instruction points are arranged in sequence, and the path is planned using a piecewise time polynomial to generate a smooth and low-energy trajectory for the bridge crane while avoiding collisions between the payload and obstacles.

[0019] Preferably, the obstacle avoidance trajectory from the starting point to the target point is formed by the instruction points obtained after discretization, and the trajectory is represented by an n-order polynomial:

[0020]

[0021] Among them, p(t) represents a one-dimensional trajectory polynomial, t represents the time parameter, p0, p1, ..., p n is n+1 trajectory parameters, p i represents the i-th trajectory parameter, i∈[0,n], and the parameter vector p=[p0,p1,...,pn ] T ; [p0,p1,...,p n ] T represents [p0,p1,...,p n ] is the transpose of ; then a trajectory is in the form of:

[0022] p(t)=[1,t,t 2 ,...,t n ]·p;

[0023] If the polynomial trajectory has a total of m segments, then the kth segment trajectory p k (t) is expressed as:

[0024] p k (t)=[1,t,t 2 ,...,t n ]·[p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] T ;

[0025] Among them, p ki represents the i-th parameter of the k-th trajectory, let the parameter vector p k =[p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] T ; [p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] T indicates [p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] is the transpose of ;

[0026] For the trajectory, set constraints according to requirements, and optimize the modeling constraints as follows:

[0027]

[0028] Among them, minf(p) means minimizing the cost function; st means being constrained; f(p) is the cost function; A eq is the matrix of dynamic constraints; p d is the polynomial coefficient vector to be optimized; beq is the vector of constraint boundary conditions;

[0029]

[0030] Wherein, T represents the termination time;

[0031] p d =[p1,p2,...,p k ,...,p m ];

[0032] b eq =[p 0,1 ,v 0,1 ,a 0,1 ,p T1,1 ,v T1,1 ,a T1,1 ,p 0,2 ,...,p 0,j ,v 0,j ,a 0,j ,p Tj,j ,v Tj,j ,a Tj,j ,...];

[0033] Among them, Tj represents the end time of the jth trajectory, p 0,j represents the starting position of the jth trajectory in the constraint boundary condition, v 0,j represents the starting velocity of the jth trajectory in the constraint boundary condition, a 0,j represents the starting acceleration of the jth trajectory in the constraint boundary condition, p Tj,j represents the end position of the jth trajectory in the constraint boundary condition, v Tj,j represents the terminal velocity of the jth trajectory in the constraint boundary condition, a Tj,j represents the terminal acceleration of the jth trajectory in the constraint boundary condition;

[0034]

[0035] Among them, t 0,j represents the initial time of the jth trajectory in the dynamic constraint, t Tj,j represents the termination time of the jth trajectory in the dynamic constraint; p(t 0,j ) represents the starting position of the jth trajectory in the dynamic constraint, v(t 0,j ) represents the starting velocity of the jth trajectory in the dynamic constraint, a(t 0,j ) represents the initial acceleration of the jth trajectory in the dynamic constraint, p(t Tj,j ) represents the end position of the jth trajectory in the dynamic constraint, v(t Tj,j) represents the terminal velocity of the jth trajectory in the dynamic constraint, a(t Tj,j ) represents the terminal acceleration of the jth trajectory in the dynamic constraint;

[0036] f(p), A eq and b eq The optimizer is used to solve the trajectory parameters and obtain the desired trajectory from the starting point to the end point.

[0037] Preferably, the given constraints include: starting point position, end point position, speed, acceleration, desired path points, maximum speed, maximum acceleration, and desired smoothness of trajectory connections.

[0038] Preferably, preset time constraints are incorporated in the trajectory planning process to optimize the smoothness and dynamic performance of the trajectory and to make the generated trajectory meet the time requirements.

[0039] Preferably, time parameterization is introduced into the trajectory planning process; in the trajectory optimization stage, the desired time function T(t) is set and a correction term for time preset control is introduced:

[0040] T(t)=T0+△T(t);

[0041] Among them, T0 is the initial time setting, △T(t) is the incremental function based on time change, which meets the constraints of time change;

[0042] Add a correction term for time preset control to the trajectory optimization objective function:

[0043] minf(p)+λ·e T (t);

[0044] Among them, e T (t) = T(t) - T0, e T (t) represents the time preset error function, T(t) represents the actual completion time, T0 is the initial time setting, and λ represents the weight coefficient, which is used to balance the influence of trajectory smoothness and time preset control.

[0045] The embodiment of this specification provides a bridge crane obstacle avoidance trajectory planning system based on piecewise time polynomials, including the following modules:

[0046] Module M1: Path planning of overhead crane in an environment with obstacles;

[0047] Module M2: Trajectory planning of overhead crane based on planned paths via piecewise-time polynomials.

[0048] Compared with the prior art, the at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects:

[0049] After path planning, this application uses segmented-time polynomial trajectory optimization technology to generate a smoother obstacle avoidance trajectory, thereby effectively reducing the energy loss of the system. It can achieve efficient and smooth trajectory planning and tracking in an obstacle environment, and improve the operating performance and energy utilization efficiency of the bridge crane. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 This is a schematic diagram of the structure of the bridge crane in this application;

[0052] Figure 2 A schematic diagram of the path planning for generating path points in this application;

[0053] Figure 3 This is a schematic diagram of trajectory planning based on path points in this application;

[0054] Figure 4 This is a pseudo code diagram of the path planning process in this application. DETAILED DESCRIPTION

[0055] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0056] The following describes the implementation methods of the present application through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features in the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0057] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspect described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0058] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0059] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the examples can be practiced without these specific details.

[0060] The technical solutions provided by various embodiments of the present application are described below in conjunction with the accompanying drawings.

[0061] The embodiment of this specification provides a bridge crane obstacle avoidance trajectory planning method based on piecewise time polynomials, such as Figure 1 and Figure 2 As shown, it includes: path planning of a bridge crane in an environment with obstacles; trajectory planning of the bridge crane through a segmented time polynomial based on the planned path.

[0062] Specifically, path planning is performed in an environment with obstacles, that is, RRT* (Rapidly-exploring Random Trees Star) algorithm is used to perform path planning in an environment with obstacles; the trajectory of the bridge crane is planned based on the planned path through a piecewise time polynomial; wherein, preset time constraints are combined in the trajectory planning process.

[0063] In one embodiment, if Figure 1 and Figure 2 As shown in the figure, a fast-expanding random tree star algorithm is used to plan paths in an environment with obstacles. After randomly sampling points in space, the parent node with the minimum cost in the neighborhood is found by searching. The cost needs to consider the path length from the initial point to the target point, and a path from the starting point to the target point is generated. Figure 2The Goal in represents the target point.

[0064] In one embodiment, if Figure 1 and Figure 2 As shown in the figure, according to the initial position, terminal position and obstacle position of the target point, a path to avoid obstacles is planned in space. The path is discretized into several path points, which are arranged in the order of the motion path to form a trajectory path point sequence.

[0065] The initial position is: P0=(x0, y0); wherein P0 represents the initial position, x0 represents the abscissa of the initial position, and y0 represents the ordinate of the initial position.

[0066] The end position is: P g =(x g ,y g ), where P g Indicates the end position; x g Indicates the horizontal coordinate of the end position; y g The vertical coordinate indicating the end position.

[0067] The obstacle position is: O I =(x I ,y I ,l I ,h I )I=0,1,2,...; among them, O I represents the spatial position of the Ith obstacle, x I is the lower left axis horizontal coordinate of the Ith obstacle, y I Indicates the lower left axis ordinate of the Ith obstacle, l I represents the length of the first obstacle, h I Indicates the width of the first obstacle.

[0068] Specifically, Figure 1 In the diagram, the bridge crane can be simplified to consist of the following main structures: trolley, cable, and load. Among them, M represents the mass of the trolley, m represents the mass of the load, l represents the length of the cable, θ represents the angle between the cable and the vertical direction, x represents the displacement of the trolley, and F represents the resultant force applied to the trolley.

[0069] According to the initial position P0 of the target point and the final position P g and the position of the rectangular obstacle O I , a path is planned in two-dimensional space to avoid obstacles. The path is discretized into several path points, which are arranged in the order of the motion path to form a trajectory path point sequence.

[0070] The specific expressions of the starting position, the ending position and the obstacle position are as follows:

[0071] P0=(x0,y0) (1)

[0072] P g =(x g ,y g ) (2)

[0073] O I =(x I ,y I ,l I ,h I ) I=0,1,2,... (3)

[0074] Among them, P0 is the starting position, x0, y0 are the starting coordinates of the car, P g is the target position, x g ,y g is the final position value of the car, O I is the spatial position of the I-th obstacle, x I ,y I is the lower left axis coordinate of the I-th obstacle, l I represents the length of the first obstacle, h i Indicates the width of the first obstacle.

[0075] like Figure 2 As shown in the figure, a path with obstacle avoidance is generated. After randomly sampling a point in space, the RRT* algorithm searches to find the parent node with the lowest cost in the neighborhood (rather than directly connecting to the nearest node). The cost takes into account the length of the path from the initial point to the target point. The algorithm connects the new point to this parent node and optimizes the path. Finally, the path is ensured to be the shortest by connecting the new node and its surrounding nodes. Figure 4 As shown in the figure, the pseudo code of the process is shown. First, determine the initial point x init and the target point x goal , find the shortest path from the starting point to the target point; randomly generate a point x in space by sampling rand ; Search point x rand The nearest neighbor node x near ;x near To x rand Move a certain distance to get a new point x new ; Search for x new A nearby range to find x new Some nearby nodes, put x near 、x near 、x new Connect them all, the point with the shortest distance is x new parent node; find a path from the starting point to x newThe points with the shortest distance are reconnected. According to the above algorithm, an initial path from the starting point to the target point is generated.

[0076] In one embodiment, when obstacles and / or target points change, the rapidly expanding random tree star algorithm is reused for path planning to update the path. For example, when obstacles or target points change, the RRT* algorithm is reapplied for path planning to update the path.

[0077] In one embodiment, if Figure 3 As shown, a smooth trajectory is generated based on the path according to the given constraints; wherein, according to the path planning, the discrete instruction points are arranged in the order of the path, and the path is planned using a piecewise time polynomial to generate a smooth and low energy consumption trajectory of the bridge crane while avoiding collision between the payload and obstacles. Figure 3 Minimum Snap Trajectory Generation in, minimum jerk trajectory.

[0078] In one embodiment, if Figure 3 As shown, a smooth trajectory is generated based on the path points according to the given constraints. According to the above path planning, the discrete instruction points are arranged in the order of the path, and the trajectory planning of the updated path is performed using the piecewise time polynomial to generate a smooth and low energy consumption trajectory of the bridge crane while avoiding the collision of the payload with obstacles.

[0079] A section of obstacle avoidance trajectory from the starting point to the target point, the command points obtained after discretization will form a complex trajectory, which is generally represented by an n-order polynomial:

[0080]

[0081] Among them, p(t) represents a one-dimensional trajectory polynomial, t represents the time parameter, p0, p1, ..., p n is n+1 trajectory parameters, p i represents the i-th trajectory parameter, i∈[0,n], and the parameter vector p=[p0,p1,...,p n ] T , the matrix superscript T represents transposition; then a trajectory can be written as:

[0082] p(t)=[1,t,t 2 ,...,t n ]·p;

[0083] If the polynomial trajectory has a total of m segments, then the kth segment trajectory p k (t) can be expressed as:

[0084] pk (t)=[1,t,t 2 ,...,t n ]·[p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] T (5);

[0085] Among them, p ki represents the i-th parameter of the k-th trajectory, let the parameter vector p k =[p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] T , the matrix superscript T represents the transpose.

[0086] For the above trajectory, we set a series of constraints according to our requirements, set the positions of the starting and ending points, speed and acceleration, the path points we want to pass through, set the maximum speed and maximum acceleration, and hope that the trajectory joints are smooth, so the problem is modeled as a constrained optimization problem, as follows:

[0087]

[0088] Among them, minf(p) means minimizing the cost function; st means being constrained; f(p) is the cost function, which is expressed as T indicates the end time; A eq is a matrix, which is a dynamic constraint. d is the polynomial coefficient vector to be optimized, denoted by p d =[p1,p2,...,p k ,...,p m ]. eq =[p 0,1 ,v 0,1 ,a 0,1 ,p T1,1 ,v T1,1 ,a T1,1 ,p 0,2 ,...,p 0,j ,v 0,j ,a 0,j ,p Tj,j ,v Tj,j ,a Tj,j ,...],b eq is a vector representing the constraint boundary conditions. j represents the jth trajectory, Tj represents the end time of the jth trajectory, and p 0,jrepresents the starting position of the jth trajectory in the constraint boundary condition, v 0,j represents the starting velocity of the jth trajectory in the constraint boundary condition, a 0,j represents the starting acceleration of the jth trajectory in the constraint boundary condition, p Tj,j represents the end position of the jth trajectory in the constraint boundary condition, v Tj,j represents the terminal velocity of the jth trajectory in the constraint boundary condition, a Tj,j Represents the terminal acceleration of the jth trajectory in the constraint boundary condition.

[0089]

[0090] Among them, t 0,j represents the initial time of the jth trajectory in the dynamic constraint, t Tj,j represents the termination time of the jth trajectory in the dynamic constraint; p(t 0,j ) represents the starting position of the jth trajectory in the dynamic constraint, v(t 0,j ) represents the starting velocity of the jth trajectory in the dynamic constraint, a(t 0,j ) represents the initial acceleration of the jth trajectory in the dynamic constraint, p(t Tj,j ) represents the end position of the jth trajectory in the dynamic constraint, v(t Tj,j ) represents the terminal velocity of the jth trajectory in the dynamic constraint, a(t Tj,j ) represents the terminal acceleration of the jth trajectory in the dynamic constraint.

[0091] The f(p) function and A in the optimization problem eq ,b eq List them out, and then use the optimizer to solve the trajectory parameters. Get a desired trajectory from the starting point to the end point.

[0092] In one embodiment, the given constraints include: starting point position, end point position, speed, acceleration, desired path points, maximum speed, maximum acceleration, and desired smoothness of trajectory connections.

[0093] In one embodiment, preset time constraints are incorporated into the trajectory planning process to optimize the smoothness and dynamic performance of the trajectory and ensure that the generated trajectory meets the time requirements.

[0094] In one embodiment, time parameterization is introduced into the trajectory planning process. In the trajectory optimization stage, the desired time function T(t) is set and a correction term for time preset control is introduced, which is specifically in the form of:

[0095] T(t)=T0+△T(t) (7)

[0096] Among them, T0 is the initial time setting, △T(t) is the incremental function based on time change, which meets the constraints of time change;

[0097] Add a correction term for time preset control to the trajectory optimization objective function:

[0098] minf(p)+λ·e T (t)(8)

[0099] Among them, e T (t) = T(t) - T0, e T (t) represents the time preset error function, T(t) represents the actual completion time, T0 represents the initial time setting, and λ represents the weight coefficient, which is used to balance the influence of trajectory smoothness and time preset control.

[0100] The embodiment of this specification also discloses a bridge crane obstacle avoidance trajectory planning system based on piecewise time polynomial, including the following modules:

[0101] Module M1: Path planning for overhead cranes in the presence of obstacles.

[0102] Module M2: Trajectory planning of overhead crane based on planned paths via piecewise-time polynomials.

[0103] This application aims at the situation of frequent acceleration and deceleration in complex environments, and optimizes the constraints of trajectory polynomials to obtain an optimized trajectory with minimum acceleration change (minimum jitter). The segmented time polynomial trajectory optimization technology is used to generate a smoother trajectory, thereby effectively reducing the energy loss of the system. A preset time is added to the trajectory planning, and the control strategy is improved to smooth speed changes, reduce mutations, and effectively handle obstacles, thereby improving the safety and efficiency of crane operations. This method can achieve efficient and smooth trajectory planning and tracking in complex environments, improving the operating performance and energy efficiency of bridge cranes.

[0104] This application uses the RRT* algorithm to perform path planning in an obstacle environment. By searching for the parent node with the lowest cost and optimizing the path, an initial path from the starting point to the target point is generated to ensure that the path avoids obstacles and has the lowest cost. The trajectory planning method based on segmented time polynomials can generate smooth and low-energy trajectories. This method discretizes the path into instruction points and uses polynomials to fit these points to meet constraints such as smoothness, maximum speed, and maximum acceleration. Through time parameterization, time preset control is introduced into trajectory planning to optimize the smoothness and dynamic performance of the trajectory, ensuring that the generated trajectory completes the task within the scheduled time. This helps to reduce speed mutations in actual operations and improve safety and efficiency.

[0105] In this specification, the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments described later, the description is relatively simple, and the relevant parts can be referred to the partial description of the previous embodiments.

[0106] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for obstacle avoidance trajectory planning for a bridge crane based on piecewise time polynomials, characterized in that: include: Path planning for overhead cranes in environments with obstacles; Trajectory planning of overhead crane by piecewise-time polynomial based path planning.

2. The obstacle avoidance trajectory planning method for a bridge crane based on piecewise time polynomial according to claim 1 is characterized in that: A fast-expanding random tree star algorithm is used to plan paths in environments with obstacles. After randomly sampling points in space, the parent node with the minimum cost in the neighborhood is searched, and the cost needs to take into account the path length from the initial point to the target point to generate a path from the starting point to the target point.

3. The obstacle avoidance trajectory planning method for a bridge crane based on piecewise time polynomial according to claim 2 is characterized in that: According to the initial position, final position and obstacle position of the target point, a path to avoid obstacles is planned in space; Discretize the path into several path points, arrange them in the order of the motion path, and form a trajectory path point sequence; The initial position is: P0 = (x0, y0); Wherein, P0 represents the initial position, x0 represents the abscissa of the initial position, and y0 represents the ordinate of the initial position; The end position is: P g =(x g ,y g ); Among them, P g Indicates the end position; x g Indicates the horizontal coordinate of the end position; y g The vertical coordinate indicating the end position; The obstacle position is: O I =(x I ,y I ,l I ,h I )I=0,1,2,...; Among them, O I represents the spatial position of the Ith obstacle, x I is the lower left axis horizontal coordinate of the Ith obstacle, y I Indicates the lower left axis ordinate of the Ith obstacle, l I represents the length of the first obstacle, h I Indicates the width of the first obstacle.

4. The obstacle avoidance trajectory planning method for a bridge crane based on piecewise time polynomial according to claim 2 is characterized in that: When obstacles and / or target points change, the fast expanding random tree star algorithm is reused for path planning to update the path.

5. The obstacle avoidance trajectory planning method for a bridge crane based on piecewise time polynomial according to claim 1, characterized in that: A smooth trajectory is generated based on the path according to given constraints; wherein, according to the path planning, discrete instruction points are arranged in a sequence of the path, and the path is planned using a piecewise time polynomial to generate a smooth and low energy consumption trajectory of the bridge crane while avoiding collision between the payload and obstacles.

6. The obstacle avoidance trajectory planning method for a bridge crane based on piecewise time polynomial according to claim 5 is characterized in that: The obstacle avoidance trajectory from the starting point to the target point is formed by the command points obtained after discretization. The trajectory is expressed by an n-order polynomial: Among them, p(t) represents a one-dimensional trajectory polynomial, t represents the time parameter, p0, p1, ..., p n is n+1 trajectory parameters, p i represents the i-th trajectory parameter, i∈[0,n], and the parameter vector p=[p0,p1,...,p n ] T ,[p0,p1,...,p n ] T represents [p0,p1,...,p n ] is the transpose of ; then a trajectory is in the form of: p(t)=[1,t,t 2 ,...,t n ]·p; If the polynomial trajectory has a total of m segments, then the kth segment trajectory p k (t) is expressed as: p k (t)=[1,t,t 2 ,...,t n ]·[p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] T ; Among them, p ki represents the i-th parameter of the k-th trajectory, let the parameter vector p k =[p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] T ; [p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] T indicates [p k0 ,p k1 ,p k2 ,...,p ki ,...,p kn ] is the transpose of ; For the trajectory, set constraints according to requirements, and optimize the modeling constraints as follows: Among them, minf(p) means minimizing the cost function; st means being constrained; f(p) is the cost function; A eq is the matrix of dynamic constraints; p d is the polynomial coefficient vector to be optimized; b eq is the vector of constraint boundary conditions; Wherein, T represents the termination time; p d =[p1,p2,...,p k ,...,p m ]; b eq =[p 0,1 ,in 0,1 ,and 0,1 ,p T1,1 ,in T1,1 ,and T1,1 ,p 0,2 ,...,p 0,j ,in 0,j ,and 0,j ,p Tj,j ,in Tj,j ,and Tj,j ,...]; Among them, Tj represents the end time of the jth trajectory, p 0,j represents the starting position of the jth trajectory in the constraint boundary condition, v 0,j represents the starting velocity of the jth trajectory in the constraint boundary condition, a 0,j represents the starting acceleration of the jth trajectory in the constraint boundary condition, p Tj,j represents the end position of the jth trajectory in the constraint boundary condition, v Tj,j represents the terminal velocity of the jth trajectory in the constraint boundary condition, a Tj,j represents the terminal acceleration of the jth trajectory in the constraint boundary condition; Among them, t 0,j represents the initial time of the jth trajectory in the dynamic constraint, t Tj,j represents the termination time of the jth trajectory in the dynamic constraint; p(t 0,j ) represents the starting position of the jth trajectory in the dynamic constraint, v(t 0,j ) represents the starting velocity of the jth trajectory in the dynamic constraint, a(t 0,j ) represents the initial acceleration of the jth trajectory in the dynamic constraint, p(t Tj,j ) represents the end position of the jth trajectory in the dynamic constraint, v(t Tj,j ) represents the terminal velocity of the jth trajectory in the dynamic constraint, a(t Tj,j ) represents the terminal acceleration of the jth trajectory in the dynamic constraint; f(p), A eq and b eq The optimizer is used to solve the trajectory parameters and obtain the desired trajectory from the starting point to the end point.

7. The obstacle avoidance trajectory planning method for a bridge crane based on piecewise time polynomial according to claim 5 is characterized in that: The given constraints include: starting position, end position, speed, acceleration, desired path points, maximum speed, maximum acceleration, and desired smoothness at the trajectory joints.

8. The obstacle avoidance trajectory planning method for a bridge crane based on piecewise time polynomial according to claim 1, characterized in that: Preset time constraints are incorporated into the trajectory planning process to optimize the smoothness and dynamic performance of the trajectory and make the generated trajectory meet the time requirements.

9. The obstacle avoidance trajectory planning method for a bridge crane based on piecewise time polynomial according to claim 8, characterized in that: Introduce time parameterization into the trajectory planning process; in the trajectory optimization stage, set the expected time function T(t) and introduce the correction term of time preset control: T(t)=T0+△T(t); Among them, T0 is the initial time setting, △T(t) is the incremental function based on time change, which meets the constraints of time change; Add a correction term for time preset control to the trajectory optimization objective function: min f(p)+λ·e T (t); Among them, e T (t) = T(t) - T0, e T (t) represents the time preset error function, T(t) represents the actual completion time, T0 is the initial time setting, and λ represents the weight coefficient, which is used to balance the influence of trajectory smoothness and time preset control.

10. An obstacle avoidance trajectory planning system for a bridge crane based on piecewise time polynomials, characterized in that: Includes the following modules: Module M1: Path planning of overhead crane in an environment with obstacles; Module M2: Trajectory planning of overhead crane based on planned paths via piecewise-time polynomials.

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