阅读说明：本技术 一种挖掘机自动作业控制方法和装置 (Automatic operation control method and device for excavator ) 是由 高娇 杨超 艾云峰 徐标 于 2021-07-19 设计创作，主要内容包括：本发明公开一种挖掘机自动作业控制方法和装置,方法包括：根据导航定位信号计算挖掘机铲斗的实时作业位置；根据挖掘机所处作业区域的环境信息以及实时作业位置,建立挖掘机作业环境电子地图；根据挖掘机动作执行机构的运行状态信息进行挖掘机的姿态计算；根据所述挖掘机作业环境电子地图以及姿态计算结果,进行挖掘作业任务规划,得到作业任务过程控制参数；基于所述作业任务过程控制参数,输出用于控制挖掘机动作执行机构的控制指令。本发明能够实现挖掘机的全过程自动作业,提升挖掘机的作业效率和作业质量。(The invention discloses an automatic operation control method and device for an excavator, wherein the method comprises the following steps: calculating the real-time operation position of the excavator bucket according to the navigation positioning signal; establishing an excavator working environment electronic map according to the environment information of the working area where the excavator is located and the real-time working position; calculating the attitude of the excavator according to the running state information of the excavator action executing mechanism; according to the excavator working environment electronic map and the attitude calculation result, carrying out excavation working task planning to obtain working task process control parameters; and outputting a control instruction for controlling the action executing mechanism of the excavator based on the work task process control parameter. The invention can realize the automatic operation of the whole process of the excavator and improve the operation efficiency and the operation quality of the excavator.)

1. An automatic operation control method for an excavator is characterized by comprising the following steps:

acquiring a navigation positioning signal, and calculating the real-time operation position of the excavator bucket according to the navigation positioning signal;

acquiring environmental information of an operation area where the excavator is located, and establishing an excavator operation environment electronic map according to the environmental information and the real-time operation position;

acquiring running state information of an excavator action executing mechanism in real time, and calculating the attitude of the excavator according to the running state information;

according to the excavator working environment electronic map and the attitude calculation result, carrying out excavation working task planning to obtain working task process control parameters;

and outputting a control instruction for controlling the action executing mechanism of the excavator based on the work task process control parameter.

2. The method of claim 1, wherein the navigational positioning signals comprise positioning signals obtained from one or more of a differential GPS high precision positioning system mounted on the excavator bucket, an inertial navigation unit;

the method further comprises the following steps: and calculating the movement speed of the excavator bucket according to the acquired navigation positioning signal.

3. The method of claim 1, wherein the environmental information comprises environmental information obtained from one or more of a vision sensor mounted on the excavator bucket, a lidar, a millimeter wave radar;

the visual sensors are arranged at the front part and the rear part of the cab roof of the excavator and the left side and the right side of the vehicle body and are used for acquiring the surrounding environment information of the vehicle body; the laser radar is arranged at the top of the cab of the excavator and is used for acquiring 360-degree environment information of a working environment; the millimeter wave radar is arranged at the front part, the rear part, the left part and the right part of the lower vehicle body and is used for collecting the information of obstacles around the vehicle body.

4. The method of claim 1, wherein the operation state information includes detection signals obtained from an angle sensor, a cylinder displacement sensor, a gyroscope installed on the excavator;

the performing attitude calculation of the excavator comprises: calculating the posture of a mechanism at the installation position of the sensor in real time according to detection signals of the angle sensor and the oil cylinder displacement sensor, and converting the posture to bucket teeth to obtain the posture information of the bucket teeth; and determining the real-time attitude of the rotation center by using the detection signal of the gyroscope.

5. The method of claim 1, wherein said performing a task plan for a mining operation comprises:

determining an excavation point to be excavated in response to the completion of the unloading action of the excavator or the new excavation operation to be executed;

planning a path for the bucket to move from a real-time position to an excavation point position, and obtaining a control parameter sequence corresponding to each actuating mechanism in the excavation process according to the planned path, wherein the path can avoid the obstacle when the movable arm and the bucket rod are lifted from the real-time position to the excavation point position in the rotation process of the rotation center;

determining an unloading point in response to the completion of the excavating action of the excavator;

planning a path of the bucket moving from the real-time position to the unloading point position, and obtaining a control parameter sequence corresponding to each actuating mechanism in the unloading process according to the planned path, wherein the path can avoid the obstacle when the movable arm and the bucket rod are lifted from the real-time position to the unloading point position in the rotation process of the rotation center.

6. The method of claim 5, wherein the method of determining the excavation point comprises:

dispersing a working face to be excavated into uniform grids;

taking the position of the operation surface after the final excavation as a current excavation point, searching adjacent grid points in 8 directions around the grid point corresponding to the current excavation point, and respectively calculating the excavation point selection cost f1 according to the following cost functions for each adjacent grid point:

f1＝g+d+e

in the formula, g represents the Euclidean distance from the adjacent grid point to the grid point corresponding to the initial digging point; d represents the recorded excavation depth of the adjacent grid point, and e represents the Euclidean distance from the adjacent grid point to the current excavation point;

the work surface position corresponding to the adjacent grid point with the minimum cost f1 is selected as the next excavation point.

7. The method as claimed in claim 6, wherein for each grid, the excavation depth d is initialized to 0.0, and the excavation depth d is updated each time the excavation is performed according to the depth change gradient Δ d of 1.0_t+1＝d_t+ Δ d ofIn d_tIndicates the depth after the t-th excavation, d_t+1Represents the depth after the t +1 th excavation;

when the excavation point is selected, only the real-time d value is less than the preset maximum depth d_maxThe cost function is calculated for the grid points of (1).

8. The method of claim 5, wherein planning a path that avoids obstacles when the boom and stick are lifted from the real-time position to the unloading point position during the rotation of the swing center comprises:

acquiring a rotation angle of a rotation center, a rotation angle of a movable arm relative to a horizontal plane of a vehicle body, a rotation angle of an arm relative to the movable arm, a rotation angle of a bucket relative to the arm, a distance from the rotation center to a movable arm pin shaft, a distance from a movable arm pin shaft to an arm pin shaft, a distance from the arm pin shaft to a bucket pin shaft and a distance from the bucket pin shaft to a tooth tip of a bucket tooth;

acquiring position information of an obstacle;

based on the acquired information, selecting an optimal configuration which enables the cost value to be minimum and does not collide in a pre-constructed arm support and bucket rod configuration space by using a preset second cost function, and determining a rotation angle of the arm support and the bucket rod in the rotation process of the rotation center according to the optimal configuration.

9. The method of claim 8, wherein the sampled mathematical model of the configuration space of the excavator comprises a model of the excavator body center of rotation O₁The movable arm winds the rotating shaft center O of the vehicle body₂The bucket rod winds the rotating shaft center O of the movable arm₃The bucket is wound around the shaft center O of the bucket rod₄Bucket tooth O₅Establishing four independent coordinate systems (X) for the origin₁、Y₁、Z₁)、(X₂、Y₂、Z₂)、(X₃、Y₃、Z₃)、(X₄、Y₄、Z₄)、(X₅、Y₅、Z₅)；

By X_i＝[θ_2ei，θ_3ei]^TShowing any one of the configuration spaces of the arm support and the bucket rodConfiguration of the sampling points, wherein_2ei、θ_3eiRespectively representing the rotation angles of the movable arm and the arm in the configuration;

the second cost function is expressed as:

f2＝c_risk+c_ref

in the above formula, f2 is the cost value, c_riskIndicating the risk of collision with an obstacle, c_refRepresenting the configuration of the sampling point relative to the desired configuration X_e＝[θ_2e，θ_3e]^TThe expected configuration corresponds to the poses of the boom and the arm when the excavator rotates to the target position;

the method for selecting the optimal configuration which enables the cost value to be minimum and does not collide in the pre-constructed boom and bucket arm configuration space by using the preset second cost function comprises the following steps:

simulating rotation by a set angle, and calculating c according to the configuration of the sampling point in the configuration space and the position information of the obstacle_riskCalculating c from the sample point configuration and the desired configuration_refAnd obtaining the configuration of the sampling point which is in the configuration space and enables f2 to be minimum and not to collide, and taking the configuration as the optimal configuration.

10. The method of claim 9, wherein c is calculated according to the configuration of the sampling point in the configuration space and the position information of the obstacle_riskCalculating a collision check result corresponding to the configuration of the sampling point in the simulated rotation process by using a preset collision check algorithm, and determining c according to the collision check result by using the following formula_riskThe value of (c):

in the above formula, w_riskRepresenting the calculated weight of the collision risk in the second cost.

11. The method of claim 9, wherein c is calculated based on the sample point configuration and the desired configuration_refCalculated using the following formula:

c_ref＝w_ref||X_i-X_e||

in the formula, w_refRepresenting the weight in the cost calculation of the deviation between the sampled configuration and the desired configuration.

12. The method as claimed in claim 8, wherein the sampling point configuration is iteratively selected twice at most in a set sampling step length from the desired configuration to the peripheral direction in the configuration space, the optimal configuration is output if the optimal configuration exists in the sampling point configuration obtained by the first iteration, the second iteration selection is performed if the optimal configuration does not exist, the optimal configuration is output if the optimal configuration exists in the sampling point configuration obtained by the second iteration, if the optimal configuration does not exist, the sampling point configuration iteration selection and the second cost function calculation are performed again after the simulated rotation angle is changed until the optimal configuration is obtained, or the optimal configuration is determined to not exist until the simulated rotation angle is smaller than a preset minimum rotation angle.

13. The method as claimed in claim 12, wherein the simulated rotation angle of the rotation center is initialized to α as the initial value during the path planning₀＝θ_1e-θ_1startWherein theta_1eIs the angle of revolution, theta, of the desired configuration of the centre of revolution_1startThe rotation angle of the rotation center when a new working cycle is started to carry out excavation is obtained;

when the second cost function calculation cannot obtain the optimal boom and bucket rod configuration, the change simulation rotation angle is changed according to the following formula:

in the formula, alpha_i、α_i+1Respectively representing the simulated rotation angles before and after the change;

the preset minimum rotation angle is

14. An automatic operation control device for an excavator, comprising:

the real-time operation position determining module is configured for acquiring the navigation positioning signal and calculating the real-time operation position of the excavator bucket according to the navigation positioning signal;

the working environment determining module is configured for acquiring environmental information of a working area where the excavator is located and establishing an excavator working environment electronic map according to the environmental information and the real-time working position;

the attitude calculation module is configured for acquiring the running state information of the excavator action executing mechanism in real time and calculating the attitude of the excavator according to the running state information;

the operation task planning module is configured for planning an excavation operation task according to the excavator operation environment electronic map and the attitude calculation result to obtain an operation task process control parameter;

and the control output module is configured for outputting a control instruction for controlling the excavator action executing mechanism based on the work task process control parameter.

Technical Field

The invention relates to the technical field of excavator control, in particular to an automatic operation control method and device for an excavator.

Background

Excavators are widely used in various construction fields such as mining, demolition of buildings, and construction of roads and bridges. The operation of the existing excavator is mainly manual operation, the construction operation efficiency and quality can be different due to the level of operators, and the unified requirement is difficult to achieve, so that the construction efficiency and the construction precision of the excavator are influenced. In addition, the working environment in some special industries is extremely harsh, such as: toxic gas and waste gas occasions, garbage cleaning, emergency rescue and relief work, tunnel excavation, explosion-proof operation, radioactive occasions and the like, and the severe operating environments bring great difficulty and inconvenience to operating personnel, can cause injury to human bodies and are not suitable for on-site operation. Therefore, it is a demand of many users to develop an excavator capable of remote control and even unmanned automatic operation.

The existing excavator has the following automatic or semi-automatic operation modes:

1. before each autonomous movement operation, a travel path mark which can be identified by the excavator is marked on the operation ground through the vision sensor, and after each preset number of working cycles is completed, the travel path is identified through the vision sensor, and the relative direction of the lower mechanism and the travel path is determined again. On one hand, the method is time-consuming, and on the other hand, in a dusty operation environment, the identification effect of the visual sensor is influenced, so that the quality of construction operation is influenced;

2. a person operates a recording button to record the track of the digging action, so that a semi-automatic operation mode is realized. This mode of operation does not completely liberate the operator;

3. the method comprises the steps of obtaining an image comprising a target digging point through a vision sensor, carrying out feature analysis on the image, identifying the target digging point, and calculating through image processing to obtain the relative position of the target digging point. In the dusty working environment, the recognition effect of the visual sensor in the realization mode is to be examined.

Disclosure of Invention

The invention aims to provide an automatic operation control method and device for an excavator, which can realize the automatic operation of the excavator in the whole process and improve the operation efficiency and the operation quality of the excavator. The technical scheme adopted by the invention is as follows.

In one aspect, the present invention provides an excavator automatic operation control method, including:

acquiring a navigation positioning signal, and calculating the real-time operation position of the excavator bucket according to the navigation positioning signal;

acquiring environmental information of an operation area where the excavator is located, and establishing an excavator operation environment electronic map according to the environmental information and the real-time operation position;

acquiring running state information of an excavator action executing mechanism in real time, and calculating the attitude of the excavator according to the running state information;

according to the excavator working environment electronic map and the attitude calculation result, carrying out excavation working task planning to obtain working task process control parameters;

and outputting a control instruction for controlling the action executing mechanism of the excavator based on the work task process control parameter.

Optionally, the navigation positioning signal comprises a positioning signal obtained from one or more of a differential GPS high precision positioning system mounted on the excavator bucket and an inertial navigation unit,

the method further comprises the following steps: and calculating the movement speed of the excavator bucket according to the acquired navigation positioning signal. The calculation of the movement speed can be used for realizing feedback control and improving the control precision. The real-time operation position of the excavator bucket is mainly obtained by positioning through a differential GPS high-precision positioning system.

Optionally, the environment information includes environment information obtained from one or more of a vision sensor, a laser radar, and a millimeter wave radar installed on the excavator bucket;

the visual sensors are arranged at the front part and the rear part of the cab roof of the excavator and the left side and the right side of the vehicle body and are used for acquiring the surrounding environment information of the vehicle body; the laser radar is arranged at the top of the cab of the excavator and is used for acquiring 360-degree environment information of a working environment; the millimeter wave radar is arranged at the front part, the rear part, the left part and the right part of the lower vehicle body and is used for collecting the information of obstacles around the vehicle body.

Optionally, the operation state information includes detection signals obtained from an angle sensor, an oil cylinder displacement sensor and a gyroscope installed on the excavator;

the performing attitude calculation of the excavator comprises: calculating the posture of a mechanism at the installation position of the sensor in real time according to detection signals of the angle sensor and the oil cylinder displacement sensor, and converting the posture to bucket teeth to obtain the posture information of the bucket teeth; and determining the real-time attitude of the rotation center by using the detection signal of the gyroscope. And obtaining a posture calculation result comprising the real-time posture data of the movable arm, the bucket rod, the bucket and the rotation center.

Optionally, the performing excavation task planning includes:

determining an excavation point to be excavated in response to the completion of the unloading action of the excavator or the new excavation operation to be executed;

planning a path for the bucket to move from a real-time position to an excavation point position, and obtaining a control parameter sequence corresponding to each actuating mechanism in the excavation process according to the planned path, wherein the path can avoid the obstacle when the movable arm and the bucket rod are lifted from the real-time position to the excavation point position in the rotation process of the rotation center;

determining an unloading point in response to the completion of the excavating action of the excavator;

planning a path of the bucket moving from the real-time position to the unloading point position, and obtaining a control parameter sequence corresponding to each actuating mechanism in the unloading process according to the planned path, wherein the path can avoid the obstacle when the movable arm and the bucket rod are lifted from the real-time position to the unloading point position in the rotation process of the rotation center.

Optionally, the method for determining the excavation point includes:

dispersing a working face to be excavated into uniform grids;

taking the position of the operation surface after the final excavation as a current excavation point, searching adjacent grid points in 8 directions around the grid point corresponding to the current excavation point, and respectively calculating the excavation point selection cost f1 according to the following cost functions for each adjacent grid point:

f1＝g+d+e

in the formula, g represents the Euclidean distance from the adjacent grid point to the grid point corresponding to the initial digging point; d represents the recorded excavation depth of the adjacent grid point, and e represents the Euclidean distance from the adjacent grid point to the current excavation point;

the work surface position corresponding to the adjacent grid point with the minimum cost f1 is selected as the next excavation point.

The initial digging point can be preset according to the working position of the excavator bucket, and then the subsequent digging point is determined according to the digging point determination method.

Optionally, for each grid, the excavation depth d is initialized to 0.0, and in the excavation process, the excavation depth d is updated to 1.0 according to the depth change gradient Δ d for each excavation_t+1＝d_t+ Δ d, wherein d_tIndicates the depth after the t-th excavation, d_t+1Represents the depth after the t +1 th excavation;

when the excavation point is selected, only the real-time d value is less than the preset maximum depth d_maxThe cost function is calculated for the grid points of (1). d_maxThe setting of the values can be determined according to the operation requirements.

The scheme can realize that once a grid point is expanded, the d value updates the record according to the actual excavation depth, and if the d value reaches the maximum excavation depth d_maxThen this node will no longer be selected as the new mining point at this point. Due to the arrangement of the cost item d, points which are not excavated can be preferentially selected in each excavation point selection, and the full bucket rate of each excavation can be relatively high. The matching of the cost items g, d and e also ensures that the selection process of the excavation points does not leak through each operation point, and ensures that the final excavation can reach the preset excavation depth. The grid resolution of the work surface is empirically set to be a grid of 1m × 1m in size.

Optionally, during route planning, when planning the gyration center gyration, movable arm and dipper can avoid the route of barrier when lifting to the uninstallation point position from real-time position, include:

acquiring a rotation angle of a rotation center, a rotation angle of a movable arm relative to a horizontal plane of a vehicle body, a rotation angle of an arm relative to the movable arm, a rotation angle of a bucket relative to the arm, a distance from the rotation center to a movable arm pin shaft, a distance from a movable arm pin shaft to an arm pin shaft, a distance from the arm pin shaft to a bucket pin shaft and a distance from the bucket pin shaft to a tooth tip of a bucket tooth;

acquiring position information of an obstacle;

based on the acquired information, selecting an optimal configuration which enables the cost value to be minimum in a pre-constructed arm support and bucket rod configuration space by using a preset second cost function, and determining the rotation angle of the arm support and the bucket rod in the rotation process of the rotation center according to the optimal configuration.

Optionally, the sampling mathematical model of the configuration space of the excavator comprises a rotation center O of an excavator body₁The movable arm winds the rotating shaft center O of the vehicle body₂The bucket rod winds the rotating shaft center O of the movable arm₃The bucket is wound around the shaft center O of the bucket rod₄Bucket tooth O₅Establishing four independent coordinate systems (X) for the origin₁、Y₁、Z₁)、(X₂、Y₂、Z₂)、(X₃、Y₃、Z₃)、(X₄、Y₄、Z₄)、(X₅、Y₅、Z₅)；

By X_i＝[θ_2ei，θ_3ei]^TRepresenting the configuration of any sampling point in the boom and the bucket rod configuration space, wherein theta_2ei、θ_3eiRespectively representing the rotation angles of the movable arm and the arm in the configuration;

the second cost function is expressed as:

f2＝c_risk+c_ref

in the above formula, f2 is the cost value, c_riskIndicating the risk of collision with an obstacle, c_refRepresenting the configuration of the sampling point relative to the desired configuration X_e＝[θ_2e，θ_3e]^TThe expected configuration corresponds to the poses of the boom and the arm when the excavator rotates to the target position;

the selecting the optimal configuration which enables the cost value to be minimum in the pre-constructed boom arm configuration space by using the preset second cost function comprises the following steps:

simulating rotation by a set angleC is calculated according to the sampling point configuration in the configuration space and the position information of the obstacle_riskCalculating c from the sample point configuration and the desired configuration_refTo obtain a configuration space such that₂And taking the minimum sampling point configuration which cannot collide as the optimal configuration.

Optionally, c is calculated according to the configuration of the sampling point in the configuration space and the position information of the obstacle_riskCalculating a collision check result corresponding to the configuration of the sampling point in the simulated rotation process by using a preset collision check algorithm, and determining c according to the collision check result by using the following formula_riskThe value of (c):

in the above formula, w_riskThe calculated weight of the collision risk in the second cost is represented, the higher the value is, the greater the proportion of the collision risk cost item in the total cost is, and the lower the value is, the collision risk cost item c is represented_riskThe smaller the specific gravity of (a). The principle of the collision check algorithm is that assuming that a set angle is rotated, the boom and the bucket rod change to corresponding poses according to the sampling configuration, and the collision risk between the working device and the obstacle in the process is calculated, and the prior art can be specifically referred to.

Optionally, c is calculated from the sample point configuration and the desired configuration_refCalculated using the following formula:

c_ref＝w_ref||X_i-X_e||

in the formula, w_refRepresenting the weight of the deviation between the sampled configuration and the desired configuration in the cost calculation, the greater the value, the more the deviation cost term c is stated_refThe higher the specific gravity in the total cost.

According to the scheme, the invention can adjust w_refAnd w_riskTwo coefficients to achieve a balance of the collision risk cost term and the desired configuration deviation term.

Optionally, the sampling point configuration is iteratively selected twice at most in a set sampling step length from the expected configuration to the peripheral direction in the configuration space, if an optimal configuration exists in the sampling point configuration obtained by the first iteration, the optimal configuration is output, if the optimal configuration does not exist in the sampling point configuration obtained by the second iteration, the sampling point configuration iterative selection and the second cost function calculation are performed again after the simulated rotation angle is changed until the optimal configuration is obtained, or until the simulated rotation angle is smaller than a preset minimum rotation angle, the optimal configuration is determined to not exist. The fact that the optimal configuration does not exist means that collision cannot be avoided no matter how the obstacle revolves, and warning information can be output to remind field personnel to process the obstacle and then conduct construction.

Optionally, during path planning, the initial value of the simulated rotation angle of the rotation center is α₀＝θ_1e-θ_1startWherein theta_1eIs the angle of revolution, theta, of the desired configuration of the centre of revolution_1startThe rotation angle of the rotation center during excavation in one working cycle is the rotation angle from the initial position or the last cycle end position to the new cycle excavation point position;

when the second cost function calculation cannot obtain the optimal boom and bucket rod configuration, the change simulation rotation angle is changed according to the following formula:

in the formula, alpha_i、α_i+1Respectively representing the simulated rotation angles before and after the change;

the preset minimum rotation angle is

In a second aspect, the present invention provides an automatic work control device for an excavator, comprising:

the real-time operation position determining module is configured for acquiring the navigation positioning signal and calculating the real-time operation position of the excavator bucket according to the navigation positioning signal;

the working environment determining module is configured for acquiring environmental information of a working area where the excavator is located and establishing an excavator working environment electronic map according to the environmental information and the real-time working position;

the attitude calculation module is configured for acquiring the running state information of the excavator action executing mechanism in real time and calculating the attitude of the excavator according to the running state information;

the operation task planning module is configured for planning an excavation operation task according to the excavator operation environment electronic map and the attitude calculation result to obtain an operation task process control parameter;

and the control output module is configured for outputting a control instruction for controlling the excavator action executing mechanism based on the work task process control parameter.

Advantageous effects

The automatic operation control method of the excavator can realize full-automatic excavation operation, frees operators from dangerous working environment, and effectively ensures the safety of the operators.

Through the design of a dynamic obstacle avoidance algorithm in the rotation process, the collision between the robot and an obstacle can be effectively avoided, and the construction safety of the operation equipment is guaranteed.

The invention can also utilize the neural network technology to absorb the experience data of skilled operators, realize automatic control parameter calculation and better improve the working efficiency.

Drawings

FIG. 1 is a block diagram of an embodiment of an automatic work control system for an excavator;

FIG. 2 is a schematic view of a work surface grid division;

FIG. 3 is a schematic diagram illustrating a manner of expanding each grid cost value record and child node of a working plane;

FIG. 4 is a flow chart illustrating an exemplary method of controlling automatic operation of an excavator;

FIG. 5 is a kinematic diagram of an excavator action actuator;

FIG. 6 is a schematic diagram illustrating a sampling principle of configuration space;

fig. 7 is a schematic diagram of an algorithm flow for finding a feasible solution for discrete numerical optimization.

Detailed Description

The following further description is made in conjunction with the accompanying drawings and the specific embodiments.

Example 1

The embodiment introduces an automatic operation control method for an excavator, which can be executed by a control center of the excavator, and the method specifically comprises the following steps:

acquiring a navigation positioning signal, and calculating the real-time operation position of the excavator bucket according to the navigation positioning signal;

acquiring environmental information of an operation area where the excavator is located, and establishing an excavator operation environment electronic map according to the environmental information and the real-time operation position;

acquiring running state information of an excavator action executing mechanism in real time, and calculating the attitude of the excavator according to the running state information;

according to the excavator working environment electronic map and the attitude calculation result, carrying out excavation working task planning to obtain working task process control parameters;

and outputting a control instruction for controlling the action executing mechanism of the excavator based on the work task process control parameter.

Examples 1 to 1

The method of embodiment 1 can be specifically implemented in an excavator automatic operation system of this embodiment, which includes a navigation positioning mechanism, a pose detection mechanism, an environment detection mechanism, an excavator action execution mechanism, and an excavator automatic operation control device, and the excavator automatic operation control device executes the method of embodiment 1, with reference to fig. 1, and the specific implementation contents are described in detail below.

The navigation positioning mechanism comprises a differential GPS high-precision positioning system, an inertial navigation unit and the like, and can acquire a high-precision positioning signal, so that the automatic operation control device of the excavator can calculate more accurate movement speed and operation position of the excavator in real time;

the pose detection mechanism comprises an angle sensor, an oil cylinder displacement sensor, a gyroscope and the like which are arranged on a working device (a movable arm, a bucket rod and a bucket) and a rotation center, and is used for providing basic data for the automatic operation control device of the excavator to calculate the self pose information of the excavator in real time;

the environment detection mechanism comprises a vision sensor, a laser radar, a millimeter wave radar and the like, wherein the vision sensor can be arranged at the front part and the rear part of the top of the cab of the excavator and at the left side and the right side of the vehicle body and is used for collecting the environment information around the vehicle body; the laser radar is arranged at the top of the cab of the excavator and is used for acquiring 360-degree environment information of a working environment; the millimeter wave radar is arranged at the front part, the rear part, the left part and the right part of the lower vehicle body and is used for collecting the information of obstacles around the vehicle body. The automatic operation control device of the excavator identifies the surrounding operation environment by utilizing the environment information collected by the environment detection mechanism, and establishes an operation environment electronic map;

the excavator action executing mechanism is a mechanism which is controlled by an excavator automatic operation control device and is used for driving the excavator rotation center, the arm support, the bucket rod and the like to move;

based on the above components, the automatic operation control device of the excavator in this embodiment can realize the following functional modules through the excavator control center:

the real-time operation position determining module is used for acquiring the navigation positioning signal and calculating the real-time operation position of the excavator bucket according to the navigation positioning signal;

the operation environment determining module is used for acquiring environment information of an operation area where the excavator is located and establishing an excavator operation environment electronic map according to the environment information and the real-time operation position;

the attitude calculation module is used for acquiring the running state information of the excavator action executing mechanism in real time and calculating the attitude of the excavator according to the running state information;

the operation task planning module is used for planning an excavation operation task according to the excavator operation environment electronic map and the attitude calculation result to obtain an operation task process control parameter;

and the control output module is used for outputting a control instruction for controlling the excavator action executing mechanism based on the work task process control parameter.

Referring to fig. 4, the automatic work control process of the excavator involves the following:

(1) calculating the current operation position: calculating the current operation position of the excavator by using a differential GPS high-precision positioning system in the navigation positioning module;

(2) extracting operation environment information, and establishing an environment map: recognizing the surrounding working area environment by using sensors such as a visual sensor, a laser radar and a millimeter wave radar which are arranged on the excavator, and establishing a working environment map;

(3) calculating the posture of the working device: calculating the attitude in real time by using information provided by an angle sensor, an oil cylinder displacement sensor and the like which are arranged on a working device (a movable arm, a bucket rod and a bucket), converting the attitude to bucket teeth to obtain attitude information, and detecting the rotary real-time attitude by using a gyroscope and the like;

(4) selecting an initial digging point: dividing the operation area into discretized uniform grids with equal areas, and determining initial excavation points;

(5) path planning (current position-initial digging point): reading the position of the current bucket tooth and the selected initial digging point position, planning a motion path between the two positions by combining a kinematic equation, and generating a motion track of the bucket tooth;

(6) rotary obstacle avoidance (current position — initial excavation point): and identifying the space barrier according to the environment perception information, and planning a path capable of avoiding the barrier. Discretely sampling in a configuration space, calculating the collision risk of the obstacle, and selecting the optimal control quantity through a pre-designed cost function to realize obstacle avoidance in the processes of lifting and rotating the movable arm;

(7) mechanism movement (current position — initial digging point): driving the working device to an initial digging point through an executing mechanism according to a planned path from the current position to the digging point;

(8) automatic excavation: generating a control sequence according to the planned initial excavation point, controlling a working device and automatically completing the excavation process of the bucket;

(9) selecting an initial unloading point: determining an initial unloading point according to an environment map;

(10) path planning (initial digging point-initial unloading point): reading the positions of the selected initial digging point and the selected initial unloading point, planning a motion path between the two positions by combining a kinematic equation, and generating a motion track of bucket teeth;

(11) rotary obstacle avoidance (initial excavation point — initial unloading point): and identifying the space barrier according to the environment perception information, and planning a path capable of avoiding the barrier. Discretely sampling in a configuration space, designing a cost function, calculating the collision risk of the obstacle, selecting an optimal control quantity, and realizing obstacle avoidance in the processes of lifting and rotating the movable arm;

(12) mechanism motion (initial digging point-initial unloading point): driving the working device to an initial unloading point through an executing mechanism according to a planned path from the initial digging point to the initial unloading point and a path generated by the rotary obstacle avoidance;

(13) automatic unloading: generating a control sequence according to the selected initial unloading point, controlling a working device and automatically completing the unloading process;

(14) selecting subsequent excavation points: expanding the nodes of the current mining point to adjacent nodes according to a pre-designed cost function, solving the cost value of each sub-node of the adjacent region, and selecting the sub-node with the lowest cost value as the next mining point;

(15) path planning (initial unloading point-subsequent digging point): reading the position of the current bucket tooth at the initial unloading point and the position of a subsequent digging point, planning a motion path between the two positions by combining a kinematic equation, and generating a motion track of the bucket tooth;

(16) rotary obstacle avoidance (initial unloading point-subsequent digging point): and identifying the space barrier according to the environment perception information, and planning a path capable of avoiding the barrier. Discretely sampling in a configuration space, calculating the collision risk of the obstacle through a preset cost function, and selecting an optimal control quantity to realize obstacle avoidance in the processes of lifting and rotating the movable arm;

(17) mechanism motion (initial unloading point-subsequent digging point): generating a path from the initial unloading point to the subsequent digging point and a path generated by the rotary obstacle avoidance according to the path planning result, and controlling the actuating mechanism to the subsequent digging point;

(18) automatic excavation: generating a control sequence according to the planned subsequent excavation points, controlling a working device and automatically completing the excavation process of the bucket;

(19) selecting subsequent unloading points: and (4) solving by using a genetic algorithm according to the mapping relation between the configuration space and the Euclidean space, and planning an expected subsequent unloading point.

(20) Path planning (subsequent excavation point-subsequent unloading point): reading the position of the selected subsequent excavation point and the position of the subsequent unloading point, planning a motion path between the two positions by combining a kinematic equation, and generating a motion track of the bucket tooth;

(21) rotary obstacle avoidance (subsequent excavation point-subsequent unloading point): and identifying the space barrier according to the environment perception information, and planning a path capable of avoiding the barrier. Discretely sampling in a configuration space, calculating the collision risk of the obstacle according to a cost function, and selecting an optimal control quantity to realize obstacle avoidance in the processes of lifting and rotating the movable arm;

(22) mechanism movement (subsequent digging point-subsequent unloading point): the path from the generated subsequent excavation point to the subsequent unloading point and the path generated by the rotary obstacle avoidance are issued by combining the path planning submodule, and the motion control module controls the actuating mechanism to the subsequent unloading point;

(23) automatic unloading: and generating a control sequence according to the selected subsequent unloading point, controlling the working device and automatically completing the unloading process.

(24) Selecting subsequent excavation points: expanding the nodes of the current mining point to adjacent nodes according to a cost function, solving the cost value of each sub-node of the adjacent region, and selecting the sub-node with the lowest cost value as the next mining point;

(25) path planning (subsequent unloading point-subsequent digging point): reading the positions of the selected subsequent unloading point and the subsequent excavating point, planning a motion path between the two positions by combining a kinematic equation, and generating a motion track of the bucket tooth of the bucket;

(26) rotary obstacle avoidance (subsequent unloading point-subsequent digging point): and identifying the space barrier according to the environment perception information, and planning a path capable of avoiding the barrier. Discretely sampling in a configuration space, designing a cost function, calculating the collision risk of the obstacle, selecting an optimal control quantity, and realizing obstacle avoidance in the processes of lifting and rotating the movable arm;

(27) mechanism movement (subsequent unloading point-subsequent digging point): and the motion control module controls the actuating mechanism to reach the subsequent excavation point by combining the path from the subsequent unloading point to the subsequent excavation point issued by the path planning submodule and the path generated by the rotary obstacle avoidance. And (7) carrying out the excavating and unloading actions of the excavator in each action cycle by repeating the steps (18) to (27).

In order to implement the above control flow, in this embodiment, the functions to be implemented by the operation task planning module include six sub-function modules, such as digging point selection, path planning, automatic digging, turning obstacle avoidance, unloading point selection, and automatic unloading, and each sub-function module respectively adopts a corresponding algorithm to implement the whole automatic operation task planning, and finally, the excavator is automatically operated by controlling the excavator action executing mechanism. The excavation job task planning performed includes:

determining an excavation point to be excavated in response to the completion of the unloading action of the excavator or the new excavation operation to be executed;

planning a path for the bucket to move from a real-time position to an excavation point position, and obtaining a control parameter sequence corresponding to each actuating mechanism in the excavation process according to the planned path, wherein the path can avoid the obstacle when the movable arm and the bucket rod are lifted from the real-time position to the excavation point position in the rotation process of the rotation center;

determining an unloading point in response to the completion of the excavating action of the excavator;

planning a path of the bucket moving from the real-time position to the unloading point position, and obtaining a control parameter sequence corresponding to each actuating mechanism in the unloading process according to the planned path, wherein the path can avoid the obstacle when the movable arm and the bucket rod are lifted from the real-time position to the unloading point position in the rotation process of the rotation center. The following is a detailed description.

First, about digging point selection

The method has the function of automatically planning excavation points, wherein the initial excavation point can be preset according to the operation position of the excavator bucket, and then the subsequent excavation points are determined according to the excavation point determination method.

In this embodiment, when selecting a subsequent digging point, the operation area is divided into a discretized uniform grid with equal area, referring to fig. 2, when selecting a new digging point, the cost value of each sub-node in the adjacent area is obtained according to a pre-designed cost function and the idea of expanding from the current node to the adjacent node, and the sub-node with the lowest cost value is selected as the next digging point.

For traditional operators, the selection of the digging point is a fuzzy strategy, the neighbor point of the last digging point is often selected, and the selection of the digging point in the automatic operation process is inspired by the inspiration.

As shown in fig. 2, the entire work surface (soil heap as an example) is divided into discrete uniform grids of equal area, and the dark grids in the figure are boundaries of the work surface.

In fig. 3, in a uniform grid of a working face, a digging depth d of each grid is initialized to 0.0, a depth increase Δ d after each digging is 1.0, in a digging process, digging depth data of each node is updated after each digging, for selection of a next digging point, the embodiment relates to a mode of expanding child nodes in eight directions, and a digging point selection cost function is designed as follows:

f1＝g++d+e

in the formula, g represents the Euclidean distance from the adjacent grid point to the grid point corresponding to the initial digging point; d represents the recorded excavation depth of the adjacent grid point, and e represents the Euclidean distance from the adjacent grid point to the current excavation point; the work surface position corresponding to the adjacent grid point with the minimum cost f1 is selected as the next excavation point. When the mining point is selected, only the real-time d value is less than the pre-thresholdSet maximum depth d_maxThe cost function is calculated for the grid points of (1). d_maxThe setting of the values can be determined according to the operation requirements.

With the cost item, the diffusion of the excavation points can be gradually performed outwards along the target point, and the continuity of the excavation range can be ensured; once a grid point is expanded, the d value is updated according to the actual digging depth, if the d value reaches the maximum digging depth d_maxThen this node will no longer be selected as the new mining point at this point. Due to the arrangement of the cost item d, points which are not excavated can be preferentially selected in each excavation point selection, and the full bucket rate of each excavation can be relatively high. The matching of the cost items g, d and e also ensures that the selection process of the excavation points does not leak through each operation point, and ensures that the final excavation can reach the preset excavation depth. The grid resolution of the work surface is empirically set to be a grid of 1m × 1m in size.

Second, obstacle avoidance related to path planning and rotation

The part of the content comprises planning of a path from an excavating point to an unloading point of a movable arm bucket of the excavator and planning of a path from the unloading point to the next excavating point, wherein the planned path needs to realize obstacle avoidance in the rotation process, and finally, excavator control parameters capable of realizing the rotation obstacle avoidance path are obtained.

For obstacle avoidance in the process of lifting and rotating the movable arm, the method is implemented by discretely sampling in a configuration space, designing a cost function, calculating the collision risk of the obstacle and selecting the optimal control quantity.

During automatic excavation and automatic unloading, a genetic algorithm can be used for solving according to a preset mapping relation between a configuration space and a Euclidean space, and an expected pose of an excavation point or an unloading point is obtained.

Specifically, during route planning, movable arm and dipper can avoid the route of barrier when lifting to the uninstallation point position from real-time position when planning gyration center gyration, include:

acquiring a rotation angle of a rotation center, a rotation angle of a movable arm relative to a horizontal plane of a vehicle body, a rotation angle of an arm relative to the movable arm, a rotation angle of a bucket relative to the arm, a distance from the rotation center to a movable arm pin shaft, a distance from a movable arm pin shaft to an arm pin shaft, a distance from the arm pin shaft to a bucket pin shaft and a distance from the bucket pin shaft to a tooth tip of a bucket tooth;

acquiring position information of an obstacle;

based on the acquired information, selecting an optimal configuration which enables the cost value to be minimum in a pre-constructed arm support and bucket rod configuration space by using a preset second cost function, and determining the rotation angle of the arm support and the bucket rod in the rotation process of the rotation center according to the optimal configuration.

The rotation obstacle avoidance algorithm is described with reference to fig. 5 and 6. FIG. 5 is a mathematical model of excavator configuration space sampling, which includes establishing four independent coordinate systems (X) respectively with the rotation center of excavator body, the center of movable arm around the rotation shaft of the excavator body, the center of arm around the rotation shaft of the arm, the center of bucket around the shaft of the arm, and the bucket tooth as the origin points₁、Y₁,Z₁),(X₂、Y₂、Z₂)、(X₃、Y₃、Z₃)、(X₄、Y₄、Z₄)、(X₅、Y₅、Z₅) Wherein theta₁Angle of revolution, theta, being the centre of revolution₂Angle of rotation of the boom with respect to the horizontal plane of the vehicle body, theta₃Angle of rotation of the arm relative to the boom, θ₄The angle of rotation of the bucket relative to the stick,/₁The distance from the center of rotation to the boom pin,/₂Distance from boom pin to bucket pin, l₃Is the distance from the bucket rod pin shaft to the bucket pin shaft, l₄The distance between the bucket pin shaft and the tooth tip of the bucket tooth.

In FIG. 6, θ_2max，θ_2min，θ_3max,θ_3minMaximum and minimum limit rotation angles of the boom and stick, respectively, and a center dot representing a desired configuration X_e＝[θ_2e，θ_3e]^TThe hatched rectangular frame indicates a configuration space formed by the boom and the arm, Δ θ indicates a sampling distance, and the sampling space of fig. 6 is configured by 25 states in total.

In order to determine the appropriate rotation angle, the present embodiment designs a rotation cost function,

f2＝c_risk+c_ref

in the formula, f2 is the cost value, c_riskIndicating the risk of collision with an obstacle, c_refRepresenting the configuration of the sampling point relative to the desired configuration X_e＝[θ_2e，θ_3e]^TThe degree of deviation of (a).

Simulating rotation by a set angle, and calculating c according to the configuration of the sampling point in the configuration space and the position information of the obstacle_riskCalculating c from the sample point configuration and the desired configuration_refThe sampling point configuration in the configuration space in which f2 is minimized and does not collide is obtained, that is, it can be regarded as the optimum configuration.

To simplify the calculation of the collision risk, the results of the collision check are set to 1 for collisions and 0 for non-collisions, as shown below:

in the above formula, w_riskThe calculated weight of the collision risk in the second cost is represented, the higher the value is, the greater the proportion of the collision risk cost item in the total cost is, and the lower the value is, the collision risk cost item c is represented_riskThe smaller the specific gravity of (a).

In the collision check process, the coordinate information of the obstacle is obtained through a laser radar, a millimeter wave radar, a vision sensor and the like, the pose information of the excavator arm is obtained through a gesture calculation module, the excavator is simulated to rotate by a certain angle alpha, and a preset collision check algorithm is called to calculate the collision check result.

For c_refThe calculation of (a), using a differential calculation of the configuration,

c_ref＝w_ref||X_i-X_e||

in the formula: x_iIs the configuration of a certain sampling point, w_refRepresenting the weight of the deviation between the sampled configuration and the desired configuration in the cost calculation, the greater the value, the more the deviation cost term c is stated_refThe higher the specific gravity in the total cost.

By adjusting w_riskAnd w_refTwo coefficients, a balance of the collision risk cost term and the desired configuration deviation term can be achieved.

The algorithm time complexity based on the configuration space sampling is O (f (N)), wherein f (N) represents the time complexity for calculating the collision risk cost value and the expected configuration cost value, and N represents the number of sampling configurations. One sampling configuration X for the lower left corner of the desired configuration of the upper diagram_i＝[θ_2ei，θ_3ei]^TThe calculation of the value of (a) gives the calculation formula:

to reduce temporal complexity, the present embodiment only samples 2 layers peripherally according to the desired configuration, as shown in fig. 6. And because the algorithm is complete in resolution, when the effective solution cannot be found in the first sampling, the sampling step length is increased, the second sampling is continued, and the steps are repeated until a feasible solution is found or a conclusion that no feasible solution is given. The algorithm flow chart is shown in fig. 7, and includes:

(1) initialization of expected configuration and sampling step length;

(2) sampling the sub-state, and calculating the configuration of the sub-state;

(3) performing collision check and deviation degree calculation;

(4) whether the configuration with the minimum cost value is a feasible solution is judged, and if the configuration with the minimum cost value is a feasible solution, the feasible solution is output; if the cost value is not the minimum, judging whether the formula delta theta is larger than theta or not_2maxAnd theta_2minIf yes, no feasible solution is available; if not, increasing the sampling step size and returning to the step (2).

In the algorithm framework of the automatic operation, two layers of circulation exist, one layer is a step length iteration process, the other layer is a forward simulation rotation angle iteration process, and a bisection method is adopted for the design of the simulation rotation angle alpha. Namely:

wherein alpha is₀＝θ_1e-θ_1start，θ_1eIs the angle of rotation, theta, of the desired configuration of the excavator rotation_1startIs the rotation angle of the excavator during excavation in each working cycle. When the collision detection is carried out by forward simulation rotation at a certain angle, the first simulation rotation angle is alpha₀And when the collision check results of all the sampled configurations are 0, indicating that no collision occurs to the sampling points, selecting the sampling configuration with the minimum cost according to the rotation cost function, ending the sampling process, and excavating the machine according to the angle alpha₀The revolution is completed. When the collision check result is 1 for all sampling configurations, the rotation anglePerforming a second round of simulated collision inspection; when the collision check of the sampled configuration has both 0 and 1, it is indicated that a feasible solution exists in the sampling round, and the feasible solution with the minimum cost is output.

In order to make the algorithm converge, the invention sets a minimum simulation rotation angleWhen the simulation rotation angle is smaller than the value in the iteration process, the situation that the obstacle avoidance operation cannot be finished no matter how the configuration of the sampling rotates is shown, and the simulated rotation angle alpha is_iReset to alpha₀And increasing the sampling step length delta theta and entering the next sampling process.

In order to ensure convergence, and in combination with actual engineering experience, threshold values are respectively set for the iteration quantity, when the threshold values are reached and the iteration is stopped, and the algorithm still has no non-convergence trend, a conclusion that a feasible solution cannot be generated is output, namely that the Cartesian space cannot complete automatic operation circulation due to the existence of obstacle constraint.

Thirdly, selection of unloading point

It is determined the desired configuration in the slewing obstacle avoidance algorithm from the excavation point to the unloading pointX_e＝[θ_2e，θ_3e]^T. In the european space, the coordinate values of x and y of the target point are expected to be well determined, because the unloading point is usually the hopper of the material transporting vehicle, the center of the hopper can be determined according to information such as laser radar or a visual sensor, and then two coordinate values of the target point are determined. For the third coordinate value height information, a value h away from the hopper is usually set according to the operation experience, and the target point p is set_goal＝[x_g，y_g，h_g]. According to the mapping relation between the configuration space and the Euclidean space, a genetic algorithm is used for solving an inverse solution, namely c_e＝[θ_2e，θ_3e，θ_4e]^TRemoving the rotation angle theta of the bucket_4eI.e. the desired configuration X is obtained_e＝[θ_2e，θ_3e]^T。

Fourthly, regarding automatic excavation and automatic unloading, the manual teaching can be realized, firstly, an operator carries out operations such as excavation and unloading, and the like, and an oil cylinder control sequence in the whole process of the operation is collected and recorded; then, the control instruction of an experienced operator is learned by utilizing the neural network, and automatic operation is completed through reproduction.

The invention can realize the full-automatic excavation operation of the excavator, liberate the operators from the dangerous working environment and effectively ensure the safety of the operators; the obstacle is avoided dynamically in the rotation process, so that the construction safety of the operation equipment is effectively guaranteed; the automatic digging and unloading process can absorb experience data of skilled operators, so that the working efficiency is improved better.

Example 2

The present embodiment introduces an automatic operation control device for an excavator, including:

the real-time operation position determining module is configured for acquiring the navigation positioning signal and calculating the real-time operation position of the excavator bucket according to the navigation positioning signal;

the working environment determining module is configured for acquiring environmental information of a working area where the excavator is located and establishing an excavator working environment electronic map according to the environmental information and the real-time working position;

the attitude calculation module is configured for acquiring the running state information of the excavator action executing mechanism in real time and calculating the attitude of the excavator according to the running state information;

the operation task planning module is configured for planning an excavation operation task according to the excavator operation environment electronic map and the attitude calculation result to obtain an operation task process control parameter;

and the control output module is configured for outputting a control instruction for controlling the excavator action executing mechanism based on the work task process control parameter.

The specific functional implementation of each functional module above refers to the relevant content of embodiment 1.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.