CN116449851B - Intelligent obstacle avoidance control method and system for driller robot - Google Patents
Intelligent obstacle avoidance control method and system for driller robot Download PDFInfo
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Abstract
The application provides an intelligent obstacle avoidance control method and system for a driller robot, which relate to the technical field of data processing, acquire operation requirement information, build an operation goldenrain model, perform operation self-obstacle avoidance analysis adjustment pre-control parameters, guide a central control system to synchronously acquire multidimensional monitoring data, perform data fusion and real-time obstacle avoidance analysis, adjust the control parameters again to perform execution control, solve the technical problems that in the prior art, the obstacle avoidance execution control of the driller robot is inadequately intelligent, the analysis depth and completeness of the survivability obstacle aiming at the operation process are insufficient, the obstacle avoidance control is not timely and accurately caused to be limited, the obstacle avoidance adjustment is performed on the control parameters in the early stage of operation aiming at the operation space domain modeling analysis and adjustment test based on a digital twin technology, the real-time obstacle avoidance analysis correction is synchronously performed on multi-dimensional operation monitoring, the regional warning is performed aiming at dynamic foreign matters, the completion stability and the engagement degree of tasks are maximized, and the obstacle avoidance control is timely and accurately performed.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent obstacle avoidance control method and system of a driller robot.
Background
With the introduction of digitalization of driller operation, unmanned automatic operation based on driller robots has become a mainstream direction, and operation of the driller robots is limited due to operation control factors and external interference factors in an operation process and operation barriers. At present, aiming at the situation that the limitation of the operation obstacle avoidance of a driller robot is too high, the control is mostly carried out based on preset established obstacle avoidance logic.
The prior art is not intelligent enough for obstacle avoidance execution control of a driller robot, and is insufficient in analysis depth and completeness aiming at the storability obstacle of the operation process, so that the obstacle avoidance control is not accurate enough in time, and the operation is limited.
Disclosure of Invention
The application provides an intelligent obstacle avoidance control method and system for a driller robot, which are used for solving the technical problems that in the prior art, obstacle avoidance execution control of the driller robot is not intelligent enough, and the operation is limited due to insufficient obstacle analysis depth and completeness of the operation process.
In view of the above problems, the application provides an intelligent obstacle avoidance control method and system for a driller robot.
In a first aspect, the present application provides an intelligent obstacle avoidance control method for a driller robot, the method comprising:
acquiring job demand information, including scene information, static information and dynamic information, based on a spatial domain of autonomous and interactive jobs of the driller robot;
constructing a work goldenseal model according to the scene information, the static information and the dynamic information, wherein the work goldenseal model is in wireless connection with a central control system of the driller robot;
performing operation self-obstacle avoidance analysis based on the operation goldenrain model, outputting obstacle avoidance execution information to adjust pre-control parameters of driller operation, and determining one-time adjustment control parameters;
the primary adjustment control parameters are led into a central control system of the driller robot, external monitoring equipment and internal sensing equipment are activated synchronously, and multi-dimensional monitoring data are determined;
fusing the multidimensional monitoring data, and performing real-time obstacle avoidance analysis to determine secondary adjustment control parameters;
and performing execution control of the driller robot based on the secondary adjustment control parameters.
In a second aspect, the present application provides an intelligent obstacle avoidance control system for a driller robot, the system comprising:
the information acquisition module is used for acquiring operation demand information, including scene information, static information and dynamic information, based on the spatial domain of autonomous operation and interactive operation of the driller robot;
the model building module is used for building an operation goldenseal model according to the scene information, the static information and the dynamic information, and the operation goldenseal model is in wireless connection with a central control system of the driller robot;
the self-obstacle avoidance analysis module is used for carrying out operation self-obstacle avoidance analysis based on the operation goldenrain model, outputting obstacle avoidance execution information to adjust pre-control parameters of driller operation, and determining one-time adjustment control parameters;
the data monitoring module is used for guiding the one-time adjustment control parameters into a central control system of the driller robot, synchronously activating external monitoring equipment and internal sensing equipment and determining multi-dimensional monitoring data;
the real-time obstacle avoidance analysis module is used for fusing the multidimensional monitoring data and carrying out real-time obstacle avoidance analysis to determine secondary adjustment control parameters;
and the execution control module is used for performing execution control on the driller robot based on the secondary adjustment control parameters.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
according to the intelligent obstacle avoidance control method for the driller robot, based on the space domain of autonomous operation and interactive operation of the driller robot, operation demand information including scene information, static information and dynamic information is collected, an operation model is built based on the scene information, operation self-obstacle avoidance analysis is conducted to output obstacle avoidance execution information, pre-control parameters of driller operation are adjusted and determined to be primary adjustment control parameters, the primary adjustment control parameters are led into a central control system of the driller robot, external monitoring equipment and internal sensing equipment are synchronously activated, multidimensional monitoring data are determined, data fusion and real-time obstacle avoidance analysis are conducted to determine secondary adjustment control parameters, obstacle avoidance execution control of the driller robot is conducted based on the secondary adjustment control parameters, the technical problems that obstacle avoidance control is insufficient in time and accuracy and limited in operation are solved, obstacle avoidance control is conducted to achieve synchronous obstacle avoidance control, obstacle avoidance control is conducted to be accurate and stable in real-time, and real-time obstacle avoidance control is conducted to the real-time obstacle avoidance control area.
Drawings
FIG. 1 is a schematic flow diagram of an intelligent obstacle avoidance control method of a driller robot;
FIG. 2 is a schematic diagram of a construction flow of an operation goldenrain model in an intelligent obstacle avoidance control method of a driller robot;
FIG. 3 is a schematic diagram of a flow chart of an operation self-obstacle avoidance analysis in an intelligent obstacle avoidance control method of a driller robot;
fig. 4 is a schematic structural diagram of an intelligent obstacle avoidance control system of a driller robot.
Reference numerals illustrate: the system comprises an information acquisition module 11, a model building module 12, a self obstacle avoidance analysis module 13, a data monitoring module 14, a real-time obstacle avoidance analysis module 15 and an execution control module 16.
Detailed Description
The application provides an intelligent obstacle avoidance control method and system for a driller robot, which are used for solving the technical problems that in the prior art, the obstacle avoidance execution control of the driller robot is not intelligent enough, the analysis depth and completeness of the obstacle avoidance are insufficient aiming at the survivability obstacle in the operation process, and the operation is limited due to the fact that the obstacle avoidance control is not timely and accurate enough.
Example 1
As shown in fig. 1, the application provides an intelligent obstacle avoidance control method of a driller robot, which comprises the following steps:
step S100: acquiring job demand information, including scene information, static information and dynamic information, based on a spatial domain of autonomous and interactive jobs of the driller robot;
specifically, with the introduction of digitalization of driller operation, the driller robot gradually replaces manual operation and semi-automatic equipment to realize unmanned automatic operation execution, so as to ensure the completion stability and engagement of work tasks and ensure the operation safety, and the operation obstacle existing in the operation process needs to be accurately and timely avoided.
Specifically, the driller robot is a machine for performing automated drilling operations, and a plurality of operation conditions exist in the operation process of the driller robot, including individual operation conditions based on the driller robot; collaborative operation conditions with driller robots as a master or auxiliary; and in the operation initial node and the operation termination node of the driller robot, the operation status of the equipment handover of the upper operation node and the lower operation node is determined based on the operation status, and the space domain of autonomous operation and interactive operation of the driller robot, namely the whole space related to the operation, is determined. Further collecting the operation scene of the driller robot, wherein the operation scene comprises an operation field, a position environment and the like, and the operation scene is used as scene information; basic information acquisition of sizes, positions and the like of articles, equipment components and the like existing in a working scene is carried out, and the basic information is used as the static information; and acquiring the existing regular and irregular dynamic change information, such as article moving, assembly interaction of components and the like, integrating the scene information, the static information and the dynamic information as the dynamic information, and taking the operation requirement information as the basic data support for operation analysis.
Step S200: constructing a work goldenseal model according to the scene information, the static information and the dynamic information, wherein the work goldenseal model is in wireless connection with a central control system of the driller robot;
further, as shown in fig. 2, the step S200 of constructing a job goldenrain model according to the scene information, the static information and the dynamic information further includes:
step S210: building a job scene model based on the scene information;
step S220: building a three-dimensional model of the equipment based on the static information, wherein the three-dimensional model of the equipment comprises a plurality of component sub-models;
step S230: combining the plurality of component sub-models, and building a brake control model based on the dynamic information, wherein a bottom layer control mechanism is embedded in the brake control model;
step S240: and carrying out model fusion nesting on the operation scene model, the equipment three-dimensional model and the brake control model to generate the operation goldenrain model.
Specifically, performing space simulation reduction on the scene information, and taking the generated simulation scene as the operation scene model, wherein the operation scene model can be synchronously adjusted based on the acquired real-time scene information; for the static information, three-dimensional modeling is respectively carried out on contained articles, equipment components and the like, a plurality of necessary construction points of modeling objects which can meet the requirements of reduction modeling are exemplarily determined, the necessary construction points are taken as scanning points, laser is emitted to the scanning points based on a laser scanner, point cloud coordinates and surface features of each modeling object are determined based on received echo signals, the point cloud coordinates and the surface features corresponding to mapping are combined, a plurality of component sub-models existing in a working scene are generated, and the equipment three-dimensional model is integrally generated; and based on the plurality of component sub-models, screening and connecting the plurality of component sub-models based on the existing combined connection relation, determining brake operation logic based on the dynamic information, for example, combining the operation mechanisms of the generated driller robot, taking the operation mechanisms as the bottom layer control mechanism, and respectively carrying out matching and embedding of the combined connection results to generate the brake control model.
Furthermore, based on a static sub-model and the brake control model in the equipment three-dimensional model, positioning and embedding are carried out in the operation scene model, and the operation goldenrain model is used as the operation goldenrain model, and is an analog digital goldenrain consistent with an operation live condition. The operation goldenrain model is a self-built auxiliary analysis tool for obstacle avoidance management, and can effectively ensure the accuracy of an analysis result so as to perform the pre-adjustment of the operation. The operation goldenrain model is in wireless connection with a central control system of the driller robot, and the goldenrain model is fed back to the driller robot in time for operation control after obstacle avoidance analysis is completed.
Step S300: performing operation self-obstacle avoidance analysis based on the operation goldenrain model, outputting obstacle avoidance execution information to adjust pre-control parameters of driller operation, and determining one-time adjustment control parameters;
further, as shown in fig. 3, the operation self-obstacle avoidance analysis is performed based on the operation goldenrain model, and step S300 of the present application further includes:
step S310: identifying pre-control parameters of the driller robot, and extracting independent operation control parameters and cooperative operation control parameters;
step S320: performing fitting test operation by combining the operation goldenrain model based on the independent operation control parameters to obtain a first collision sequence, wherein the first collision sequence comprises structural tangents and is characterized as an empty set for a collision-free condition;
step S330: performing simulated test operation by combining the operation goldenrain model based on the cooperative operation control parameters to obtain a second collision sequence, wherein the second collision sequence is provided with an active collision identifier and a passive collision identifier;
step S340: and integrating the first collision sequence and the second collision sequence to be used as a self-obstacle avoidance analysis result.
Further, there is a step S350, including:
step S351: if the self obstacle avoidance analysis result is an empty set, generating a pre-execution instruction and transmitting the pre-execution instruction to a central control system of the driller robot;
step S352: if any one of the first collision sequence and the second collision sequence belongs to a non-empty set in the self-obstacle avoidance analysis result, a braking adjustment instruction is generated;
step S353: and carrying out brake adjustment analysis on the pre-control parameters along with the receiving of the brake adjustment command, and determining the primary adjustment control parameters, wherein the primary adjustment control parameters comprise an adjustment direction, an adjustment scale and a brake time limit.
Further, step S353 of the present application further includes:
step S3531: invoking a non-empty collision sequence based on the brake adjustment instruction;
step S3532: determining preliminary adjustment parameters based on collision parameters contained in the non-empty collision sequence;
step S3533: aiming at the preliminary adjustment parameters, testing is carried out based on the operation goldenrain model, and an adjustment and test result is obtained;
step S3534: and if the adjustment and verification result is not qualified, repeating the parameter adjustment and verification step.
Specifically, the pre-control parameters are execution control parameters of the driller robot operation, the pre-control parameters are subjected to time sequence splitting based on operation conditions, the independent operation control parameters of independent operation execution of the driller robot are determined, the cooperative operation control parameters of operation interaction of equipment or articles and the driller robot exist, and an analog operation test is performed based on the operation goldler model respectively so as to perform operation collision analysis detection. Specifically, based on the independent operation control parameters, a brake control test of the operation goldenrain model is performed, whether collision exists in the simulated test running process is detected, if the structure tangency condition exists, the structure tangency condition is judged to exist, the detection results are integrated, a plurality of sequences which are characterized by collision positions, collision time sequences and collision grades are determined to serve as the first collision sequence, and if no independent operation collision exists, the first collision sequence is characterized as an empty set.
Based on the cooperative operation control parameters, collecting control parameters of cooperative operation equipment, performing time sequence mapping, detecting whether collision exists in the simulated test running process or not based on a brake control test of the operation goldenrain model, judging the collision type aiming at the existing operation collision, and if a collision source is an active collision of a driller robot; and if the collision source is a passive collision of the cooperative operation equipment, generating the second collision sequence with the collision type identifier, and similarly, if the cooperative operation collision does not exist, the second collision sequence is characterized as an empty set. And carrying out time sequence integration on the first collision sequence and the second collision sequence to generate the self-obstacle avoidance analysis result, wherein the self-obstacle avoidance analysis result has high consistency with the actual operation.
Further, the self obstacle avoidance analysis result is identified, if the self obstacle avoidance analysis result is an empty set, a qualitative operation obstacle does not exist, the pre-execution instruction is generated, the pre-execution instruction is a start instruction for executing the pre-control parameter, the pre-execution instruction is transmitted to a central control system of the driller robot, and the operation control of the driller robot is performed; if any one of the first collision sequence and the second collision sequence belongs to a non-empty set in the self-obstacle avoidance analysis result, the pre-control parameters are required to be adjusted, and the braking adjustment instruction is generated, namely, a starting instruction for parameter adjustment is generated. And carrying out brake adjustment analysis of the pre-control parameters along with the receiving of the brake adjustment command.
Specifically, based on the receiving of the braking adjustment instruction, the non-empty collision sequence is called, and based on the non-empty collision sequence, the adjustment direction is determined based on the collision position on the basis of not affecting the operation effect; performing avoidance analysis based on a collision sequence, and determining the braking time limit, wherein avoidance equipment is required to be determined for a second collision sequence; and determining an adjustment scale based on the collision grade, and determining the preliminary adjustment parameters. And performing mapping replacement of the preliminary adjustment parameters in the pre-control parameters, performing trial operation again based on the operation goldenrain model, detecting whether collision exists in the trial operation process, and obtaining the adjustment and inspection result. And if the debugging result is not qualified, namely that operation collision still exists, performing parameter adjustment and model test operation analysis again based on the steps until the final detection and determination result is qualified, and taking the corresponding adjusted test operation control parameter as the primary adjustment control parameter, wherein the primary adjustment control parameter is the obstacle avoidance optimization control parameter determined for qualitative operation.
Step S400: the primary adjustment control parameters are led into a central control system of the driller robot, external monitoring equipment and internal sensing equipment are activated synchronously, and multi-dimensional monitoring data are determined;
step S500: fusing the multidimensional monitoring data, and performing real-time obstacle avoidance analysis to determine secondary adjustment control parameters;
step S600: and performing execution control of the driller robot based on the secondary adjustment control parameters.
Specifically, the first adjustment control parameter is fed back to a central control system of the driller robot, the pre-control parameter is iterated to perform brake control, and the external monitoring equipment and the internal sensing equipment are synchronously activated along with the brake control start of the driller robot, and the external monitoring equipment is arranged in an operation environment, such as an image monitoring equipment, and is used for performing real-time operation monitoring; the built-in sensing device is a sensing device, such as an infrared ranging sensor, configured inside the driller robot. And carrying out time sequence mapping integration on the acquired data of the external monitoring equipment and the internal sensing equipment, wherein the acquired data is used as the multi-dimensional monitoring data which is source data for carrying out real-time obstacle avoidance analysis.
Because the single-dimensional monitoring data is influenced by factors such as vision difference or perception deviation, the accuracy of data identification is insufficient, the same-time sequence data fusion is carried out on the multi-dimensional monitoring data, whether real-time operation barriers exist or not is judged according to the fusion data, for example, sudden barriers such as operation derivatives, personnel flow and the like, obstacle tracing and obstacle avoidance analysis are carried out to determine correction parameters, positioning adjustment is carried out on the primary adjustment control parameters, the secondary adjustment control parameters are generated, the real-time control parameters with obstacle avoidance energy efficiency exist on the secondary adjustment control parameters currently, and execution control of the driller robot is carried out on the basis of the secondary adjustment control parameters, so that the accuracy and completeness of the obstacle avoidance control effect are guaranteed to the maximum extent.
Further, the step S500 of the present application further includes the steps of:
step S510-1: positioning a collision detection point;
step S520-1: based on the collision detection point, identifying, extracting and coaxially converting the built-in monitoring data and the external monitoring data to determine fusion data;
step S530-1: and performing collision judgment on the fusion data, and acquiring collision parameters to perform operation control adjustment.
Further, the step S500 of the present application further includes:
step S510-2: defining an early warning triggering space, wherein the early warning triggering space is the superposition of an operation demand space and a space tolerance;
step S520-2: if foreign matter invasion exists in the early warning triggering space, collision warning information is generated to carry out early warning, and the collision warning information comprises a plurality of warning grades.
Specifically, based on the multidimensional monitoring data, performing simultaneous sequence analysis, respectively performing collision positioning on the built-in monitoring data and the external monitoring data, and taking an integrated positioning result as the collision detection point, namely, a survivability collision position without regard to the identification deviation, wherein the collision detection point is a predicted collision positioning point in a preset time zone. And respectively identifying and extracting the monitoring data corresponding to the collision monitoring points from the built-in monitoring data and the external monitoring data, comprehensively analyzing and homodromously converting aiming at multiple data types and multiple monitoring angles, and determining the fusion data corresponding to the collision monitoring points. And performing collision analysis on the fusion data to ensure the accuracy of real-time monitoring data, avoiding abnormal judgment caused by external factors such as visual differences and the like, acquiring a collision result, and if operation collision exists, determining an obstacle avoidance mode and obstacle avoidance parameters according to a collision source and performing operation obstacle avoidance control.
Meanwhile, the operation space of each component during the operation of the driller robot is determined to be used as the operation demand space, meanwhile, the space tolerance is set, and the space region which is set by user definition and extends in the operation demand space, for example, the space extends for 10cm, so that warning prevention is convenient to be carried out in advance, and the operation demand space and the space tolerance are overlapped to be used as the early warning triggering space. If foreign matter invasion exists in the early warning triggering space, such as mobile equipment triggering, personnel triggering and the like, collision warning information is generated, wherein invasion depth and risk degree are different, corresponding warning levels are different, and different warning information is adopted for different warning levels so as to quickly judge invasion live conditions.
Example two
Based on the same inventive concept as the intelligent obstacle avoidance control method of a driller robot in the foregoing embodiment, as shown in fig. 4, the present application provides an intelligent obstacle avoidance control system of a driller robot, the system includes:
the information acquisition module 11 is used for acquiring operation demand information, including scene information, static information and dynamic information, based on the spatial domain of autonomous operation and interactive operation of the driller robot;
the model building module 12 is used for building a work goldenrain model according to the scene information, the static information and the dynamic information, and the work goldenrain model is in wireless connection with a central control system of the driller robot;
the self-obstacle avoidance analysis module 13 is used for carrying out operation self-obstacle avoidance analysis based on the operation goldenrain model, outputting obstacle avoidance execution information to adjust pre-control parameters of driller operation, and determining one-time adjustment control parameters;
the data monitoring module 14 is used for guiding the primary adjustment control parameters into a central control system of the driller robot, synchronously activating external monitoring equipment and internal sensing equipment, and determining multi-dimensional monitoring data;
the real-time obstacle avoidance analysis module 15 is used for fusing the multidimensional monitoring data, and performing real-time obstacle avoidance analysis to determine secondary adjustment control parameters;
and the execution control module 16 is used for executing execution control of the driller robot based on the secondary adjustment control parameters.
Further, the system further comprises:
the operation scene model building module is used for building an operation scene model based on the scene information;
the equipment three-dimensional model building module is used for building an equipment three-dimensional model based on the static information, and the equipment three-dimensional model comprises a plurality of component sub-models;
the brake control model building module is used for building a brake control model based on the dynamic information by combining the plurality of component sub-models, and a bottom control mechanism is embedded in the brake control model;
and the fusion nesting module is used for carrying out model fusion nesting on the operation scene model, the equipment three-dimensional model and the brake control model to generate the operation goldenrain model.
Further, the system further comprises:
the parameter extraction module is used for identifying pre-control parameters of the driller robot and extracting independent operation control parameters and cooperative operation control parameters;
the first collision sequence acquisition module is used for carrying out simulated test operation by combining the operation goldenrain model based on the independent operation control parameters to acquire a first collision sequence, wherein the first collision sequence comprises a structure tangent and is characterized as an empty set for the collision-free condition;
the second collision sequence acquisition module is used for carrying out simulated test operation by combining the operation goldenrain model based on the cooperative operation control parameters to acquire a second collision sequence, wherein the second collision sequence is provided with an active collision identifier and a passive collision identifier;
the sequence integration module is used for integrating the first collision sequence and the second collision sequence and is used as a self-obstacle avoidance analysis result.
Further, the system further comprises:
the pre-execution instruction generation module is used for generating a pre-execution instruction and transmitting the pre-execution instruction to a central control system of the driller robot if the self obstacle avoidance analysis result is an empty set;
the braking adjustment instruction generation module is used for generating a braking adjustment instruction if any one of the first collision sequence and the second collision sequence belongs to a non-empty set in the self obstacle avoidance analysis result;
the adjustment analysis module is used for carrying out braking adjustment analysis on the pre-control parameters along with the receiving of the braking adjustment instruction, and determining the primary adjustment control parameters, wherein the primary adjustment control parameters comprise an adjustment direction, an adjustment scale and a braking time limit.
Further, the system further comprises:
the sequence calling module is used for calling a non-empty collision sequence based on the brake adjustment instruction;
the preliminary adjustment parameter determination module is used for determining preliminary adjustment parameters based on collision parameters contained in the non-empty collision sequence;
the testing module is used for testing the preliminary adjustment parameters based on the operation goldenrain model to obtain adjustment and test results;
and the repeated adjustment and verification module is used for repeating the parameter adjustment and verification step if the adjustment and verification result is not qualified.
Further, the system further comprises:
the monitoring point positioning module is used for positioning collision detection points;
the fusion data determining module is used for identifying, extracting and coaxially converting the built-in monitoring data and the external monitoring data based on the collision detection point to determine fusion data;
and the collision judgment module is used for carrying out collision judgment on the fusion data, acquiring collision parameters and carrying out operation control adjustment.
Further, the system further comprises:
the space definition module is used for defining an early warning triggering space which is the superposition of the operation demand space and the space tolerance;
the collision warning module is used for generating collision warning information to perform early warning if foreign matter invasion exists in the early warning triggering space, and the collision warning information comprises a plurality of warning grades.
Through the foregoing detailed description of the intelligent obstacle avoidance control method of the driller robot, those skilled in the art can clearly know the intelligent obstacle avoidance control method and system of the driller robot in this embodiment, and for the device disclosed in the embodiment, the description is relatively simple because it corresponds to the method disclosed in the embodiment, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. An intelligent obstacle avoidance control method of a driller robot is characterized by comprising the following steps:
acquiring job demand information, including scene information, static information and dynamic information, based on a spatial domain of autonomous and interactive jobs of the driller robot;
constructing a work goldenseal model according to the scene information, the static information and the dynamic information, wherein the work goldenseal model is in wireless connection with a central control system of the driller robot;
performing operation self-obstacle avoidance analysis based on the operation goldenrain model, outputting obstacle avoidance execution information to adjust pre-control parameters of driller operation, and determining one-time adjustment control parameters;
the primary adjustment control parameters are led into a central control system of the driller robot, external monitoring equipment and internal sensing equipment are activated synchronously, and multi-dimensional monitoring data are determined;
fusing the multidimensional monitoring data, and performing real-time obstacle avoidance analysis to determine secondary adjustment control parameters;
performing execution control of the driller robot based on the secondary adjustment control parameters;
wherein, based on the operation goldenrain model, the operation self-obstacle avoidance analysis comprises:
identifying pre-control parameters of the driller robot, and extracting independent operation control parameters and cooperative operation control parameters;
performing fitting test operation by combining the operation goldenrain model based on the independent operation control parameters to obtain a first collision sequence, wherein if a structure tangency condition exists in the fitting test operation, the fitting test operation is judged to have collision, detection results are integrated, a plurality of sequences which are characterized by collision position, collision time sequence and collision grade are determined to be used as the first collision sequence, and if no independent operation collision exists, the first collision sequence is characterized as an empty set;
performing simulated test operation by combining the operation goldenrain model based on the cooperative operation control parameters to obtain a second collision sequence, wherein the second collision sequence is provided with an active collision identifier and a passive collision identifier;
and integrating the first collision sequence and the second collision sequence to be used as a self-obstacle avoidance analysis result.
2. Method according to claim 1, wherein said building a job goldenrain model from said scene information, said static information and said dynamic information comprises:
building a job scene model based on the scene information;
building a three-dimensional model of the equipment based on the static information, wherein the three-dimensional model of the equipment comprises a plurality of component sub-models;
combining the plurality of component sub-models, and building a brake control model based on the dynamic information, wherein a bottom layer control mechanism is embedded in the brake control model;
and carrying out model fusion nesting on the operation scene model, the equipment three-dimensional model and the brake control model to generate the operation goldenrain model.
3. The method of claim 1, wherein the method comprises:
if the self obstacle avoidance analysis result is an empty set, generating a pre-execution instruction and transmitting the pre-execution instruction to a central control system of the driller robot;
if any one of the first collision sequence and the second collision sequence belongs to a non-empty set in the self-obstacle avoidance analysis result, a braking adjustment instruction is generated;
and carrying out brake adjustment analysis on the pre-control parameters along with the receiving of the brake adjustment command, and determining the primary adjustment control parameters, wherein the primary adjustment control parameters comprise an adjustment direction, an adjustment scale and a brake time limit.
4. A method as claimed in claim 3, wherein the method comprises:
invoking a non-empty collision sequence based on the brake adjustment instruction;
determining preliminary adjustment parameters based on collision parameters contained in the non-empty collision sequence;
aiming at the preliminary adjustment parameters, testing is carried out based on the operation goldenrain model, and an adjustment and test result is obtained;
and if the adjustment and verification result is not qualified, repeating the parameter adjustment and verification step.
5. The method of claim 1, wherein the fusing the multi-dimensional monitoring data for real-time obstacle avoidance analysis determines secondary adjustment control parameters, the method comprising:
positioning a collision detection point;
based on the collision detection point, identifying, extracting and coaxially converting the built-in monitoring data and the external monitoring data to determine fusion data;
and performing collision judgment on the fusion data, and acquiring collision parameters to perform operation control adjustment.
6. The method of claim 1, wherein the method comprises:
defining an early warning triggering space, wherein the early warning triggering space is the superposition of an operation demand space and a space tolerance;
if foreign matter invasion exists in the early warning triggering space, collision warning information is generated to carry out early warning, and the collision warning information comprises a plurality of warning grades.
7. An intelligent obstacle avoidance control system for a driller robot, the system comprising:
the information acquisition module is used for acquiring operation demand information, including scene information, static information and dynamic information, based on the spatial domain of autonomous operation and interactive operation of the driller robot;
the model building module is used for building an operation goldenseal model according to the scene information, the static information and the dynamic information, and the operation goldenseal model is in wireless connection with a central control system of the driller robot;
the self-obstacle avoidance analysis module is used for carrying out operation self-obstacle avoidance analysis based on the operation goldenrain model, outputting obstacle avoidance execution information to adjust pre-control parameters of driller operation, and determining one-time adjustment control parameters;
the data monitoring module is used for guiding the one-time adjustment control parameters into a central control system of the driller robot, synchronously activating external monitoring equipment and internal sensing equipment and determining multi-dimensional monitoring data;
the real-time obstacle avoidance analysis module is used for fusing the multidimensional monitoring data and carrying out real-time obstacle avoidance analysis to determine secondary adjustment control parameters;
the execution control module is used for performing execution control on the driller robot based on the secondary adjustment control parameters;
the parameter extraction module is used for identifying pre-control parameters of the driller robot and extracting independent operation control parameters and cooperative operation control parameters;
the first collision sequence acquisition module is used for carrying out simulated test operation by combining the operation goldenrain model based on the independent operation control parameters to acquire a first collision sequence, wherein if a structure tangent condition exists in the simulated test operation, the first collision sequence is judged to have a collision, detection results are integrated, a plurality of sequences which are characterized as collision positions, collision time sequences and collision grades are determined to be used as the first collision sequence, and if no independent operation collision exists, the first collision sequence is characterized as an empty set;
the second collision sequence acquisition module is used for carrying out simulated test operation by combining the operation goldenrain model based on the cooperative operation control parameters to acquire a second collision sequence, wherein the second collision sequence is provided with an active collision identifier and a passive collision identifier;
the sequence integration module is used for integrating the first collision sequence and the second collision sequence and is used as a self-obstacle avoidance analysis result.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104766371A (en) * | 2015-04-13 | 2015-07-08 | 南京工程学院 | Collision detection method for objects in large-scale scene |
| CN112828886A (en) * | 2020-12-31 | 2021-05-25 | 天津职业技术师范大学(中国职业培训指导教师进修中心) | Industrial robot collision prediction control method based on digital twinning |
| CN113050649A (en) * | 2021-03-24 | 2021-06-29 | 西安科技大学 | Remote control system and method for inspection robot driven by digital twin |
| CN113954066A (en) * | 2021-10-14 | 2022-01-21 | 国电南瑞科技股份有限公司 | Distribution network operation robot control method and device based on digital twin system |
| CN114131597A (en) * | 2021-11-24 | 2022-03-04 | 山东哈博特机器人有限公司 | Industrial robot simulation linkage method and system based on digital twinning technology |
| CN115556112A (en) * | 2022-10-28 | 2023-01-03 | 北京理工大学 | Robot teleoperation method and system based on digital twins |
| CN115781685A (en) * | 2022-12-26 | 2023-03-14 | 广东工业大学 | High-precision mechanical arm control method and system based on reinforcement learning |
| CN116157240A (en) * | 2021-08-04 | 2023-05-23 | 克朗斯股份公司 | System and method for automatically generating movement and position parameters for a wrapping or palletizing machine for bundle placement |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DK179938B1 (en) * | 2018-03-11 | 2019-10-14 | Maersk Drilling A/S | Robotic Apparatus for performing Drill Floor Operations |
-
2023
- 2023-06-12 CN CN202310688088.XA patent/CN116449851B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104766371A (en) * | 2015-04-13 | 2015-07-08 | 南京工程学院 | Collision detection method for objects in large-scale scene |
| CN112828886A (en) * | 2020-12-31 | 2021-05-25 | 天津职业技术师范大学(中国职业培训指导教师进修中心) | Industrial robot collision prediction control method based on digital twinning |
| CN113050649A (en) * | 2021-03-24 | 2021-06-29 | 西安科技大学 | Remote control system and method for inspection robot driven by digital twin |
| CN116157240A (en) * | 2021-08-04 | 2023-05-23 | 克朗斯股份公司 | System and method for automatically generating movement and position parameters for a wrapping or palletizing machine for bundle placement |
| CN113954066A (en) * | 2021-10-14 | 2022-01-21 | 国电南瑞科技股份有限公司 | Distribution network operation robot control method and device based on digital twin system |
| CN114131597A (en) * | 2021-11-24 | 2022-03-04 | 山东哈博特机器人有限公司 | Industrial robot simulation linkage method and system based on digital twinning technology |
| CN115556112A (en) * | 2022-10-28 | 2023-01-03 | 北京理工大学 | Robot teleoperation method and system based on digital twins |
| CN115781685A (en) * | 2022-12-26 | 2023-03-14 | 广东工业大学 | High-precision mechanical arm control method and system based on reinforcement learning |
Non-Patent Citations (1)
| Title |
|---|
| 基于数字孪生的工业机器人运动仿真;崔亚飞 等;机电工程技术;第51卷(第12期);第183-186页 * |
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