An autonomous excavation operation trajectory generation method and system

Abstract

This invention provides a method and system for generating autonomous excavation operation trajectories, relating to the field of excavator technology. The method includes: analyzing the excavation movement on an excavator experimental platform by integrating the operational characteristics of skilled operators to obtain an excavation path model; representing the excavation trajectory using piecewise Bézier curves based on the excavation path model; establishing an objective function for the time-optimal trajectory generation problem based on the piecewise Bézier curves, using the time of the excavation movement as the optimization objective and continuity constraints, boundary constraints, and dynamic feasibility constraints as constraints; and reconstructing the time-optimal trajectory generation problem into a solvable second-order cone optimization problem using a discretization method, iteratively solving the second-order cone optimization problem to obtain the time-optimal excavation trajectory. This invention enables the machine to complete the task in the shortest possible time, improving the excavator's operational efficiency.

Description

Technical Field

[0001] This invention relates to the field of excavator technology, specifically to a method and system for generating autonomous excavation operation trajectories. Background Technology

[0002] Excavators are widely used and play an important role in urban and rural construction, transportation, and disaster relief. In recent years, with the development of computer and control technology, the development of autonomous excavators has received significant attention from the construction machinery industry. To date, excavator operation heavily relies on skilled operators, resulting in low excavation efficiency. In addition, operators' safety is greatly threatened by harsh and variable working environments. To address these two issues, there is an urgent need to develop autonomous excavators to achieve autonomous excavation operations. The key technology is the autonomous generation of efficient motion trajectories. The efficiency of excavation operations means that the excavation process requires optimal time allocation.

[0003] To address the problem of generating optimal digging trajectories for excavators, existing technologies disclose methods for generating digging trajectories with minimum torque through dynamic modeling of the excavator, and methods for generating minimum torque digging trajectories that satisfy dynamic constraints using piecewise polynomials. While these methods yield optimal digging trajectories, the optimal time allocation problem remains unsolved. Optimization of time-optimal digging trajectories generally includes heuristic and optimization methods. Existing technologies propose a scheme using a heuristic algorithm to allocate optimal digging time; however, for large-scale digging trajectory generation, this method has high computational costs and is difficult to meet the requirements of real-time trajectory generation.

[0004] Time optimality refers to the ability of a actuator to appropriately increase speed and acceleration, even to the point of reaching its physical limits, without violating the machine's physical limits, so that the machine can complete the task in the shortest possible time, thereby improving efficiency. Previous methods for studying this type of time optimality problem usually involve converting it into a nonlinear optimization problem while satisfying the machine's physical limits. However, these solutions do not fully utilize the actuator's performance, i.e., they cannot guarantee time optimality. In addition, the above solutions all require an initial solution, and the solution to the problem depends heavily on the selection of the initial solution, which may lead to getting trapped in local solutions and make it difficult to guarantee global optimality. Summary of the Invention

[0005] In view of this, the present invention proposes an autonomous excavation operation trajectory generation method and system to solve the technical problem of how to ensure the optimal time of the excavation trajectory in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention provides a method for generating autonomous excavation operation trajectories, comprising the following steps:

[0008] By integrating the operational characteristics of skilled operators into the excavator experimental platform, an excavation operation motion model is obtained.

[0009] Based on the aforementioned excavation path model, a piecewise Bézier curve is used to represent the excavation trajectory;

[0010] Based on the piecewise Bézier curve, the time of the excavation operation is taken as the optimization objective, and the continuity constraint, boundary constraint, and dynamic feasibility constraint are used as constraints to establish the objective function for the time-optimal trajectory generation problem.

[0011] Based on the discretization method, the problem of generating the optimal time trajectory is reconstructed into a solvable second-order cone optimization problem. The second-order cone optimization problem is solved iteratively to obtain the optimal time mining trajectory.

[0012] Preferably, the excavator test platform is equipped with a tilt sensor and an absolute encoder. The tilt sensor and the absolute encoder are used to measure the changes in the angle values ​​of the slewing joint and the boom, stick, and bucket joints during the movement of the excavator, respectively. The data is collected and processed by a computer, and finally tracked by a controller.

[0013] Preferably, the operational characteristics of the skilled operator are obtained by analyzing the topology information of the skilled operator's excavation path based on the excavation path generation rules for the excavation task.

[0014] Preferably, the excavation movement is analyzed by incorporating the operational characteristics of skilled operators on the excavator test platform to obtain an excavation path model, including:

[0015] By integrating the operational characteristics of skilled operators into the excavator experimental platform, the topological information of the trajectory in the joint space is obtained through the analysis of excavation operation motion.

[0016] The topological information of the trajectory in the joint space is processed by using a time-series signal alignment method to ensure that the data lengths in the topological information of the trajectory in the joint space are consistent.

[0017] The topological information of the trajectory in the joint space is denoised using a moving mean filter method.

[0018] The average excavation path is obtained by averaging multiple sets of joint space trajectories in the topological information of the trajectories in the joint space.

[0019] The average excavation path is transformed into pose space, and the Douglas-Peucker algorithm is used to find key path points to establish the excavation path model.

[0020] Preferably, the piecewise Bézier curve satisfies the following constraints:

[0021] Path point constraints ensure that the trajectory passes through critical path points;

[0022] Boundary value constraints set the velocity and acceleration values ​​of the excavation trajectory at the start and end points of the trajectory to meet the state requirements of the excavation operation.

[0023] Continuity constraints ensure that the order derivatives of two connected trajectory segments are continuous at the breakpoint, resulting in a smooth trajectory. This invention also provides an autonomous excavation operation trajectory generation system, comprising:

[0024] The acquisition module is used to analyze the excavation movement by integrating the operational characteristics of skilled operators on the excavator experimental platform to obtain an excavation path model.

[0025] The representation module is used to represent the excavation trajectory using piecewise Bézier curves;

[0026] A module is established to optimize the time of the excavation operation, with continuity constraints, boundary constraints, and dynamic feasibility constraints as constraints, to establish the objective function for the time-optimal trajectory generation problem.

[0027] The processing module is used to reconstruct the time-optimal trajectory generation problem into a solvable second-order cone optimization problem, and iteratively solve the second-order cone optimization problem to obtain the time-optimal mining trajectory.

[0028] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes an autonomous excavation operation trajectory generation method and system, comprising: analyzing the excavation operation movement by integrating the operation characteristics of skilled operators on an excavator experimental platform to obtain an excavation path model; representing the excavation trajectory using piecewise Bézier curves based on the excavation path model; establishing an objective function for the time-optimal trajectory generation problem based on the piecewise Bézier curves, taking the time of the excavation operation movement as the optimization objective, and using continuity constraints, boundary constraints, and dynamic feasibility constraints as constraints; and reconstructing the time-optimal trajectory generation problem into a solvable second-order cone optimization problem based on a discretization method, iteratively solving the second-order cone optimization problem to obtain the time-optimal excavation trajectory, enabling the machine to complete the operation task in the shortest time and improving the excavator's operating efficiency. Attached Figure Description

[0029] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0030] Figure 1 A flowchart illustrating an autonomous excavation operation trajectory generation method provided in an embodiment of the present invention;

[0031] Figure 2 This is a schematic diagram of excavation trajectory analysis and wayfinding points for skilled operators provided in an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of the movement state of the excavator's slewing joint provided in an embodiment of the present invention;

[0033] Figure 4 This is a schematic diagram of the movement state of the excavator boom joint provided in an embodiment of the present invention;

[0034] Figure 5 This is a schematic diagram of the movement state of the excavator boom joint provided in an embodiment of the present invention;

[0035] Figure 6 The excavator bucket joint movement state provided in the embodiments of the present invention;

[0036] Figure 7 This is a schematic diagram comparing the trajectory in the excavator's pose space provided in an embodiment of the present invention;

[0037] Figure 8 A schematic diagram of the structure of an autonomous excavation operation trajectory generation system provided in this embodiment of the invention. Detailed Implementation

[0038] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0039] The purpose of this invention is to provide an autonomous excavation operation trajectory generation method and system, which solves the technical problem of how to ensure the optimal time of the excavation trajectory in the prior art.

[0040] See Figure 1 As shown, a method for generating an autonomous excavation operation trajectory includes the following steps:

[0041] Step S101: Analyze the excavation operation movement by integrating the operational characteristics of skilled operators on the excavator experimental platform to obtain an excavation path model;

[0042] Step S102: Based on the excavation path model, the excavation trajectory is represented by a piecewise Bézier curve;

[0043] Step S103: Based on the piecewise Bézier curve, taking the time of the excavation operation as the optimization objective, and using continuity constraints, boundary constraints, and dynamic feasibility constraints as constraints, establish the objective function for the time-optimal trajectory generation problem, specifically including:

[0044] To generate motion as quickly as possible and satisfy the kinematic constraints of quadcopter, a function τ(t) is introduced to map the time-domain variable t to the imaginary-domain variable τ. The following equation describes the mapping relationship between the imaginary and time domains:

[0045]

[0046] In the formula, This represents the time series of each segment of the trajectory.

[0047] (1) Objective function

[0048] To improve the efficiency of autonomous excavator operations, it is necessary to reduce the total excavation time while satisfying the machine's dynamic constraints. Therefore, this invention uses the excavation movement time as the optimization objective. The total time required to perform the excavation task is T. S When the time is right, the optimization objective can be written in the following form:

[0049]

[0050] Solving this nonlinear optimization problem requires transforming it into a convex optimization form to obtain the global optimum without relying on initial values. Based on this, two functions are introduced. .

[0051]

[0052] Simultaneously, let:

[0053]

[0054] After the above transformation, the velocity and acceleration continuity constraints, boundary constraints, and kinematic feasibility condition constraints of the segmented trajectory can be obtained through... express.

[0055] (2) Continuity constraint:

[0056]

[0057]

[0058] (3) Boundary constraints:

[0059]

[0060]

[0061] (4) Dynamic feasibility constraints:

[0062]

[0063]

[0064] In the formula, and These are the velocity and acceleration at the start and end of the trajectory, respectively; , These are the maximum speed and maximum acceleration of the excavation motion, respectively.

[0065] Step S104: Based on the discretization method, the time-optimal trajectory generation problem is reconstructed into a solvable second-order cone optimization problem. The second-order cone optimization problem is solved iteratively to obtain the time-optimal mining trajectory.

[0066] To obtain the global optimal solution to the aforementioned optimization problem, the trajectory generation problem needs to be reconstructed into a standard second-order cone optimization form. In this invention, for each trajectory segment... Divide into M equal parts, so that , It is also discretized into , If set as piecewise constant, then Written as:

[0067]

[0068] Therefore, slack variables are introduced. and Then the objective function can be rewritten as:

[0069]

[0070] In the formula, At the same time, we have the following formula:

[0071]

[0072] The two inequalities above can be represented in the following second-order cone form:

[0073]

[0074] In the formula, , .

[0075]

[0076] in, , .

[0077] The velocity and acceleration continuity constraints of the segmented trajectory, the boundary value constraints of the mining trajectory, and the kinematic feasibility constraints of the trajectory can also be written in discrete form. Ultimately, the objective function and constraints of the original trajectory generation problem can be reconstructed into a second-order cone optimization problem:

[0078]

[0079] In the formula, continuity constraints and boundary value constraints are collectively denoted as equality constraints ( Dynamic feasibility constraints are denoted as inequality constraints. The optimization variable x is derived from... The vector formed This trajectory generation problem is a standard second-order cone optimization problem. For this type of convex optimization problem, iterative optimization can be performed in real time to obtain the globally optimal mining trajectory.

[0080] This invention provides a method for generating autonomous excavation operation trajectories, which obtains the time-optimal excavation trajectory, enabling the machine to complete the task in the shortest time and improving the operating efficiency of the excavator.

[0081] Furthermore, achieving autonomous operation of excavators relies on sensing technology, which is the foundation for autonomous operation. To this end, the present invention configures an inclination sensor and an absolute encoder on the excavator experimental platform. The inclination sensor and the absolute encoder are used to measure the changes in the angle values ​​of the slewing joint and the boom, stick, and bucket joints during the movement of the excavator, respectively. The data is collected and processed by a computer, and finally tracked by a controller.

[0082] Furthermore, the operational characteristics of the skilled operator are obtained by analyzing the topology information of the skilled operator's excavation path based on the excavation path generation rules for the excavation task.

[0083] For a given excavation task, the excavation motion requires planning the movement of the bucket teeth in the pose space; however, excavator trajectory generation is typically performed in the joint space. Therefore, it is necessary to convert the angle values ​​in the joint space to the pose space, completing the mutual conversion between the joint space and the pose space. The DH parameters are shown in Table 1, where the angles in the swing, boom, stick, and bucket joint spaces are... Then the position space coordinates of the bucket tooth tip It can be obtained through the following formula:

[0084]

[0085] In the formula, , , , These represent the connecting rod lengths of the swing, boom, stick, and bucket joint shafts, respectively. express arrive Linkage offset between shafts.

[0086] Table 1 Parameters of SWE50E Excavator Robot DH

[0087]

[0088] Before analyzing the topology information of the excavation path of skilled operators, it is necessary to establish the excavation path generation rules for skilled operators for trenching tasks (trapezoidal trenches). For trenching, the complete operation process can be divided into five stages: bucket tooth tip insertion, bucket dragging, bucket lifting, rotation to unloading point, and rotation to digging point.

[0089] The analysis of the excavation rules comes from the excavation trajectories formed by skilled drivers (with over 10 years of excavation experience) through multiple trench excavations. First, in the preparation excavation stage, the starting position of the bucket is selected as a location where the bucket can easily move towards the starting point of the excavation. The initial posture of the bucket is such that the angle between the bucket and the ground is in the direction with the least cutting resistance (usually the angle between the bucket and the ground is 30° to 60°). Second, in the bucket dragging stage, the bucket is kept full of excavated material and dragged horizontally quickly. Third, in the bucket lifting stage, after completing step two, the bucket is lifted and rotated to maintain the excavated material, preventing it from falling out of the bucket. Fourth, in the rotation to the unloading stage, the bucket is lifted while maintaining the excavation action and quickly rotated to the unloading point (in this invention, it is set to rotate 90° counterclockwise). As the bucket approaches the unloading point, it decelerates and unloads the soil. Finally, during the rotation to the initial excavation point stage, the working device maintains the state of the previous stage and adjusts to the first stage posture when approaching the excavation point.

[0090] Furthermore, by integrating the operational characteristics of skilled operators into the excavator experimental platform, an excavation movement model was obtained, including:

[0091] By integrating the operational characteristics of skilled operators into the excavator experimental platform, the topological information of the trajectory in the joint space is obtained through the analysis of excavation operation motion.

[0092] The topological information of the trajectory in the joint space is processed by using a time-series signal alignment method to ensure that the data lengths in the topological information of the trajectory in the joint space are consistent.

[0093] The topological information of the trajectory in the joint space is denoised using a moving mean filter method.

[0094] The average excavation path is obtained by averaging multiple sets of joint space trajectories in the topological information of the trajectories in the joint space.

[0095] The average excavation path is transformed into pose space, and the Douglas-Peucker algorithm is used to find key path points to establish the excavation path model.

[0096] After generating the skilled operator's trajectory through the above process, we need to analyze the topological information of the trajectory in the joint space, such as... Figure 2 As shown, firstly, the data is processed using time-series signal alignment to ensure consistent data length. This is because during operation, multiple sets of tests need to be conducted for a specific excavation task, and operators cannot guarantee that the starting state of each test is completely consistent. Secondly, moving average filtering is used to reduce the interference of noise signals on the excavator trajectory generation. This is because the hydraulic cylinders of the excavator will experience a certain degree of impact during operation, generating corresponding noise interference. Finally, the statistical average excavation path is obtained by averaging multiple sets of trajectories for the excavation paths of skilled operators.

[0097] Since the excavation task requires path planning in the pose space, we need to transform the joint space trajectory obtained above into the pose space for processing. In addition, given that the excavation operation paths obtained by skilled operators are extremely poor, with their geometry and time distribution being far from optimal, they are useless or even harmful for trajectory optimization. However, the topological information of the manually excavated path is essential because it reflects human intent (i.e., the fastest excavation and smooth operation). In order to preserve the topological information of the found path and improve the efficiency and quality of the generated trajectory, this invention proposes an intelligent path point selection strategy to generate sparse excavation paths. The critical path points obtained in this way provide a great degree of freedom for trajectory optimization. The path point selection strategy of this invention borrows an idea from the Douglas-Peucker algorithm. Once all path points are found, we can generate a trajectory that satisfies the dynamic constraints of the optimal trajectory.

[0098] Furthermore, the excavation trajectory needs to pass through the critical path point p, so a series of curves are needed to connect these critical path points. This invention uses Bézier curves to represent the excavation trajectory. An n-order Bézier curve can guarantee the continuity of the n-1 order trajectory and has good convex hull properties, which is beneficial for subsequent optimization solutions. However, the order of the curve is proportional to the number of control points.

[0099] The nth-order piecewise Bézier curve parameterized by τ can be represented in the following form:

[0100]

[0101] In the formula, This represents the j-th control point on the i-th segment of the Bézier curve. Represents the number of combinations, virtual parameter To ensure the smoothness of the generated time-optimal trajectory, this invention selects the order n of the piecewise Bézier curve as 5, and the piecewise Bézier curve must satisfy the following constraints:

[0102] (1) Path point constraint: ensure that the trajectory passes through critical path points.

[0103] (2) Boundary value constraints: Set the velocity and acceleration values ​​of the excavation operation trajectory at the start and end points of the trajectory to meet the state requirements of the excavation operation.

[0104] (3) Continuity constraint: ensure that the n-1th derivative of two connected trajectories is continuous at the breakpoint, so as to obtain a smooth trajectory.

[0105] like Figure 8 As shown, the present invention also provides an autonomous excavation operation trajectory generation system 12, comprising:

[0106] The acquisition module 1201 is used to analyze the excavation operation movement by integrating the working characteristics of skilled operators on the excavator experimental platform to obtain an excavation path model.

[0107] Module 1202 is used to represent the excavation trajectory using piecewise Bézier curves;

[0108] Module 1203 is established to take the time of the excavation operation as the optimization objective and use continuity constraints, boundary constraints, and dynamic feasibility constraints as constraints to establish the objective function for the time-optimal trajectory generation problem.

[0109] The processing module 1204 is used to reconstruct the time-optimal trajectory generation problem into a solvable second-order cone optimization problem, and to iteratively solve the second-order cone optimization problem to obtain the time-optimal mining trajectory.

[0110] This invention provides an autonomous excavation trajectory generation system that obtains the time-optimal excavation trajectory, enabling the machine to complete the task in the shortest time and improving the excavator's operating efficiency.

[0111] To verify the feasibility of the scheme, this paper compares and analyzes the simulation results with measured data. The measured data includes the angle values ​​of each joint during the trench digging, rotation, tilting, and return processes of the excavator. The above digging operation process is repeated for 7 cycles, which is considered one digging cycle. With the excavator's engine speed at 1500 rpm, a total of 15 digging operations were performed for analysis and comparison to ensure the validity of the measured data. For ease of comparison, this paper only analyzes one digging cycle. The kinematic feasibility constraints of each joint are shown in Table 2.

[0112] Table 2 Dynamic feasibility constraints of the working device

[0113]

[0114] like Figures 3-6 The curves showing the changes in angle, velocity, and acceleration of each joint during excavation are presented. It can be seen that the method proposed in this paper (red dashed line) takes 10.7s to complete one excavation cycle, which is less than the measured time (blue solid line) of 11.0s. This indicates that the method proposed in this paper can make full use of the physical limits of the actuator to generate the excavation trajectory with the minimum time.

[0115] To verify the feasibility of the scheme, the characteristics of the trajectories of the two in the pose space were analyzed, such as... Figure 7 As shown, the two trajectories are generally similar, but the optimized trajectory (dashed line) shows significant local changes. This may be because when performing the excavation task at this power, the excavator's performance is fully utilized, and the speed and acceleration reach their limits at this moment, causing the excavator to generate a large impact and resulting in local abrupt changes in the trajectory.

[0116] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of this invention. Therefore, if these modifications and variations of this invention fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for generating autonomous excavation operation trajectories, characterized in that, Includes the following steps: This paper analyzes the excavation motion of a skilled operator on an excavator test platform by integrating their operational characteristics to obtain an excavation path model. The process includes: analyzing the excavation motion of a skilled operator on the excavator test platform to obtain the topological information of the trajectory in joint space; processing the topological information of the trajectory in joint space using a time-series signal alignment method to ensure consistent data lengths; using a moving average filtering method to denoise the topological information of the trajectory in joint space; averaging multiple sets of joint space trajectories in the topological information of the trajectory in joint space to obtain the average excavation path; transforming the average excavation path into pose space; using the Douglas-Peucker algorithm to find key path points; and establishing the excavation path model. Based on the aforementioned excavation path model, a piecewise Bézier curve is used to represent the excavation trajectory; Based on the piecewise Bézier curve, the time of the excavation operation is taken as the optimization objective, and the continuity constraint, boundary constraint, and dynamic feasibility constraint are used as constraints to establish the objective function for the time-optimal trajectory generation problem. Based on the discretization method, the problem of generating the optimal time trajectory is reconstructed into a solvable second-order cone optimization problem. The second-order cone optimization problem is solved iteratively to obtain the optimal time mining trajectory.

2. The method for generating autonomous excavation operation trajectories according to claim 1, characterized in that, The excavator test platform is equipped with a tilt sensor and an absolute encoder. The tilt sensor and the absolute encoder are used to measure the changes in the angle values ​​of the slewing joint and the boom, stick, and bucket joints during the movement of the excavator, respectively. The data is collected and processed by a computer, and finally tracked by a controller.

3. The method for generating autonomous excavation operation trajectories according to claim 1, characterized in that, The operational characteristics of the skilled operator are obtained by analyzing the topology information of the skilled operator's excavation path based on the excavation path generation rules for the excavation task.

4. The method for generating autonomous excavation operation trajectories according to claim 1, characterized in that, The piecewise Bézier curve satisfies the following constraints: Path point constraints ensure that the trajectory passes through critical path points; Boundary value constraints set the velocity and acceleration values ​​of the excavation trajectory at the start and end points of the trajectory to meet the state requirements of the excavation operation. The continuity constraint ensures that the order derivatives of two connected trajectory segments are continuous at the breakpoint, resulting in a smooth trajectory.

5. An autonomous excavation operation trajectory generation system, applied to the autonomous excavation operation trajectory generation method according to any one of claims 1-4, characterized in that, include: The acquisition module is used to analyze the excavation movement by integrating the operational characteristics of skilled operators on the excavator experimental platform to obtain an excavation path model. The representation module is used to represent the excavation trajectory using piecewise Bézier curves; A module is established to optimize the time of the excavation operation, with continuity constraints, boundary constraints, and dynamic feasibility constraints as constraints, to establish the objective function for the time-optimal trajectory generation problem. The processing module is used to reconstruct the time-optimal trajectory generation problem into a solvable second-order cone optimization problem, and iteratively solve the second-order cone optimization problem to obtain the time-optimal mining trajectory.

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