Optimal trajectory planning method for excavator pipe ditch operation

Abstract

The invention is suitable for the technical field of trajectory planning, and provides an optimal trajectory planning method for excavator pipe ditch operation, which comprises the following steps: establishing an excavator kinematics and dynamics model; the pipe ditch operation mode is divided into straight ditch excavation and side wall finishing; a piecewise polynomial interpolation method is adopted, a straight ditch excavation track and a side wall finishing track are planned in a joint space, and the angle, the angular speed and the angular acceleration of each joint of the excavator are obtained; the interpolation time of each segment in the segmented polynomial trajectory is used as a variable, and a trajectory planning model with mining time, joint impact and mining energy consumption as a comprehensive target is established; optimizing the trajectory planning model by adopting a self-adaptive genetic algorithm; and outputting the optimized pipe ditch excavation track. According to the method, aiming at the optimal excavation trajectory planning problem, the excavation trajectory is optimized, and the working efficiency, the smoothness and the energy consumption are effectively considered.

Description

Technical Field

[0001] This invention belongs to the field of trajectory planning technology, and in particular relates to an optimal trajectory planning method for trenching operations by excavators. Background Technology

[0002] Excavators are common earthmoving machines that play a crucial role in construction, disaster relief, and improving people's livelihoods, serving as essential tools in modern construction. Pipe trench excavation, one of the most common and typical excavator operations, demands high precision, efficiency, and safety. In traditional manual operation, operators rely on their experience to control the excavator and continuously excavate along the intended trench cross-section. However, manual operation is not only physically demanding but also prone to errors, resulting in irregular trench slopes and uneven trench bottoms, which can affect subsequent pipeline or cable laying. Furthermore, the lack of precise operational procedures in manual excavation often leads to energy waste, over-excavation, or under-excavation, reducing construction efficiency and increasing costs.

[0003] Currently, although intelligent methods exist, most employ a direct-position approach, where the excavator is parked at a right angle to the trench edge. This approach has the following drawbacks in trajectory planning: Limited operating space restricts the excavator's working device's range of motion, constraining the freedom of trajectory planning; the continuity of the trajectory in terms of position, speed, and acceleration is difficult to guarantee, resulting in an uneven movement and significant operational impact; there is a risk of interference between the bucket's trajectory and the trench wall or bottom, making it difficult to ensure a flat trench bottom and consistent side slopes; and when excavating a trench of a certain length, the excavator needs to be moved to a new work point multiple times, leading to low work efficiency and increased energy consumption.

[0004] Therefore, generating trench excavation trajectories that meet the requirements of smoothness, high efficiency, and low energy consumption based on task needs has significant practical significance and potential value for improving the level of intelligent and unmanned operation of excavators. Summary of the Invention

[0005] The purpose of this invention is to provide an optimal trajectory planning method for trenching operations using excavators, aiming to solve the problems mentioned in the background art.

[0006] The present invention is implemented as follows: an optimal trajectory planning method for excavator trenching operations includes the following steps:

[0007] Establish a kinematic and dynamic model of the excavator;

[0008] The trenching operation methods are divided into straight trench excavation and sidewall trimming;

[0009] Using a piecewise polynomial interpolation method, the straight trench excavation and sidewall trimming trajectories are planned separately in the joint space to obtain the angles, angular velocities, and angular accelerations of each joint of the excavator.

[0010] By using the interpolation time of each segment in the piecewise polynomial trajectory as variables, a trajectory planning model with excavation time, joint impact, and excavation energy consumption as comprehensive objectives is established.

[0011] An adaptive genetic algorithm is used to optimize the trajectory planning model;

[0012] Output the optimized trench excavation trajectory.

[0013] This invention provides an optimal trajectory planning method for excavator trenching operations. It employs a straddle-type excavation method (the excavator is positioned in the center of the trench, with the bucket digging between the two tracks, compared to the traditional face-to-face arrangement). The method divides the operation into straight trench excavation and sidewall trimming, which not only expands the movement space of the working device but also reduces interference risks while maintaining smooth and continuous trajectories, effectively improving construction accuracy and operational efficiency, while also optimizing energy consumption. For different operational scenarios of straight trench excavation and sidewall trimming, a piecewise polynomial interpolation trajectory planning method is proposed, enabling smooth and continuous connection of multiple trajectory segments during straight trench excavation, while simultaneously meeting the precise constraints of inclined trajectory during sidewall trimming. An adaptive genetic algorithm is used to optimize the time, impact, and energy consumption of the excavation trajectory, achieving a balance between global search capability and convergence efficiency under complex constraints, thus obtaining superior overall performance. Automated trajectory planning reduces reliance on manual operation and improves operational safety. Attached Figure Description

[0014] Figure 1 A flowchart illustrating an optimal trajectory planning method for excavator trenching operations provided in an embodiment of the present invention;

[0015] Figure 2 This is a schematic diagram of the trajectory curve of the straight trench excavation process provided in an embodiment of the present invention;

[0016] Figure 3 This is a schematic diagram of the trajectory curve of the sidewall trimming process provided in an embodiment of the present invention;

[0017] Figure 4 The flowchart of the adaptive genetic algorithm provided in the embodiment of the present invention is shown. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0020] like Figure 1 The flowchart shown is a method for optimal trajectory planning in trenching operations using an excavator, according to an embodiment of the present invention, and includes the following steps:

[0021] S1. Establish the kinematics and dynamics model of the excavator:

[0022] Based on the geometric dimensions of the excavator's working device, a kinematic model is established, and a dynamic model is constructed to provide a theoretical basis for subsequent trajectory planning.

[0023] S2. Decomposition of trenching tasks: Trenching operations are divided into straight trench excavation and sidewall trimming.

[0024] When using a straddle-type excavator layout, the trench excavation operation can be divided into straight trench excavation and sidewall trimming. Trench operation is a continuous path planning problem, which requires defining a path consisting of a starting point, an ending point, and several intermediate excavation control points.

[0025] S3. Piecewise Polynomial Excavation Trajectory Planning: Using a piecewise polynomial interpolation method, the trajectories for straight trench excavation and sidewall trimming are planned separately in the joint space to obtain the angles, angular velocities, and angular accelerations of each joint of the excavator. Specifically:

[0026] When performing straight trench excavation, if the bottom width of the trench is required to be greater than the width of the bucket, a geometric decomposition method is used to divide the bottom of the trench into several parallel sub-track zones and generate corresponding excavation tracks to ensure that the cross-section of the trench can be completely covered in the width direction.

[0027] like Figure 2 As shown, the trajectory planning is performed by dividing the shoveling, swirling, dragging, swirling and lifting paths into five segments;

[0028] To ensure smooth motion and a smooth trajectory curve, a 5-4-5-4-5 piecewise polynomial is used for interpolation in joint space, with the expression defined as follows:

[0029] ;

[0030] In the formula, For the first The first joint The joint angular displacement function of the segment, where In order, they are the slewing joint, boom joint, stick joint, and bucket joint. ; It is time The number of times; It is time The coefficient;

[0031] right By performing first, second, and third derivatives, we can obtain the expressions for the angular velocity, angular acceleration, and angular jerk of the corresponding joints, respectively:

[0032] ;

[0033] ;

[0034] ;

[0035] Consider joint angle variable constraints and continuity constraints for intermediate path points:

[0036] (1) The joint angular displacement constraint is:

[0037] ;

[0038] In the formula, Indicates the first The first joint The joint angle of the segment trajectory at the initial moment. Indicates the first The first joint At the end of the segment trajectory The joint angle, Indicates the first The joint in the first Joint angles at each path point;

[0039] (2) The joint angular velocity constraint is:

[0040] ;

[0041] In the formula, Indicates the first The first joint The joint angular velocity of the segment trajectory at the initial moment. Indicates the first The first joint At the end of the segment trajectory Angular velocity of the joint;

[0042] (3) The joint angular acceleration constraint is:

[0043] ;

[0044] In the formula, Indicates the first The first joint The joint angular acceleration of the segment trajectory at the initial moment. Indicates the first The first joint At the end of the segment trajectory Joint angular acceleration;

[0045] (4) The joint angular acceleration constraint is:

[0046] ;

[0047] In the formula, Indicates the first The first joint The joint angular acceleration of the trajectory segment at the initial moment. Indicates the first The first joint At the end of the segment trajectory Joint angular acceleration;

[0048] The 5-4-5-4-5 piecewise polynomial has a total of 28 coefficients. The unknown quantity, based on the above constraints, can be solved using 28 equations constructed from the formulas for the four continuity constraints mentioned above. The matrix relationship between the joint times and the polynomial coefficients is as follows:

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] Interpolation time for the 5-4-5-4-5 degree polynomial trajectory Matrix; It is a matrix of polynomial coefficients; Let be the matrix of boundary conditions and transition conditions; where, , , , , , , , It is the continuity constraint matrix for the angular displacement, angular velocity, angular acceleration, angular jerk, and fourth derivative of the trajectory in the middle segment. , Let represent the constraint matrices for the angular displacement, angular velocity, angular acceleration, and angular jerk at the initial moment of the first trajectory segment and the end moment of the last trajectory segment, respectively. , , Let be the angular displacement constraint matrix of the middle segment trajectory at the initial moment. , , , , The polynomial coefficient matrix represents each segment of the trajectory. , , Let be the boundary condition vector, and 0 be the zero matrix whose row and column numbers are determined by the adjacent matrices. Then:

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] Therefore, the first The expression for solving the coefficients of the joint interpolation polynomial is as follows:

[0077] ;

[0078] When performing sidewall trimming work, such as Figure 3 As shown, to simplify the operation scenario, the trench sidewall area is defined as a regular slope, and a top-down trimming trajectory is used for planning within this slope.

[0079] Sidewall trimming can be approximated as a slope trimming operation with rotation. Given a starting and ending point, to prevent the planned trajectory from deviating from the sidewall slope, intermediate points need to be added to constrain the trajectory and ensure flatness. Therefore, a four-segment quintic polynomial is used for interpolation. The first joint of the first... The segment trajectory interpolation expression is:

[0080] ;

[0081] In the formula, It is time The coefficient;

[0082] In addition to joint angular displacement constraints, joint angular velocity continuity constraints, joint angular acceleration continuity constraints, and joint angular jerk constraints, the four-segment fifth-order polynomial interpolation also satisfies the continuity of the fourth derivative of the joint angle at intermediate path points:

[0083] ;

[0084] In the formula, Indicates the first The first joint The fourth derivative of the joint angle of the segment trajectory at the initial moment. Indicates the first The first joint At the end of the segment trajectory The fourth derivative of the joint angle;

[0085] Similarly, the system of equations can be written in matrix form as follows:

[0086] ;

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] In the formula, , It is the continuity constraint matrix for the angular displacement, angular velocity, angular acceleration, angular jerk, and fourth derivative of the trajectory in the middle segment. , Let represent the constraint matrices for the angular displacement, angular velocity, and angular acceleration at the initial moment of the first trajectory segment and the final moment of the last trajectory segment, respectively. , , Let be the angular displacement constraint matrix of the middle segment trajectory at the initial moment;

[0094] ;

[0095] In the formula, Interpolation time for four segments of fifth-order polynomial trajectory Matrix;

[0096] ;

[0097] In the formula, This is a matrix representing the boundary conditions and transition conditions. Indicates the first The joint in the first Joint angles at each path point;

[0098] The result is the Coefficients of the joint interpolation polynomial:

[0099] .

[0100] S4. Using the interpolation time of each segment in the piecewise polynomial trajectory as variables, establish a trajectory planning model with excavation time, joint impact, and excavation energy consumption as comprehensive objectives:

[0101] To meet operational requirements in terms of efficiency, smoothness, and energy consumption, the planned trajectory needs to be optimized. Taking a 5-4-5-4-5 piecewise polynomial trajectory for straight trench excavation as an example, the interpolation time of each segment is used as the optimization variable. The straight trench excavation trajectory is divided into 5 continuous segments by 6 path points. The optimization parameter is the interpolation time of each segment, and the total time length is:

[0102] ;

[0103] In the formula, For the first A period of time;

[0104] The maximum impact of each trajectory segment is defined as:

[0105] ;

[0106] In the formula, For the first The first joint The acceleration of the segment;

[0107] Therefore, the impact optimization model is:

[0108] ;

[0109] Energy consumption of operation Energy consumption refers to the energy consumed by the excavator after completing one digging operation. It directly determines the energy-saving level of digging operations; the lower the energy consumption, the higher the energy-saving level. The expression is:

[0110] ;

[0111] In the formula, The average power of an excavator to complete one digging operation;

[0112] To avoid the impact of differences in dimensions and orders of magnitude on the optimization results, the three indicators of excavation time, joint impact, and excavation energy consumption need to be normalized first, and then a weighted sum is used to obtain the comprehensive objective function:

[0113] ;

[0114] In the formula, This is the normalized mining time. and The threshold for mining time; This represents the normalized joint impact. and The threshold for joint impact; The normalized energy consumption for excavation. and To determine the threshold for energy consumption;

[0115] In summary, the trajectory planning model with optimal time, impact, and energy consumption is as follows:

[0116] ;

[0117] In the formula, To optimize the objective function, , , These are the weighting coefficients, and ; and Indicates joint angle The threshold, In order, they are the slewing joint, boom joint, stick joint, and bucket joint; , and These represent joint angular velocity, joint angular acceleration, and joint angular jerk, respectively. The threshold; This represents the average power of the excavator in completing one digging operation. This indicates the maximum power of the drive system.

[0118] S5. Optimize the trajectory planning model using an adaptive genetic algorithm:

[0119] The model constructed in step S4 is a nonlinear model with multiple constraints, all of which exist in the form of inequalities. Given the cumbersome nature of conventional methods, this embodiment of the invention employs a heuristic algorithm for model optimization. The process of implementing the adaptive genetic algorithm is as follows: Figure 4 As shown, the specific steps are as follows:

[0120] 1) Trajectory optimization parameter set: Optimization objective function and constraints;

[0121] 2) Encoding: Individual encoding represents the solution to the problem in chromosome form. Its rationality directly affects the execution efficiency and convergence characteristics of the algorithm. In the comprehensive optimization problem of trench excavation trajectory, the core parameter involved is time. It uses real number encoding.

[0122] 3) Initialize the population: Initializing the population refers to randomly generating the population. These individuals constitute the initial population of the genetic algorithm, and they represent the initial solutions to the problem.

[0123] 4) Calculate the overall fitness of the trajectory: The fitness value of each individual is calculated using a fitness function to reflect its adaptability in the optimization problem. An individual with a higher fitness value has stronger adaptability. Let the fitness function be:

[0124] ;

[0125] In the formula, It is the fitness function; , , These are the weighting coefficients, and ; This is the normalized mining time; Normalized joint impact; This represents the normalized energy consumption for excavation.

[0126] 5) Selection for Reproduction: The selection for reproduction operation selects some superior individuals from the current population as parents based on their fitness values, using a roulette wheel selection strategy.

[0127] Individual choice probability calculation:

[0128] ;

[0129] In the formula, It is the first The probability of an individual being selected; It is the first The fitness of each individual; It refers to population size;

[0130] Cumulative probability calculation:

[0131] ;

[0132] In the formula, Indicates the preceding The cumulative choice probability of each individual;

[0133] Random sampling selection:

[0134] generate random numbers within the interval If satisfied (Regulation If so, then choose the first one. Individuals enter the next generation;

[0135] 6) Adaptive crossover: Crossover generates new individuals by exchanging partial genes between two parent individuals. A higher crossover probability in the early stages of evolution accelerates the population's evolutionary process; a lower crossover probability in the later stages helps preserve the genetic information of superior individuals. The crossover rate is dynamically adjusted using the following formula:

[0136] ;

[0137] In the formula, Indicates the first Crossover rate of individuals and These represent the minimum crossover rate and the maximum crossover rate, respectively. and These represent the minimum fitness and average fitness of the population, respectively.

[0138] 7) Adaptive Mutation Operation: Mutation operation involves modifying some genes of an individual to form a new individual. In the early stages of evolution, when there are significant differences between individuals in the population, using a lower mutation probability is beneficial for accelerating the evolutionary process. In the later stages of evolution, when there are fewer differences between individuals in the population and the population is prone to getting stuck in local optima, using a higher mutation probability is beneficial for increasing population richness and enhancing its ability to escape local optima. The mutation rate is dynamically adjusted using the following formula:

[0139] ;

[0140] In the formula, Indicates the first The rate of variation of an individual and These represent the minimum and maximum rates of variation, respectively.

[0141] 8) Elite preservation: The individual with the highest fitness in each generation is directly inherited to the next generation to avoid the loss of the best individual during crossover or mutation.

[0142] 9) Termination condition: Perform termination condition detection on the newly generated population to determine whether the optimization algorithm should terminate or continue iterating.

[0143] S6. Output the optimized trench excavation trajectory.

[0144] The method provided in the embodiments of the present invention was subjected to simulation testing, and the simulation results of the method in the embodiments of the present invention were compared with those of the traditional method, as follows:

[0145] Excavator Model: An experimental prototype model of an electric cylinder excavator, with a maximum bucket capacity of 8.8 × 10⁻⁶. 4 cm 3 ;

[0146] Simulation conditions: trench length 1.6m, trench bottom width 0.75m, slope 1:0.67;

[0147] Embodiment of the present invention: Riding-dig method + piecewise polynomial interpolation + adaptive genetic algorithm;

[0148] Traditional method: direct alignment + curve interpolation + empirical parameter control;

[0149] The simulation results are shown in Table 1:

[0150] Table 1

[0151]

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimal trajectory planning method for excavator trenching operations, characterized in that, Includes the following steps: Establish a kinematic and dynamic model of the excavator; The trenching operation methods are divided into straight trench excavation and sidewall trimming; Using a piecewise polynomial interpolation method, the straight trench excavation and sidewall trimming trajectories are planned separately in the joint space to obtain the angles, angular velocities, and angular accelerations of each joint of the excavator. By using the interpolation time of each segment in the piecewise polynomial trajectory as variables, a trajectory planning model with excavation time, joint impact, and excavation energy consumption as comprehensive objectives is established. An adaptive genetic algorithm is used to optimize the trajectory planning model; Output the optimized trench excavation trajectory.

2. The optimal trajectory planning method for excavator trenching operations according to claim 1, characterized in that, The step of using a piecewise polynomial interpolation method to plan the straight trench excavation and sidewall trimming trajectories in the joint space to obtain the angles, angular velocities, and angular accelerations of each joint of the excavator includes: Using a geometric decomposition method, the bottom of the trench for straight trench excavation is divided into several parallel sub-track zones, and five track plans are generated for shoveling, swirling, dragging, swirling and lifting respectively. Interpolation is performed in joint space using a piecewise polynomial of degree 5-4-5-4-5, and the expression is: ； In the formula, For the first The first joint The joint angular displacement function of the segment, where In order, they are the slewing joint, boom joint, stick joint, and bucket joint. ; It is time The number of times; It is time The coefficient; right By performing first, second, and third derivatives, we obtain the expressions for the angular velocity, angular acceleration, and angular jerk of the corresponding joints: ； ； ； Consider joint angle variable constraints and continuity constraints for intermediate path points: The joint angular displacement constraint is: ； In the formula, Indicates the first The first joint The joint angle of the segment trajectory at the initial moment. Indicates the first The first joint At the end of the segment trajectory The joint angle, Indicates the first The joint in the first Joint angles at each path point; The joint angular velocity constraint is: ； In the formula, Indicates the first The first joint The joint angular velocity of the segment trajectory at the initial moment. Indicates the first The first joint At the end of the segment trajectory Angular velocity of the joint; The joint angular acceleration constraint is: ； In the formula, Indicates the first The first joint The joint angular acceleration of the segment trajectory at the initial moment. Indicates the first The first joint At the end of the segment trajectory Joint angular acceleration; The joint angular acceleration constraint is: ； In the formula, Indicates the first The first joint The joint angular acceleration of the trajectory segment at the initial moment. Indicates the first The first joint At the end of the segment trajectory Joint angular acceleration; No. The matrix relationship between the joint times and the polynomial coefficients is as follows: ； ； ； ； Interpolation time for the 5-4-5-4-5 degree polynomial trajectory Matrix; It is a matrix of polynomial coefficients; Let be the matrix of boundary conditions and transition conditions; where, , , , , , , , It is the continuity constraint matrix for the angular displacement, angular velocity, angular acceleration, angular jerk, and fourth derivative of the trajectory in the middle segment. , Let represent the constraint matrices for the angular displacement, angular velocity, angular acceleration, and angular jerk at the initial moment of the first trajectory segment and the end moment of the last trajectory segment, respectively. , , Let be the angular displacement constraint matrix of the middle segment trajectory at the initial moment. , , , , The polynomial coefficient matrix represents each segment of the trajectory. , , Let be the boundary condition vector, and 0 be the zero matrix whose row and column numbers are determined by the adjacent matrices. Then: ； ； ； ； ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; Therefore, the first The expression for solving the coefficients of the joint interpolation polynomial is as follows: 。 3. The optimal trajectory planning method for excavator trenching operations according to claim 2, characterized in that, The step of using piecewise polynomial interpolation to plan the straight trench excavation and sidewall trimming trajectories in the joint space to obtain the angles, angular velocities, and angular accelerations of each joint of the excavator also includes: When performing sidewall trimming work, the trench sidewall area is defined as a regular slope, and a trimming trajectory is planned from top to bottom within the slope. Interpolation is performed using a four-segment fifth-degree polynomial. The first joint of the first... The segment trajectory interpolation expression is: ； In the formula, It is time The coefficient; In addition to joint angular displacement constraints, joint angular velocity continuity constraints, joint angular acceleration continuity constraints, and joint angular jerk constraints, the four-segment fifth-order polynomial interpolation also satisfies the continuity of the fourth derivative of the joint angle at intermediate path points: ； In the formula, Indicates the first The first joint The fourth derivative of the joint angle of the segment trajectory at the initial moment. Indicates the first The first joint At the end of the segment trajectory The fourth derivative of the joint angle; The system of equations can be written in matrix form as follows: ； ； ； ； ； ； ； In the formula, , It is the continuity constraint matrix for the angular displacement, angular velocity, angular acceleration, angular jerk, and fourth derivative of the trajectory in the middle segment. , Let represent the constraint matrices for the angular displacement, angular velocity, and angular acceleration at the initial moment of the first trajectory segment and the final moment of the last trajectory segment, respectively. , , Let be the angular displacement constraint matrix of the middle segment trajectory at the initial moment; ； In the formula, Interpolation time for four segments of fifth-order polynomial trajectory Matrix; ； In the formula, This is a matrix representing the boundary conditions and transition conditions. Indicates the first The joint in the first Joint angles at each path point; The result is the Coefficients of the joint interpolation polynomial: 。 4. The optimal trajectory planning method for excavator trenching operations according to claim 3, characterized in that, The steps for establishing a trajectory planning model with excavation time, joint impact, and excavation energy consumption as comprehensive objectives, using the interpolation time of each segment in the piecewise polynomial trajectory as variables, are as follows: The 5-4-5-4-5 piecewise polynomial trajectory based on straight trench excavation is divided into 5 continuous trajectories by 6 path points. The optimization parameter is the interpolation time of each trajectory segment, and the total time length is: ； In the formula, For the first A period of time; The maximum impact of each trajectory segment is defined as: ； In the formula, For the first The first joint The acceleration of the segment; The impact optimization model is as follows: ； Energy consumption of operation The expression is: ； In the formula, The average power of an excavator to complete one digging operation; The three indicators of excavation time, joint impact, and excavation energy consumption are normalized, and then a comprehensive objective function is obtained by weighted summation: ； In the formula, This is the normalized mining time. and The threshold for mining time; This represents the normalized joint impact. and The threshold for joint impact; The normalized energy consumption for excavation. and To determine the threshold for energy consumption; The trajectory planning model that optimizes time, impact, and energy consumption is as follows: ； In the formula, To optimize the objective function, , , These are the weighting coefficients, and ; and Indicates joint angle The threshold, In order, they are the slewing joint, boom joint, stick joint, and bucket joint; , and These represent joint angular velocity, joint angular acceleration, and joint angular jerk, respectively. The threshold; This represents the average power of the excavator in completing one digging operation. This indicates the maximum power of the drive system.

5. The optimal trajectory planning method for excavator trenching operations according to claim 4, characterized in that, The steps for optimizing the trajectory planning model using an adaptive genetic algorithm are as follows: Optimize the objective function and constraints; Based on the core parameter of time, a real number encoding method is used for encoding; Initialize the population; Calculate the overall fitness of the trajectory; Based on the fitness value of an individual, select some excellent individuals from the current population as parents, using a roulette wheel selection strategy. New individuals are generated by exchanging some genes between two parent individuals; By modifying some of an individual's genes, a new individual can be created; The individual with the highest fitness in each generation is directly inherited into the next generation. The generated new population is subjected to termination condition detection to determine whether the optimization algorithm should terminate or continue iterating.

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