Mining electric shovel trajectory planning method based on whole-process asymmetric multi-section S-shaped curve

Abstract

The invention discloses a mining electric shovel trajectory planning method based on a whole-process asymmetric multi-section S-shaped curve, and belongs to the technical field of trajectory planning. First, material surface point cloud data are obtained and processed. Secondly, establishing a mathematical model of the mining electric shovel motor based on a whole-process asymmetric multi-section S-shaped curve speed control algorithm; thirdly, constructing a trajectory equation of the bucket tooth tip of the mining electric shovel; and fourthly, giving a constraint boundary. And 5, constructing a mathematical model of a target function and a constraint function based on the whole-process asymmetric multi-section S-shaped curve speed control algorithm. And 6, inputting an optimization initial value, and performing optimization. And 7, outputting an optimal mining track, and ending optimization. On the basis of a traditional seven-section S-shaped curve speed control algorithm, the maximum speed of the motor of the mining electric shovel and the running time of each section of the motor can be optimized and adjusted, and online planning and energy consumption optimization of the mining electric shovel excavating track are achieved; complex motion characteristics in the excavation process can be effectively captured, and efficient operation under various working conditions is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of trajectory planning, and relates to a mining electric shovel trajectory planning method based on a full-process asymmetric multi-segment S-curve, and specifically to a method integrating an asymmetric seven-segment S-curve speed control algorithm in excavation trajectory planning. Background Art

[0002] As an important equipment for open-pit mining, mining shovels currently rely mainly on manual operation, but due to limited vision and complex terrain, there are safety hazards and increased energy consumption and wear. With the improvement of efficiency and safety requirements, intelligent excavation has become the development direction, among which trajectory planning is a key technology because it directly affects efficiency and energy consumption.

[0003] At present, one of the commonly used methods in trajectory planning is the method of numerical interpolation and fitting. The trajectory planning method based on numerical interpolation and fitting has the advantages of high accuracy, strong flexibility, and energy consumption optimization, but it is computationally complex and lacks robustness, and it is difficult to adapt to rapidly changing operating scenarios. Another method is trajectory planning based on kinematic and dynamic models, which has the advantage of accurately describing the motion characteristics of excavation equipment and can optimize energy efficiency and stability. However, its model complexity is high, the algorithm implementation and debugging are difficult, and its adaptability in complex operating environments still needs to be further verified. The asymmetric multi-segment S-curve speed planning algorithm achieves smooth control and energy consumption reduction by optimizing the bucket motion of the mining electric shovel. It has the potential to solve the computational complexity and model adaptability of the above methods, improve system stability, and reduce excavation energy consumption. The front-end working mechanism of the mining electric shovel mainly includes a bucket, a push rod, a pushing mechanism, a lifting rope, a lifting mechanism, a boom, and a sheave. In addition, the mining electric shovel is also equipped with a laser radar and an inclination sensor.

[0004] The present invention combines the asymmetric multi-segment S-curve speed planning algorithm with genetic algorithm optimization to construct a kinematic model of the mining shovel bucket based on the analytical method. The model takes the lowest excavation energy consumption as the optimization goal, and obtains the optimal speed curve by optimizing the maximum motor speed of the mining shovel and its control time in each segment, avoiding the mechanical shock and vibration caused by sharp deceleration, and ensuring the smoothness and continuity of the excavation trajectory. The main value of the present invention lies in achieving improved stability and reduced energy consumption during the excavation process, and providing an efficient and accurate optimization solution for the trajectory planning of the mining shovel. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a mining electric shovel trajectory planning method based on a full-process asymmetric multi-segment S-curve.

[0006] The technical solution adopted by the present invention is:

[0007] A mining electric shovel trajectory planning method based on a full-process asymmetric multi-segment S-curve is a method that integrates an asymmetric seven-segment S-curve speed control algorithm in excavation trajectory planning. The method of the asymmetric seven-segment S-curve speed control algorithm in excavation trajectory planning is as follows: the first step is to obtain and process the material surface point cloud data. First, the point cloud data of the material surface is obtained, including its three-dimensional coordinates and contour information. Then, MATLAB is used to segment the point cloud data, remove noise and irrelevant areas, extract effective three-dimensional shape and contour information, and fit the material surface through the Polynomial Response Surface (PRS) technology to provide a basis for subsequent excavation volume calculation. The second step is to establish a mathematical model of the mining electric shovel motor based on the full-process asymmetric multi-segment S-curve speed control algorithm. The maximum speed of the pushing motor and the lifting motor of the mining electric shovel and the running time of each segment are respectively used as optimization variables, and combined with kinematics knowledge, the mathematical model of the pushing motor and the lifting motor of the mining electric shovel based on the full-process asymmetric multi-segment S-curve speed control algorithm is constructed. The third step is to construct the trajectory equation of the bucket tooth tip of the mining electric shovel. Based on the mathematical model of the push motor and lifting motor of the mining electric shovel constructed in the fourth step, combined with the knowledge of kinematics, trigonometric functions and the front working mechanism of the mining electric shovel, the trajectory equation from the push motor and lifting motor of the mining electric shovel to the tip of the bucket tooth of the mining electric shovel is established. The fourth step is to give the constraint boundary. According to the actual maximum speed of the push motor and lifting motor of the mining electric shovel and the total excavation time, the constraint boundary of the corresponding optimization variable is determined. The fifth step is to construct the mathematical model of the objective function and constraint function based on the full-process asymmetric multi-segment S-curve speed control algorithm. The objective function takes the lowest energy consumption as the goal, and the constraint function includes the motor constraint of the mining electric shovel, the structural constraint of the front working mechanism of the mining electric shovel, and the safety height constraint during excavation. The sixth step is to input the initial optimization value and optimize. Based on the given initial value, the genetic algorithm (GA) is used for global optimization. The optimization variables are iteratively updated through selection, crossover and mutation operations until the objective function converges or reaches the set number of iterations, and finally the optimal excavation trajectory is generated. The seventh step is to output the optimal excavation trajectory and end the optimization. The specific steps include:

[0008] The first step is to obtain and process the material surface point cloud data. The details are as follows:

[0009] The laser radar collects the three-dimensional coordinates and contour information of the material surface within the excavation range of the mining electric shovel. Due to the influence of factors such as the reduced density and resolution of the laser beam when scanning the edge, in order to improve the fitting accuracy and reduce the calculation burden, the material surface point cloud data needs to be segmented, noise points and irrelevant areas need to be removed, and the effective three-dimensional shape and contour information of the material surface point cloud need to be extracted. The x-axis coordinate range of the segmented material surface point cloud is [-6, 6], the y-axis coordinate range is [6, 25], and the z-axis range is [0, 15], all in meters. Finally, the segmented material surface point cloud is fitted using PRS.

[0010] The second step is to establish a mathematical model of the speed control algorithm of the electric shovel motor based on the asymmetric multi-segment S-curve in the whole process. The maximum speed of the push motor and the lifting motor of the electric shovel and the running time of each segment are used as optimization variables, and combined with kinematics knowledge, a mathematical model of the speed control algorithm of the push motor and the lifting motor of the electric shovel based on the asymmetric multi-segment S-curve in the whole process is constructed. The details are as follows:

[0011] In the asymmetric multi-segment S-curve speed, in order to ensure the stability of the excavation process, the initial speed and the final speed are set to 0. Assume that the time periods from the first to the last are T1, T2, T3, T4, T5, T6, and T7 respectively, and the total time is T, t i (i=1,2,…,7) represents the end time node of each time period, and the maximum speed of the motor is v max , the motor acceleration expression is derived as:

[0012]

[0013] The acceleration expression is:

[0014]

[0015] Where J1, J3, J5, and J7 are the jerks of the 1st, 3rd, 5th, and 7th stages, respectively.

[0016] The speed expression is:

[0017]

[0018] Where v1, v2, v5, and v6 represent the final velocities of stages 1, 2, 5, and 6, respectively.

[0019] The displacement expression is:

[0020]

[0021] Where S1, S2, S3, S4, S5, and S6 represent the final displacements of stages 1, 2, 3, 4, 5, and 6, respectively. The maximum velocity v of formula (1) maxAnd the motor running time T i (i=1,2,…,7) refers to the pushing motor and lifting motor of the mining electric shovel and their corresponding operating time.

[0022] Furthermore, when calculating the acceleration formula (2) of the mining shovel motor, the asymmetric multi-segment S-curve speed control algorithm is designed more finely in the start-stop stage. By adjusting the time allocation and acceleration curve shape of each stage, the residual vibration can be effectively reduced or eliminated, and a smoother motion process can be achieved. This optimized control strategy helps to improve the motion quality and overall efficiency of the mining shovel.

[0023] The asymmetric multi-segment S-curve speed control algorithm makes the speed curve smoother and more flexible by flexibly adjusting the acceleration and jerk in different motion stages. The algorithm can effectively optimize under complex working conditions, reduce the load and power peak of the motor during acceleration and deceleration, and reduce the total energy consumption of the system. Through reasonable speed curve planning, the asymmetric multi-segment S-curve speed control algorithm not only ensures the smoothness of movement, but also avoids unnecessary shock and vibration, improving the operating efficiency and reliability of the system.

[0024] The third step is to construct the trajectory equation of the bucket tooth tip of the mining shovel. Based on the mathematical model of the push motor and lifting motor of the mining shovel constructed in the second step, combined with the knowledge of kinematics, trigonometric functions and the front working mechanism of the mining shovel, the trajectory equation from the push motor and lifting motor of the mining shovel to the bucket tooth tip of the mining shovel is established. The details are as follows:

[0025] Step 3.1) First, according to the working principle of mining shovel, during the mining process, the rich excavation tracks are directly formed by the combined action of the pushing motor and lifting motor of the mining shovel. In other words, when the pushing mechanism and lifting mechanism of the mining shovel are given different speeds, different push rod displacements Δl will be obtained. gan and the lifting rope displacement Δl rope , the push rod and the big arm will form different arc angles θ in the same plane, which directly affects the shape of the excavation trajectory. The trajectory equation of the push motor and the lifting motor of the mining shovel to the bucket tooth tip of the mining shovel is derived. The first thing to solve is the above angle θ, and find the relationship between the angle θ and the push rod displacement Δl of the mining shovel. gan and the lifting rope displacement Δl rope The function f is:

[0026] θ=f(Δl gan ,Δl rope ) (5)

[0027] Step 3.2) Secondly, according to the structural relationship between the front working mechanisms of the mining electric shovel, find the trajectory equation of the bucket tooth tip of the mining electric shovel. The equation can be expressed as follows:

[0028]

[0029] In the formula, x(t) and y(t) are the horizontal and vertical trajectory equations of the bucket tooth tip of the mining shovel, respectively. g and h are the functional relationship between the horizontal and vertical coordinates of the bucket tooth tip of the mining shovel and time. s It is a collection of the structural dimensions of the mining shovel bucket, push rod, hoisting rope, boom, and sheave, as well as the height from the sheave to the horizontal ground, and the initial height from the mining shovel bucket to the horizontal ground during initial excavation.

[0030] Step 3.3) Finally, the volume of the material inside the bucket of the mining shovel during excavation is calculated based on the trajectory equation of the vertical direction of the tooth tip of the mining shovel bucket obtained in step 3.2):

[0031]

[0032] In the formula, w represents the bucket width of the mining shovel, y w (t) is the material surface prediction equation, Δx represents the two adjacent discrete points x in the horizontal excavation trajectory x(t). i (t) and x i+1 (t) The displacement difference within time Δt is x(Δt+t)-x(t). The calculation of excavation volume is the basis for the subsequent calculation of the full bucket rate constraint.

[0033] The fourth step is to give the constraint boundary. The speed change of the motor of the mining shovel is divided into seven periods. By applying the constraints of speed boundary and time boundary, the motor speed of the mining shovel and the running time of each section of the motor are controlled to obtain the optimal excavation trajectory and the lowest excavation energy consumption. The details are as follows:

[0034] According to the maximum motor speed of the mining shovel and its corresponding running time in each section in the above formula (1), all the optimization variables of the present invention are determined as follows:

[0035]

[0036] In the formula, x is the decision variable, v pmax 、v hmax are the maximum speeds of the pushing motor and lifting motor of the mining shovel, T pi (i=1,2,…,7) and T hj(j=1,2,…,6) are the running time of each section of the pushing motor and the lifting motor of the mining shovel. Here, only the first 6 time periods of the lifting motor of the mining shovel need to be taken. The sum of the 7 time periods of the pushing motor of the mining shovel minus the sum of the 6 time periods of the lifting motor of the mining shovel can be obtained to obtain the last time period of lifting.

[0037] The following is the value range of all optimization variables of the present invention corresponding to formula (8):

[0038] Pushing motor speed v pmax (m / s): 3650-6100; increase motor speed v hmax (m / s): 3650-6000; Pushing the first time period T p1 / s: 0.5-4; push the second time period T p2 / s: 0.5-4; push the third time period T p3 / s: 0.5-4; push the fourth time period T p4 / s: 0.3-3; push the fifth time period T p5 / s: 0.5-4; push the sixth time period T p6 / s: 0.5-4; push the seventh time period T p7 / s: 0.5-4; improve the first time period T h1 / s: 0.5-4; increase the second time period T h2 / s: 0.5-4; improve the third time period T h3 / s: 0.5-4; improve the T of the fourth time period h4 / s: 0.5-4; improve the fifth time period T h5 / s: 0.5-4; improve the T of the sixth time period h6 / s:0.5-4.

[0039] The fifth step is to construct a mathematical model of the objective function and constraint function based on the full-process asymmetric multi-segment S-curve speed control algorithm. High energy consumption means high operating costs; secondly, it means waste of resources and low efficiency; finally, high energy consumption will also shorten the life of the equipment. Optimizing the excavation energy consumption can reduce the operating temperature of the equipment, extend the life of the equipment, and improve the stability and reliability of the equipment. Therefore, the objective function of the present invention is defined as the energy consumption per unit excavation volume, and the constraint function includes the motor constraints of the mining electric shovel, the structural constraints of the front-end working mechanism of the mining electric shovel, and the safety height constraints during excavation. The details are as follows:

[0040] Step 5.1) Determine the objective function. The objective function of the present invention is expressed as:

[0041] J=(E p +E h ) / V (9)

[0042] In the formula, E p and E h Respectively represent the energy consumption of the pushing motor and lifting motor of the mining shovel during the excavation process, specifically:

[0043]

[0044] In the formula, F p and F h Represent the pushing force and lifting force respectively, v gan and v rope They represent the speed of the push rod and the speed of the lifting rope of the mining electric shovel respectively.

[0045] Step 5.2) Determine the constraint function. To ensure that the mining shovel meets the necessary structural and reliability requirements during the excavation process, it is necessary to constrain the motor's operating speed and time to ensure that the movement of the shovel under actual working conditions meets the engineering requirements. In addition, the structural constraints of the front working mechanism of the mining shovel need to be considered to ensure the reliability and stability of its structure. Specifically:

[0046] (1) For the excavation performance constraint, the instantaneous output force and instantaneous output power of the motor of the mining shovel are considered to be less than the allowable limit value, that is:

[0047]

[0048] In the formula, F pmax and F hmax are the limit output forces of the pushing motor and lifting motor of the mining shovel, P p and P h are the instantaneous output power of the pushing motor and lifting motor of the mining electric shovel, P pmax and P hmax They are respectively the maximum power of the pushing motor and the lifting motor of the mining electric shovel.

[0049] (2) Considering the stability of the excavation process, the speed of the push rod and the lifting rope cannot be less than 0 during excavation, that is:

[0050]

[0051] (3) In order to ensure the excavation efficiency, according to the excavation experience of experienced mining shovel operators, a single excavation cycle must be kept within a reasonable range, that is:

[0052]

[0053] (4) In order to avoid energy waste when digging underload and low efficiency when overloaded, it is necessary to constrain the full bucket rate. Here, the full bucket rate range for a single digging is set to 90%-110%, that is:

[0054]

[0055] Where V n Stands for rated load volume.

[0056] (5) For structural constraints, the maximum extension of the push rod Less than the maximum length l allowed max ,Right now:

[0057]

[0058] (6) To ensure that the mining shovel can contact the material during excavation, the initial horizontal position of the excavation trajectory is greater than the bottom of the material pile, that is:

[0059] g6=[Htanθ0-x(t0)≤0](17)

[0060] (7) In order to prevent the bottom of the bucket from scraping the ground at the beginning of excavation and to ensure that the equipment is not damaged, the minimum initial height of the bottom of the bucket is higher than the ground height, that is:

[0061] g7=[H0-y Mmin ≤0](18)

[0062] (8) In order to ensure that the overall structure of the bucket is basically or completely separated from the material surface at the end of excavation and to ensure smooth excavation, the end of the excavation end position is greater than the set value and greater than the material surface height, that is:

[0063] g8=[Y e -y tip_end (t)≤0](19)

[0064] Where Y e It is the setting value according to the material surface height.

[0065] After arranging the above constraints, all the constraints of the asymmetric multi-segment S-curve speed control algorithm can be obtained as follows:

[0066]

[0067] Step 6: Input the initial optimization value and optimize. Given the initial value of the optimization variable, use GA to globally optimize the objective function (Equation (9)) of the mining shovel. When the design variable converges to a stable value or the number of iterations is greater than the maximum allowed number of iterations, set the end condition. If the end condition is not met, modify the initial optimization value and repeat step 6; otherwise, terminate the loop and generate the optimal mining trajectory.

[0068] Step 7: Output the optimal mining trajectory and end the optimization.

[0069] Beneficial effects of the present invention:

[0070] The present invention can plan the excavation trajectory of the mining electric shovel online. On the basis of the traditional seven-segment S-curve speed control algorithm, it optimizes and adjusts the maximum speed of the motor of the mining electric shovel and the running time of each segment of the motor, so as to realize the online planning and energy consumption optimization of the excavation trajectory of the mining electric shovel. At the same time, the proposed method has significant characteristics. By introducing the full-process asymmetric multi-segment S-curve speed control algorithm, it can realize the fine control of speed, acceleration and jerk in trajectory planning, and effectively capture the complex motion characteristics during the excavation process. In addition, the algorithm flexibly adjusts the motion trajectory of the push motor and the lifting motor of the mining electric shovel, and takes the excavation energy consumption as the optimization target, so that the excavation trajectory is smoother and more stable, and the impact and vibration are reduced, ensuring the efficient operation of the mining electric shovel under various working conditions. The present invention will improve the intelligence level of the mining electric shovel and provide a new architecture for the trajectory planning of the mining electric shovel. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The present invention is a flow chart of a method for trajectory planning of a mining electric shovel based on a full-process asymmetric multi-segment S-curve.

[0072] Figure 2 This is a power comparison chart of the push motor of the WK12 model mining electric shovel based on the method of mining electric shovel trajectory planning based on the full-process asymmetric multi-segment S-curve and the T-curve speed control algorithm and the symmetric S-curve speed control algorithm.

[0073] Figure 3 This is a comparison chart of the power of the lifting motor of the WK12 model mining electric shovel based on the method of trajectory planning for a mining electric shovel based on a full-process asymmetric multi-segment S-curve and the T-curve speed control algorithm and the symmetric S-curve speed control algorithm. DETAILED DESCRIPTION

[0074] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0075] The method of the convolutional recurrent deep learning model designed by the present invention for predicting digging force by integrating physical priors is as follows: Figure 1 As shown, the specific implementation is as follows:

[0076] In the first step, the laser radar is used to collect the three-dimensional coordinates and contour information of the material surface within the excavation range of the mining shovel, and then the x-axis coordinate range of the segmented material surface point cloud is set to [-6, 6], the y-axis coordinate range is [6, 25], and the z-axis range is [0, 15], all in meters. Finally, the segmented material surface point cloud is fitted using PRS to provide a material surface basis for subsequent trajectory planning.

[0077] The second step is to establish a mathematical model of the speed control algorithm of the electric shovel motor based on the full-process asymmetric multi-segment S-curve. The maximum speed of the push motor and the lifting motor of the electric shovel and the running time of each segment are used as optimization variables, combined with kinematics knowledge. At the same time, in order to ensure the stability of the excavation process, the initial speed and the final speed are set to 0, and the jerk (formula (1)), acceleration (formula (2)), speed (formula (3)), and displacement (formula (4)) of the push motor and the lifting motor of the electric shovel based on the full-process asymmetric multi-segment S-curve speed control algorithm are constructed.

[0078] The third step is to construct the trajectory equation of the bucket tooth tip of the mining electric shovel based on the mathematical model of the pushing motor and lifting motor of the mining electric shovel constructed in the second step, combined with the knowledge of kinematics, trigonometric functions and the front working mechanism of the mining electric shovel. Follow the steps below:

[0079] First, according to the working principle of mining shovels, when the pushing mechanism and lifting mechanism of mining shovels are given different speeds (Formula (3)), different push rod displacements Δl will be obtained. gan and the lifting rope displacement Δl rope , the push rod and the boom will form different arc angles θ in the same plane, determine the angle θ and the push rod displacement Δl of the mining electric shovel gan and the lifting rope displacement Δl rope The function f is formula (5).

[0080] Secondly, according to the structural dimensions of the mining shovel bucket, push rod, lifting rope, boom, and sheave, as well as the height from the sheave to the horizontal ground and the initial height from the mining shovel bucket to the horizontal ground during initial excavation, the trajectory equations of the mining shovel bucket tooth tip in the horizontal direction and the vertical direction are determined, namely, formula (6).

[0081] Finally, based on the trajectory equation of the vertical direction of the bucket tooth tip of the mining shovel, the material volume inside the bucket of the mining shovel during excavation is calculated, laying the foundation for the subsequent full bucket rate objective function.

[0082] The fourth step is to give the constraint boundary. The speed change of the motor of the mining shovel is divided into seven time periods. By optimizing the maximum speed of the pushing motor and the lifting motor of the mining shovel and the running time of each section of the corresponding motor, the optimal excavation trajectory and the lowest excavation energy consumption are obtained. The value range of all optimization variables in the optimization process of the present invention is as follows:

[0083] Pushing motor speed v pmax (m / s): 3650-6100; increase motor speed v hmax (m / s): 3650-6000; Pushing the first time period T p1 / s: 0.5-4; push the second time period Tp2 / s: 0.5-4; push the third time period T p3 / s: 0.5-4; push the fourth time period T p4 / s: 0.3-3; push the fifth time period T p5 / s: 0.5-4; push the sixth time period T p6 / s: 0.5-4; push the seventh time period T p7 / s: 0.5-4; improve the first time period T h1 / s: 0.5-4; increase the second time period T h2 / s: 0.5-4; improve the third time period T h3 / s: 0.5-4; improve the T of the fourth time period h4 / s: 0.5-4; improve the fifth time period T h5 / s: 0.5-4; improve the T of the sixth time period h6 / s:0.5-4.

[0084] The fifth step is to construct a mathematical model of the objective function and constraint function of the present invention based on the full-process asymmetric multi-segment S-curve speed control algorithm, taking the energy consumption per unit excavation volume as the objective function of the present invention, taking the motor constraint of the mining electric shovel, the structural constraint of the front working mechanism of the mining electric shovel, and the safety height constraint during excavation as the constraint function. The specific implementation is as follows:

[0085] First, the objective function (Formula (9)) is determined based on the energy consumption of the pushing motor and lifting motor of the mining shovel during the excavation process (Formulas (10) and (11)).

[0086] Secondly, determine the constraint function. To ensure that the mining shovel meets the necessary structural and reliability requirements during the excavation process, it is necessary to constrain the running speed and time of the motor to ensure that the movement of the shovel under actual working conditions meets the engineering requirements. In addition, the structural constraints of the front working mechanism of the mining shovel need to be considered to ensure the reliability and stability of its structure. The specific constraints are summarized as formula (20).

[0087] Step 6: Input the initial optimization value and optimize. Given a set of initial values ​​of the optimization variables, use GA to globally optimize the objective function of the mining shovel. When the design variables converge to a stable value or the number of iterations is greater than the maximum allowed number of iterations, set the end condition. If the end condition is not met, modify the initial optimization value and repeat step 6; otherwise, terminate the loop and generate the optimal mining trajectory. Here, the maximum number of iterations of GA is set to 400, the population size is set to 500, and the function tolerance is set to 10 -6 .

[0088] Step 7: Output the optimal mining trajectory and end the optimization.

[0089] Figure 2 and Figure 3 The following are the power comparison diagrams of the pushing motor and lifting motor of the WK12 mining electric shovel based on the method of trajectory planning of the whole process asymmetric multi-segment S-curve of the present invention, the T-curve speed control algorithm, and the symmetric S-curve speed control algorithm. It can be seen from the figure that the pushing motor power and lifting motor power of the method of the present invention are lower than those of the other two methods. The measured data can be compared with the prediction method proposed in this patent.

[0090] The above-described embodiments merely express the implementation methods of the present invention, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A mining shovel trajectory planning method based on a full-process asymmetric multi-segment S-curve, characterized in that: The mining electric shovel trajectory planning method is a method that integrates an asymmetric seven-segment S-curve speed control algorithm in excavation trajectory planning, and the steps are: The first step is to obtain and process the material surface point cloud data; The second step is to establish a mathematical model of the speed control algorithm of the electric shovel motor based on the asymmetric multi-segment S-curve throughout the whole process. The maximum speed of the push motor and the lifting motor of the electric shovel and the running time of each segment are used as optimization variables, and combined with kinematics knowledge, a mathematical model of the speed control algorithm of the push motor and the lifting motor of the electric shovel based on the asymmetric multi-segment S-curve throughout the whole process is constructed. The third step is to construct the trajectory equation from the pushing motor and the lifting motor of the mining electric shovel to the bucket tooth tip of the mining electric shovel based on the mathematical model of the pushing motor and the lifting motor of the mining electric shovel constructed in the second step; The fourth step is to give the constraint boundary; according to the actual maximum speed of the pushing motor and lifting motor of the mining electric shovel and the total excavation time, determine the constraint boundary of the corresponding optimization variable; The fifth step is to construct a mathematical model of the objective function and constraint function based on the full-process asymmetric multi-segment S-curve speed control algorithm; the objective function takes the lowest energy consumption as the goal, and the constraint function includes the motor constraint of the mining electric shovel, the structural constraint of the front working mechanism of the mining electric shovel, and the safety height constraint during excavation; Step 6: Input the initial optimization value and optimize; perform global optimization based on the given initial value and iteratively update the optimization variable until the objective function converges or reaches the set number of iterations, and finally generate the optimal mining trajectory; Step 7: Output the optimal mining trajectory and end the optimization.

2. According to claim 1, a mining shovel trajectory planning method based on a full-process asymmetric multi-segment S-curve is characterized in that: The first step is to obtain and process the material surface point cloud data; the details are as follows: Firstly, the laser radar collects the 3D coordinates and contour information of the material surface within the excavation range of the mining electric shovel; secondly, MATLAB is used to segment the point cloud data, eliminate noise and irrelevant areas, and extract the effective 3D shape and contour information of the material surface point cloud. The x-axis coordinate range of the segmented material surface point cloud is [-6, 6], the y-axis coordinate range is [6, 25], and the z-axis range is [0, 15], all in meters; finally, the segmented material surface point cloud is fitted using PRS.

3. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-curve according to claim 2 is characterized in that: The second step is to establish a mathematical model of the mining shovel motor based on the full-process asymmetric multi-segment S-curve speed control algorithm; the details are as follows: In the asymmetric multi-segment S-curve speed, let the initial speed and final speed be 0; suppose that the time periods from the first to the last are T1, T2, T3, T4, T5, T6, and T7 respectively, and the total time is T, t i (i=1,2,…,7) represents the end time node of each time period, and the maximum speed of the motor is v max , the motor acceleration expression is derived as: The acceleration expression is: Where, J1, J3, J5, and J7 are the jerks of the 1st, 3rd, 5th, and 7th stages respectively; The speed expression is: Where v1, v2, v5, and v6 represent the final velocities of stages 1, 2, 5, and 6, respectively; The displacement expression is: Where S1, S2, S3, S4, S5, and S6 represent the final displacements of stages 1, 2, 3, 4, 5, and 6, respectively; the maximum velocity v of formula (1) is max And the motor running time T i (i=1,2,…,7) refers to the pushing motor and lifting motor of the mining electric shovel and their corresponding operating time.

4. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-curve according to claim 3 is characterized in that: The third step is to construct a trajectory equation from the pushing motor and the lifting motor of the mining electric shovel to the bucket tooth tip of the mining electric shovel; the details are as follows: Step 3.1) First, during the excavation process of the mining shovel, its excavation trajectory is directly formed by the combined action of the pushing motor and the lifting motor of the mining shovel; when the pushing mechanism and the lifting mechanism of the mining shovel are given different speeds, different push rod displacements Δl will be obtained gan and the lifting rope displacement Δl rope , the push rod and the big arm will form different arc angles θ in the same plane, which directly affects the shape of the excavation trajectory; the trajectory equation of the pushing motor and the lifting motor of the mining electric shovel to the bucket tooth tip of the mining electric shovel is derived to obtain the angle θ, which is related to the push rod displacement Δl of the mining electric shovel. gan and the lifting rope displacement Δl rope The function f is: θ=f(Δl gan ,Δl rope ) (5) Step 3.2) Secondly, according to the structural relationship between the front-end working mechanisms of the mining electric shovel, the trajectory equation of the bucket tooth tip of the mining electric shovel is obtained as shown in formula (6), that is: In the formula, x(t) and y(t) are the horizontal and vertical trajectory equations of the bucket tooth tip of the mining shovel, respectively. g and h are the functional relationship between the horizontal and vertical coordinates of the bucket tooth tip of the mining shovel and time. s It is a collection of the structural dimensions of the mining shovel bucket, push rod, hoist rope, boom, and sheave, as well as the height from the sheave to the horizontal ground, and the initial height from the mining shovel bucket to the horizontal ground during initial excavation; Step 3.3) Finally, the volume of the material inside the bucket of the mining shovel during excavation is calculated according to the trajectory equation of the vertical direction of the bucket tooth tip of the mining shovel obtained in step 3.2), that is: In the formula, w represents the bucket width of the mining shovel, y w (t) is the material surface prediction equation, Δx represents the two adjacent discrete points x in the horizontal excavation trajectory x(t). i (t) and x i+1 (t) The displacement difference within time Δt is x(Δt+t)-x(t).

5. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-curve according to claim 4 is characterized in that: The fourth step is to give the constraint boundary. The speed change of the motor of the mining shovel is divided into seven time periods. By applying the constraints of the speed boundary and the time boundary, the motor speed of the mining shovel and the running time of each section of the motor are controlled to obtain the optimal excavation trajectory and the lowest excavation energy consumption. The details are as follows: According to the maximum motor speed of the mining shovel and its corresponding running time in each section in formula (1), all optimization variables are determined as follows: In the formula, x is the decision variable, v pmax 、v hmax are the maximum speeds of the pushing motor and lifting motor of the mining shovel, T pi (i=1,2,…,7) and T hj (j=1,2,…,6) are the running time of each section of the pushing motor and the lifting motor of the mining electric shovel respectively; Here, only the first 6 time periods of the lifting motor of the mining electric shovel are taken, and the sum of the 7 time periods of the pushing motor of the mining electric shovel is subtracted from the sum of the 6 time periods of the lifting motor of the mining electric shovel to obtain the last time period of lifting.

6. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-curve according to claim 5 is characterized in that: The value ranges of all optimization variables are as follows: Pushing motor speed v pmax (m / s): 3650-6100; increase motor speed v hmax (m / s): 3650-6000; Pushing the first time period T p1 / s: 0.5-4; push the second time period T p2 / s: 0.5-4; push the third time period T p3 / s: 0.5-4; push the fourth time period T p4 / s: 0.3-3; push the fifth time period T p5 / s: 0.5-4; push the sixth time period T p6 / s: 0.5-4; push the seventh time period T p7 / s: 0.5-4; improve the first time period T h1 / s: 0.5-4; increase the second time period T h2 / s: 0.5-4; improve the third time period T h3 / s: 0.5-4; improve the T of the fourth time period h4 / s: 0.5-4; improve the fifth time period T h5 / s: 0.5-4; improve the T of the sixth time period h6 / s:0.5-4.

7. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-curve according to claim 5 is characterized in that: The fifth step is to construct a mathematical model of the objective function and constraint function based on the full-process asymmetric multi-segment S-curve speed control algorithm; specifically as follows: Step 5.1) Determine the objective function: J=(E p +E h ) / V (9) In the formula, E p and E h They represent the energy consumption of the pushing motor and lifting motor of the mining shovel during the excavation process respectively; Step 5.2) Determine the constraint function, as follows: (1) For the excavation performance constraint, the instantaneous output force and instantaneous output power of the motor of the mining shovel are considered to be less than the allowable limit value, that is: In the formula, F pmax and F hmax are the limit output forces of the pushing motor and lifting motor of the mining shovel, P p and P h are the instantaneous output power of the pushing motor and lifting motor of the mining electric shovel, P pmax and P hmax They are the limit power of the pushing motor and lifting motor of the mining electric shovel respectively; (2) Considering the stability of the excavation process, the speed of the push rod and the lifting rope cannot be less than 0 during excavation, that is: (3) In order to ensure mining efficiency, a single mining cycle must be kept within a reasonable range, that is: (4) Constraining the full bucket rate; here, the full bucket rate range of a single excavation is set to 90%-110%, that is: Where V n Represents the rated loading volume; (5) For structural constraints, the maximum extension of the push rod l O2F Less than the maximum length l allowed max ,Right now: (6) To ensure that the mining shovel can contact the material during excavation, the initial horizontal position of the excavation trajectory is greater than the bottom of the material pile, that is: g6=[Htanθ0-x(t0)≤0] (17) (7) In order to prevent the bottom of the bucket from scraping the ground at the beginning of excavation and to ensure that the equipment is not damaged, the minimum initial height of the bottom of the bucket is higher than the ground height, that is: g7=[H0-y Mmin ≤0] (18) (8) At the end of excavation, the overall structure of the bucket is basically or completely out of the material surface, and the end of the excavation end position is greater than the set value and greater than the material surface height, that is: g8=[Y e -and tip_end (t)≤0] (19) Where Y e It is based on the set value of the material surface height; After arranging the above constraints, all the constraints of the asymmetric multi-segment S-curve speed control algorithm can be obtained as follows:

8. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-curve according to claim 7 is characterized in that: In step 5.1), E p and E h Specifically: In the formula, F p and F h Respectively represent the pushing force and lifting force, v gan and v rope They represent the speed of the push rod and the speed of the lifting rope of the mining electric shovel respectively.

9. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-curve according to claim 7 is characterized in that: The sixth step is to input the initial optimization value for optimization, as follows: Given the initial values ​​of the optimization variables, GA is used to perform global optimization on the objective function of the mining electric shovel as shown in formula (9); when the design variables converge to a stable value or the number of iterations is greater than the maximum allowed number of iterations, the end condition is set; if the end condition is not met, the initial optimization value is modified and the sixth step is repeated; Otherwise, the loop is terminated and the optimal mining trajectory is generated.

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