CN116277041B - Robot structure optimization method and system combined with demand analysis - Google Patents
Robot structure optimization method and system combined with demand analysis Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B25J9/00—Programme-controlled manipulators
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- B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
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Abstract
The invention provides a robot structure optimization method and a system combining demand analysis, which relate to the technical field of data analysis, and are used for reading preset tasks of a preset robot and carrying out decomposition analysis to obtain preset functional demands, carrying out demand analysis, constructing a path planning unit and a motion control unit, controlling the preset robot to carry out inspection of preset equipment to simulate, analyzing and determining optimal structure parameters, carrying out structure optimization on the preset robot by combining the optimal inspection parameters, solving the technical problems in the prior art that the optimization result and the operation demands are not matched enough due to the fact that the analysis level is shallow and the combination degree with the demands is insufficient, causing the follow-up operation to be limited, determining the functional demands by decomposing the inspection tasks, carrying out adaptability adjustment analysis, guaranteeing the demand consistency of the optimization direction, carrying out target grid division and grid-by-grid analysis, and guaranteeing the analysis depth and accuracy.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a robot structure optimization method and system combining requirement analysis.
Background
With the development of computer technology, robots gradually replace manual work to execute efficient operation, so that the execution fit degree of tasks is effectively guaranteed, the robots are required to be adjusted in an adapting mode according to operation requirements, and currently, in the aspect of optimization of the robots, mechanical structure adjustment is mainly performed based on index requirements, so that certain technical limitations exist, and the operation requirements cannot be met.
In the structural optimization aspect of the robot, the analysis level is shallow and the combination degree with the requirement is insufficient, so that the optimization result and the operation requirement are not adapted enough, and the follow-up operation is limited.
Disclosure of Invention
The application provides a robot structure optimization method and a system combining demand analysis, which are used for solving the technical problem that the follow-up operation is limited due to insufficient adaptation of an optimization result and operation demands caused by shallow analysis level and insufficient combination degree with the demands in the prior art.
In view of the above problems, the present application provides a method and a system for optimizing a robot structure in combination with demand analysis.
In a first aspect, the present application provides a method for optimizing a robot structure in combination with demand analysis, the method comprising:
Reading a preset task of a preset robot, wherein the preset task refers to an intelligent patrol task for monitoring the running state of preset equipment;
decomposing and analyzing the intelligent inspection task to obtain preset functional requirements, wherein the preset functional requirements comprise a first functional requirement and a second functional requirement;
analyzing the path planning requirements in the first functional requirements and constructing a path planning unit, analyzing the motion control requirements in the second functional requirements and constructing a motion control unit to form an intelligent control module;
simulating the inspection of the preset equipment by controlling the preset robot by the intelligent control module to obtain inspection simulation data, wherein the preset robot has preset structural parameters;
analyzing the inspection simulation data, obtaining an optimizing result of the preset structural parameter according to an analysis result, and marking the optimizing result as the optimal structural parameter;
the intelligent patrol module is reversely matched with the second functional requirement, wherein the intelligent patrol module is internally embedded with optimal patrol parameters;
and carrying out structural optimization on the preset robot by combining the optimal structural parameters and the optimal inspection parameters.
In a second aspect, the present application provides a robotic structure optimization system incorporating demand analysis, the system comprising:
the task reading module is used for reading a preset task of a preset robot, wherein the preset task is an intelligent patrol task for monitoring the running state of preset equipment;
the task decomposition module is used for decomposing and analyzing the intelligent inspection task to obtain a preset function requirement, wherein the preset function requirement comprises a first function requirement and a second function requirement;
the construction module is used for analyzing the path planning requirements in the first functional requirements and constructing a path planning unit, analyzing the motion control requirements in the second functional requirements and constructing a motion control unit to form an intelligent control module;
the inspection simulation module is used for simulating the inspection of the preset robot by the intelligent control module to obtain inspection simulation data, wherein the preset robot has preset structural parameters;
the parameter optimizing module is used for analyzing the inspection simulation data, obtaining an optimizing result of the preset structural parameter according to an analysis result and recording the optimizing result as an optimal structural parameter;
The matching module is used for reversely matching the intelligent inspection module with the second functional requirement, wherein the intelligent inspection module is internally embedded with optimal inspection parameters;
the structure optimization module is used for carrying out structure optimization on the preset robot by combining the optimal structure parameter and the optimal inspection parameter.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
according to the robot structure optimization method combining with the demand analysis, preset tasks of the preset robot are read, namely intelligent patrol tasks for monitoring the running state of preset equipment; decomposing and analyzing the intelligent inspection task to obtain preset functional requirements, including a first functional requirement and a second functional requirement, analyzing path planning requirements in the first functional requirement and constructing a path planning unit; analyzing the motion control requirements in the second functional requirements and constructing a motion control unit to form an intelligent control module; the preset robot is controlled to carry out inspection of the preset device to simulate, inspection simulation data are obtained, inspection simulation data analysis is carried out to obtain an optimizing result of the preset structural parameter, the optimizing result is recorded as an optimal structural parameter and is reversely matched with an intelligent inspection module of the second functional requirement, the intelligent inspection module is internally embedded with the optimal inspection parameter, the optimal structural parameter and the optimal inspection parameter are combined to carry out structural optimization on the preset robot, the technical problems that in the prior art, the optimizing result and the running requirement are not matched enough due to the fact that an analysis layer is shallow and the combination degree with the requirement is insufficient, the follow-up operation is limited are solved, functional requirements are determined through decomposing inspection tasks, adaptability adjustment analysis is carried out, the requirement consistency of the optimizing direction is guaranteed, target grid division and grid-by-grid analysis are carried out, and analysis depth and accuracy are guaranteed.
Drawings
FIG. 1 is a schematic flow diagram of a robot structure optimization method in combination with demand analysis;
FIG. 2 is a schematic diagram of a target path planning and obtaining process in a robot structure optimization method combining demand analysis;
FIG. 3 is a schematic diagram of an optimal structure parameter obtaining process in a robot structure optimization method combined with demand analysis;
fig. 4 is a schematic structural diagram of a robot structural optimization system according to the present application, which combines with demand analysis.
Reference numerals illustrate: the system comprises a task reading module 11, a task decomposing module 12, a constructing module 13, a patrol simulation module 14, a parameter optimizing module 15, a matching module 16 and a structure optimizing module 17.
Detailed Description
The application provides a robot structure optimization method and system combining requirement analysis, which are used for reading preset tasks of a preset robot and carrying out decomposition analysis to obtain preset functional requirements, carrying out requirement analysis and construction path planning units and motion control units, controlling the preset robot to carry out inspection of preset equipment to simulate, analyzing and determining optimal structure parameters, reversely matching an intelligent inspection module of the second functional requirements, and carrying out structure optimization on the preset robot by combining the optimal structure parameters and the optimal inspection parameters, wherein the intelligent inspection module is internally provided with the optimal inspection parameters and is used for solving the technical problems of limited follow-up operation caused by insufficient adaptation of an optimization result and operation requirements due to shallow display of an analysis layer and insufficient combination degree of requirements in the prior art.
Example 1
As shown in fig. 1, the present application provides a robot structure optimization method combined with demand analysis, the method comprising:
step S100: reading a preset task of a preset robot, wherein the preset task refers to an intelligent patrol task for monitoring the running state of preset equipment;
specifically, with the development of computer technology, the robot gradually replaces manual execution of efficient operation, so as to effectively ensure the execution fitting degree of tasks, and the robot needs to be adaptively adjusted in combination with operation requirements. Specifically, the preset robot is a target robot to be subjected to operation control, the preset device is a device to be inspected arranged in an inspection area, an intelligent inspection task for monitoring the running state of the preset robot on the preset device is read and used as the preset task, and the preset task is a credential for performing structural optimization on the preset robot.
Step S200: decomposing and analyzing the intelligent inspection task to obtain preset functional requirements, wherein the preset functional requirements comprise a first functional requirement and a second functional requirement;
step S300: analyzing the path planning requirements in the first functional requirements and constructing a path planning unit, analyzing the motion control requirements in the second functional requirements and constructing a motion control unit to form an intelligent control module;
specifically, the intelligent inspection task is decomposed, and basic functions of the preset robot, namely, basic functions simulating basic movements of a person, such as movement, steering, obstacle avoidance and the like, are determined to be used as the first functional requirements; determining a special function of the preset robot, namely, regarding functions of the preset task, such as inspection, examination, analysis and diagnosis, early warning and the like, taking the first function requirement and the second function requirement as the preset function requirement, and constructing an adaptive function area based on the preset function requirement. Further, the path planning unit is constructed based on the path planning requirement in the first functional requirement, and is used for performing routing inspection path planning of the preset robot, and for example, the path planning unit can be generated by collecting sample data to perform neural network training, and planning execution logic is configured to perform unit embedding, wherein the planning execution logic is grid searching, estimated cost calculating and grid optimizing. The motion control requirement of the second functional requirement is further analyzed, the motion control requirement comprises point location inspection time length, inspection direction control, monitoring equipment start and stop and the like, a plurality of inspection control instructions are generated, activation conditions are configured to form the motion control unit, the path planning unit and the motion control unit are combined to generate the intelligent control module, and the intelligent control module is used for performing inspection simulation execution control of the preset robot.
Step S400: simulating the inspection of the preset equipment by controlling the preset robot by the intelligent control module to obtain inspection simulation data, wherein the preset robot has preset structural parameters;
specifically, a visual simulation platform is connected, simulation modeling is carried out on the preset equipment and the preset robot with preset structural parameters, and a visual inspection simulation model is generated. Based on the intelligent control module, generating a target path plan according to the path planning unit, taking the target path plan as simulation constraint, storing the simulation constraint into the motion control unit in the intelligent control module, performing inspection simulation control based on the motion control unit in the visual inspection simulation model, and generating inspection simulation data, wherein the inspection simulation data is reference source data for performing optimization adjustment on the preset robot structure.
Further, as shown in fig. 2, before the simulation of the intelligent control module controlling the preset robot to perform the inspection of the predetermined equipment, step S400 of the present application further includes:
step S410: performing multi-feature collection on the preset equipment to obtain equipment feature parameters;
Step S420: establishing an equipment operation map according to the equipment characteristic parameters;
step S430: performing grid division on the equipment operation map to obtain an equipment operation grid diagram, wherein the equipment operation grid diagram comprises M grids, and M is an integer greater than 1;
step S440: sequentially judging and marking the M grids to obtain M grid identifications;
step S450: analyzing the M grid identifications through the path planning unit to obtain a target path plan;
step S460: and taking the target path planning as simulation constraint, and storing the simulation constraint into the motion control unit in the intelligent control module.
Further, the step S440 of the present application further includes:
step S441: acquiring the structural size characteristics of the preset equipment to obtain the structural size parameters of the equipment;
step S442: reading a device top view of the preset device, and combining the device structural dimension parameters to obtain the structural dimension parameters of the device top view;
step S443: grid division is carried out on the equipment top view to obtain an equipment top view grid image, wherein the equipment top view grid image comprises N grids, N is an integer greater than 1, and N is greater than or equal to M;
Step S444: establishing grid mapping relations between the N grids and the M grids;
step S445: and sequentially judging and marking the M grids according to the grid mapping relation and the structural size parameter to obtain M grid identifications.
Further, the step S450 of the present application further includes:
step S451: reading a predetermined position grid of the preset robot;
step S452: the grid at the preset position is used as an originating grid and a destination grid of the preset robot at the same time;
step S453: obtaining a first grid set of the original grids in a preset searching mode, wherein the first grid set comprises P first grids, and P is an integer greater than or equal to 1;
step S454: extracting target first grids in the P first grids, and calculating estimated cost of the target first grids and the target grids to obtain target first estimated cost;
step S455: analyzing the target first estimated cost, and matching an optimal first grid according to an analysis result;
step S456: and obtaining the target path planning based on the optimal first grid.
Further, after the target path planning is obtained based on the optimal first grid, the method further includes step S457, including:
step S4571: acquiring preset target inspection points of the preset equipment, and marking the preset target inspection points to the equipment operation grid diagram to obtain preset target grids, wherein the preset target grids comprise Q target grids, and Q is an integer greater than or equal to 1;
step S4572: extracting a first target grid in the Q target grids, and presetting a first target grid threshold of the first target grid;
step S4573: judging whether the target path planning passes through the first target grid threshold;
step S4574: and if the target path is passed, not adjusting the target path plan, and if the target path is not passed, adjusting the target path plan.
Specifically, the operation characteristic collection, such as an operation area, an operation state and the like, is performed on the predetermined equipment, and the equipment characteristic parameter is used as the basic basis for performing equipment monitoring and inspection. And determining a patrol space domain based on the equipment characteristic parameters according to the running state and the running area, and rubbing a space structure and layout live condition of the patrol space domain to generate the equipment running map. Further, a grid division size is set, namely, a self-defined standard for carrying out region division is set, grid uniform division is carried out on the equipment operation map based on the grid division size, and M grids which are spliced continuously are determined to be used as the equipment operation grid map.
And further judging and marking the M grids in sequence, and specifically, acquiring structural dimension characteristics, such as shapes, dimensions and the like, of the space characterization of the preset equipment as structural dimension parameters of the equipment. And reading the equipment top view of the preset equipment, matching with the equipment structural dimension parameters, and extracting plane top view parameters such as directional contour dimensions, shapes and the like as the structural dimension parameters of the equipment top view. And setting a top view grid division size, namely a self-defined standard for top view division, wherein the top view grid division size is smaller than the grid division size, for example, setting 1:5 as the size ratio of the top view grid division size to the grid division size, uniformly dividing the grid of the equipment top view based on the top view grid division size, and determining the N grids. Mapping and corresponding the M grids and the N grids, and determining a corresponding sequence of each grid in the M grids and at least one grid in the N grids as the grid mapping relation. Based on the grid mapping relation, the mapping judgment of grids with different size divisions is carried out, and the accuracy and fineness of the subsequent grid judgment analysis can be effectively improved. Further, based on the grid mapping relation and the structural size parameter, performing occupation judgment on the M grids, namely whether the grid area has objects to prevent passing, and performing grid identification based on a judgment result, wherein 1 and 0 are used as identification information to perform free identification and occupation identification on the grids, and the occupation identification refers to the grids which are occupied by equipment components and cannot be passed by a robot; the free identifiers are grids through which the robot can move, and the M grid identifiers are acquired.
Further, the M grid identifications are analyzed through the path planning unit, and a target path plan is obtained. Specifically, grid positioning is performed on the preset robot at the initial stage of inspection, the preset position grid is used as the preset position grid, and meanwhile the preset position grid is used as the starting grid and the target grid of the preset robot, namely the initial position of inspection and the final position of inspection for the bypass of the preset equipment. And searching the lower grids of the initial grids based on the preset searching mode, and acquiring P first grid integration as the first grid set, wherein the preset searching mode is to perform the grid searching with the existence of patrol executable in all directions by taking a circle around the preset equipment as a grid searching target.
Randomly extracting a grid from the P first grids as the target first grid, performing estimation cost calculation on the target first grid and the target grid, specifically, performing estimation cost calculation based on the following formula,，/>，)，/>wherein->Refers to the Euclidean distance between the target first grid and the target grid, +.>Means the Manhattan distance of the target first grid from the target grid, < > >Means chebyshev distance of said target first grid from said target grid,/->Refers to the target first estimated cost,) Refers to the coordinates of the first grid of the object, < >>) The coordinates of the target grid are referred to, the parameters can be determined by direct mapping, and the first estimated cost of the target of the first grid is calculated by combining the formula. And respectively carrying out estimation cost calculation on the first grid sets, determining an estimation cost set, carrying out correction, sequencing from small to large, and selecting a first grid corresponding to the minimum estimation cost as the optimal first grid. Similarly, based on the grid searching and screening mode, estimating cost calculation and optimal matching are performed on the lower inspection grid of the optimal first grid, wherein in the grid searching process, the direction corresponding to the upper grid is locked, and reverse searching of the grid is avoided. And so on until the preset equipment is wound for a circle, sequentially ordering the sequentially determined optimal grids to generate the target path plan, wherein the target path plan is an optimal path meeting the inspection requirement, is the path to be inspected of the preset robot, and is the target path gauge And drawing the motion control unit as simulation constraint and storing the simulation constraint into the intelligent control module.
Further, the preset target inspection points are positioned in the equipment operation grid diagram, grids with the preset target inspection points are determined to exist and marked, and Q target grids are obtained to serve as the preset target grids. And matching the Q target grids with the optimal first grid, determining the first target grid, and setting the threshold of the first target grid based on the marks in the first target grid, namely the grid area of the preset target inspection point marked in the grid. Judging whether the target path planning passes through the first target grid threshold, if so, indicating that the marked preset target inspection point is an inspection necessary point location, and not adjusting the target path planning; if the monitoring point does not pass through, the target path planning is indicated to not pass through the necessary monitoring point, the target path planning is locally adjusted, and the coverage of the monitoring point of the target path planning is ensured.
Step S500: analyzing the inspection simulation data, obtaining an optimizing result of the preset structural parameter according to an analysis result, and marking the optimizing result as the optimal structural parameter;
further, as shown in fig. 3, the optimizing result of the predetermined structural parameter is obtained according to the analysis result and is recorded as the optimal structural parameter, and step S500 of the present application further includes:
step S510: acquiring the planned path width of the target path, and analyzing the structure width of the preset robot based on the path width to obtain an optimal structure width;
step S520: sequentially detecting the heights of all the inspection points in the preset target inspection points to obtain preset target inspection point heights, wherein the preset target inspection point heights comprise a plurality of height values of a plurality of inspection points;
step S530: comparing the plurality of height values to a highest value, and analyzing the structural height of the preset robot by using the highest value to obtain an optimal structural height;
step S540: and taking the optimal structure width and the optimal structure height as the optimal structure parameters.
Specifically, the inspection simulation data are analyzed, whether the preset structural parameters of the preset robot are consistent with inspection live or not is analyzed, so that adaptive structural parameter adjustment is performed, and the optimal structural parameters are determined. Specifically, extracting a path width based on the target planning path, determining a minimum path width as a limiting condition, analyzing the structural width of the preset robot, and taking the minimum path width as the optimal structural width; and sequentially detecting the height of the inspection points of the preset target inspection points, and statistically integrating a plurality of height values of each inspection point in the preset target inspection points to serve as the height of the preset target inspection points. And checking the plurality of height values, determining the highest value as a limiting factor, carrying out structural height analysis on the preset robot, taking the highest value as the optimal structural height, taking the optimal structural width and the optimal structural height as adaptive structural parameters meeting the inspection requirement, taking the optimal structural width and the optimal structural height as the optimal structural parameters, and carrying out structural optimization on the preset robot based on the optimal structural parameters so as to ensure the inspection scene conformity of the optimized structure.
Step S600: the intelligent patrol module is reversely matched with the second functional requirement, wherein the intelligent patrol module is internally embedded with optimal patrol parameters;
step S700: and carrying out structural optimization on the preset robot by combining the optimal structural parameters and the optimal inspection parameters.
Specifically, the intelligent inspection module is an intelligent inspection control module of the preset robot, inspection control parameters are built in the intelligent inspection control module, image acquisition equipment, starting time and closing time corresponding to all inspection points are determined in combination with the target path planning, the optimal inspection parameters are used as the optimal inspection parameters, the optimal inspection parameters are embedded into the intelligent inspection module, and the intelligent inspection module corresponding to the second functional requirement is matched to perform inspection control of the preset robot. And the optimal structural parameters and the optimal inspection parameters are execution parameters with highest degree of fit with inspection scenes, and are used as optimization targets to carry out structural optimization on the preset robot, so that the task execution suitability of the optimization result of the preset robot is ensured to the maximum extent.
Further, the intelligent inspection module has embedded therein the optimal inspection parameters, and step S600 of the present application further includes:
Step S610: adapting a plurality of image acquisition devices based on the plurality of height values of the plurality of inspection points, and correspondingly installing the plurality of image acquisition devices to the preset robot;
step S620: acquiring a first image acquisition device in the plurality of image acquisition devices and matching a first inspection point of the first image acquisition device;
step S630: combining the equipment operation grid graph to obtain a second target grid of the first inspection point, and obtaining a second target grid threshold of the second target grid;
step S640: acquiring a preset advancing speed of the preset robot, and combining the target path planning to obtain a predicted advancing time length from the preset robot to the second target grid threshold;
step S650: starting and setting the first image acquisition equipment according to the predicted travelling time length to obtain a first starting time;
step S660: acquiring a preset inspection image sampling scheme, wherein the preset inspection image sampling scheme comprises preset sampling frequency and preset sampling quantity;
step S670: determining a first off time according to the predetermined sampling frequency, the predetermined number of samples, and the first on time;
Step S680: taking the first starting time and the first closing time as first optimal inspection parameters of the first image acquisition equipment;
step S690: and obtaining the optimal inspection parameters according to the first optimal inspection parameters.
Specifically, the plurality of height values of the plurality of inspection points are summarized and integrated to determine a plurality of monitoring heights, a plurality of image acquisition devices are configured, the plurality of image acquisition devices are the same as the plurality of monitoring height values, the plurality of image acquisition devices are respectively installed at the plurality of monitoring heights of the preset robot, a first preset target inspection point under path inspection is determined based on the target path planning and the preset target inspection point, the first image acquisition device is determined based on the monitoring height corresponding to the inspection point, and the inspection point is used as the first inspection point.
Further, the second target grid is an identification grid corresponding to the next preset target inspection point after the first inspection point, and the identified monitoring area is used as the second target grid threshold. And acquiring a preset travelling speed of the preset robot, namely a preset inspection speed, determining a route distance from the first inspection point to the second target grid threshold in combination with the target path planning, and dividing the route distance by the preset travelling speed to determine the predicted travelling duration. Acquiring the starting demand time of the first image acquisition equipment, determining a complete equipment starting time node from the first image acquisition equipment to the second target grid threshold starting based on the predicted travelling time, and taking the complete equipment starting time node as the first starting time, and ensuring the continuity of inspection and the completeness of monitoring data by carrying out equipment starting analysis.
Further, the preset sampling frequency and the preset sampling number are sampling schemes preset based on monitoring requirements, the sampling schemes of different inspection points are different, the preset inspection image sampling scheme of the point to be inspected is obtained, sampling time length meeting the preset sampling frequency and the preset sampling number is analyzed, the first closing time meeting the sampling time length is determined, namely, after the first starting time, a time node of the starting time and the sampling time length is continued. And taking the first starting time and the first closing time as the first optimal inspection parameters of the first image acquisition equipment. And respectively carrying out image acquisition equipment determination and starting time and closing time analysis aiming at each inspection point, and integrally determining the optimal inspection parameters for realizing optimal inspection control.
Example two
Based on the same inventive concept as the robot structural optimization method in combination with demand analysis in the foregoing embodiments, as shown in fig. 4, the present application provides a robot structural optimization system in combination with demand analysis, the system comprising:
the task reading module 11 is used for reading a preset task of a preset robot, wherein the preset task is an intelligent inspection task for monitoring the running state of preset equipment;
The task decomposition module 12 is configured to decompose and analyze the intelligent patrol task to obtain a predetermined function requirement, where the predetermined function requirement includes a first function requirement and a second function requirement;
the construction module 13 is configured to analyze a path planning requirement in the first functional requirement and construct a path planning unit, analyze a motion control requirement in the second functional requirement and construct a motion control unit, and form an intelligent control module;
the inspection simulation module 14 is configured to simulate the intelligent control module to control the preset robot to perform inspection of the predetermined device, so as to obtain inspection simulation data, where the preset robot has predetermined structural parameters;
the parameter optimizing module 15 is used for analyzing the inspection simulation data, obtaining an optimizing result of the preset structural parameter according to an analysis result, and recording the optimizing result as an optimal structural parameter;
the matching module 16 is configured to reversely match the intelligent inspection module with the second functional requirement, where the intelligent inspection module has optimal inspection parameters embedded therein;
The structure optimization module 17, the structure optimization module 17 is configured to perform structure optimization on the preset robot by combining the optimal structure parameter and the optimal inspection parameter.
Further, the system further comprises:
the device characteristic acquisition module is used for carrying out multi-characteristic acquisition on the preset device to obtain device characteristic parameters;
the map building module is used for building an equipment operation map according to the equipment characteristic parameters;
the equipment operation grid diagram acquisition module is used for carrying out grid division on the equipment operation map to obtain an equipment operation grid diagram, wherein the equipment operation grid diagram comprises M grids, and M is an integer greater than 1;
the grid marking module is used for sequentially judging and marking the M grids to obtain M grid identifications;
the path planning module is used for analyzing the M grid identifications through the path planning unit to obtain a target path plan;
and the path storage module is used for taking the target path planning as simulation constraint and storing the target path planning into the motion control unit in the intelligent control module.
Further, the system further comprises:
the structure size acquisition module is used for acquiring the structure size characteristics of the preset equipment to obtain the structure size parameters of the equipment;
the overhead view parameter acquisition module is used for reading the equipment overhead view of the preset equipment and combining the equipment structural size parameters to obtain the structural size parameters of the equipment overhead view;
the equipment overlook grid image acquisition module is used for meshing the equipment overlook grid image to obtain an equipment overlook grid image, wherein the equipment overlook grid image comprises N grids, N is an integer greater than 1, and N is greater than or equal to M;
the mapping relation establishing module is used for establishing grid mapping relation between the N grids and the M grids;
and the marking module is used for sequentially judging and marking the M grids according to the grid mapping relation and the structural size parameter to obtain M grid identifications.
Further, the system further comprises:
the grid reading module is used for reading a grid at a preset position of the preset robot;
The grid setting module is used for simultaneously taking the grid at the preset position as an originating grid and a destination grid of the preset robot;
the first grid set acquisition module is used for obtaining a first grid set of the original grid through a preset searching mode, wherein the first grid set comprises P first grids, and P is an integer greater than or equal to 1;
the estimated cost calculation module is used for extracting target first grids in the P first grids, calculating estimated cost of the target first grids and the target grids, and obtaining target first estimated cost;
the optimal first grid matching module is used for analyzing the target first estimated cost and matching the optimal first grid according to an analysis result;
and the target path planning acquisition module is used for acquiring the target path plan based on the optimal first grid.
Further, the system further comprises:
the device comprises a preset target grid acquisition module, a detection module and a control module, wherein the preset target grid acquisition module is used for acquiring preset target inspection points of the preset device, marking the preset target inspection points to the device operation grid diagram to obtain preset target grids, wherein the preset target grids comprise Q target grids, and Q is an integer greater than or equal to 1;
A first target grid threshold setting module for extracting a first target grid of the Q target grids and presetting a first target grid threshold of the first target grid;
the target path planning judging module is used for judging whether the target path planning passes through the first target grid threshold or not;
and the target path planning adjustment module is used for not adjusting the target path planning if the target path planning adjustment module passes through, and adjusting the target path planning if the target path planning adjustment module does not pass through.
Further, the system further comprises:
the structure width analysis module is used for acquiring the path width planned by the target path, and analyzing the structure width of the preset robot based on the path width to obtain the optimal structure width;
the height detection module is used for sequentially detecting the heights of all the inspection points in the preset target inspection points to obtain preset target inspection point heights, wherein the preset target inspection point heights comprise a plurality of height values of a plurality of inspection points;
the structure height analysis module is used for comparing the plurality of height values to a highest value, and analyzing the structure height of the preset robot by the highest value to obtain an optimal structure height;
And the optimal structure parameter acquisition module is used for taking the optimal structure width and the optimal structure height as the optimal structure parameters.
Further, the system further comprises:
the equipment installation module is used for adapting a plurality of image acquisition equipment based on the height values of the inspection points and correspondingly installing the image acquisition equipment to the preset robot;
the first inspection point matching module is used for acquiring first image acquisition equipment in the plurality of image acquisition equipment and matching first inspection points of the first image acquisition equipment;
a second target grid threshold acquisition module, configured to obtain a second target grid of the first inspection point in conjunction with the equipment operation grid graph, and obtain a second target grid threshold of the second target grid;
the predicted travel time length acquisition module is used for acquiring the preset travel speed of the preset robot and combining the target path planning to obtain the predicted travel time length from the preset robot to the second target grid threshold;
The first starting time acquisition module is used for starting and setting the first image acquisition equipment according to the predicted travelling time length to obtain a first starting time;
the scheme acquisition module is used for acquiring a preset inspection image sampling scheme, wherein the preset inspection image sampling scheme comprises preset sampling frequency and preset sampling quantity;
the first closing time determining module is used for determining a first closing time according to the preset sampling frequency, the preset sampling number and the first starting time;
the first optimal inspection parameter determining module is used for taking the first starting time and the first closing time as first optimal inspection parameters of the first image acquisition equipment;
and the parameter acquisition module is used for acquiring the optimal inspection parameters according to the first optimal inspection parameters.
In the present disclosure, through the foregoing detailed description of a method for optimizing a robot structure in combination with demand analysis, those skilled in the art may clearly know a method and a system for optimizing a robot structure in combination with demand analysis in this embodiment, and for a device disclosed in the embodiment, since the device corresponds to a method disclosed in the embodiment, the description is relatively simple, 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 (8)
1. The robot structure optimization method combined with the demand analysis is characterized by comprising the following steps of:
reading a preset task of a preset robot, wherein the preset task refers to an intelligent patrol task for monitoring the running state of preset equipment;
decomposing and analyzing the intelligent inspection task to obtain preset functional requirements, wherein the preset functional requirements comprise a first functional requirement and a second functional requirement;
analyzing the path planning requirements in the first functional requirements and constructing a path planning unit, analyzing the motion control requirements in the second functional requirements and constructing a motion control unit to form an intelligent control module;
Simulating the inspection of the preset equipment by controlling the preset robot by the intelligent control module to obtain inspection simulation data, wherein the preset robot has preset structural parameters;
analyzing the inspection simulation data, obtaining an optimizing result of the preset structural parameter according to the analyzing result, and marking the optimizing result as an optimal structural parameter, wherein the optimal structural parameter is a setting parameter of an optimal space range of a preset robot inspection structure based on inspection scene requirements;
the intelligent inspection module is reversely matched with the second functional requirement, wherein the intelligent inspection module is internally embedded with optimal inspection parameters, and the optimal inspection parameters refer to setting parameters of image acquisition equipment, starting time and closing time corresponding to all inspection points when inspection is performed based on target path planning;
and carrying out structural optimization on the preset robot by combining the optimal structural parameters and the optimal inspection parameters.
2. The robot configuration optimization method according to claim 1, characterized by comprising, before said simulating the inspection of the predetermined equipment by the intelligent control module controlling the predetermined robot:
Performing multi-feature collection on the preset equipment to obtain equipment feature parameters;
establishing an equipment operation map according to the equipment characteristic parameters;
performing grid division on the equipment operation map to obtain an equipment operation grid diagram, wherein the equipment operation grid diagram comprises M grids, and M is an integer greater than 1;
sequentially judging and marking the M grids to obtain M grid identifications;
analyzing the M grid identifications through the path planning unit to obtain a target path plan;
and taking the target path planning as simulation constraint, and storing the simulation constraint into the motion control unit in the intelligent control module.
3. The method for optimizing a structure of a robot according to claim 2, wherein the sequentially performing judgment and marking on the M grids to obtain M grid identifications includes:
acquiring the structural size characteristics of the preset equipment to obtain the structural size parameters of the equipment;
reading a device top view of the preset device, and combining the device structural dimension parameters to obtain the structural dimension parameters of the device top view;
grid division is carried out on the equipment top view to obtain an equipment top view grid image, wherein the equipment top view grid image comprises N grids, N is an integer greater than 1, and N is greater than or equal to M;
Establishing grid mapping relations between the N grids and the M grids;
and sequentially judging and marking the M grids according to the grid mapping relation and the structural size parameter to obtain M grid identifications.
4. The method for optimizing a robot structure according to claim 2, wherein the analyzing, by the path planning unit, the M grid identifications to obtain a target path plan includes:
reading a predetermined position grid of the preset robot;
the grid at the preset position is used as an originating grid and a destination grid of the preset robot at the same time;
obtaining a first grid set of the original grids in a preset searching mode, wherein the first grid set comprises P first grids, and P is an integer greater than or equal to 1;
extracting target first grids in the P first grids, and calculating estimated cost of the target first grids and the target grids to obtain target first estimated cost;
analyzing the target first estimated cost, and matching an optimal first grid according to an analysis result;
and obtaining the target path planning based on the optimal first grid.
5. The method of claim 4, further comprising, after the obtaining the target path plan based on the optimal first grid:
Acquiring preset target inspection points of the preset equipment, and marking the preset target inspection points to the equipment operation grid diagram to obtain preset target grids, wherein the preset target grids comprise Q target grids, and Q is an integer greater than or equal to 1;
extracting a first target grid in the Q target grids, and presetting a first target grid threshold of the first target grid;
judging whether the target path planning passes through the first target grid threshold;
and if the target path is passed, not adjusting the target path plan, and if the target path is not passed, adjusting the target path plan.
6. The method for optimizing a structure of a robot according to claim 5, wherein the obtaining the optimizing result of the predetermined structural parameter according to the analysis result is recorded as the optimal structural parameter, and includes:
acquiring the planned path width of the target path, and analyzing the structure width of the preset robot based on the path width to obtain an optimal structure width;
sequentially detecting the heights of all the inspection points in the preset target inspection points to obtain preset target inspection point heights, wherein the preset target inspection point heights comprise a plurality of height values of a plurality of inspection points;
Comparing the plurality of height values to a highest value, and analyzing the structural height of the preset robot by using the highest value to obtain an optimal structural height;
and taking the optimal structure width and the optimal structure height as the optimal structure parameters.
7. The method for optimizing a structure of a robot according to claim 6, wherein the intelligent inspection module has optimal inspection parameters embedded therein, comprising:
adapting a plurality of image acquisition devices based on the plurality of height values of the plurality of inspection points, and correspondingly installing the plurality of image acquisition devices to the preset robot;
acquiring a first image acquisition device in the plurality of image acquisition devices and matching a first inspection point of the first image acquisition device;
combining the equipment operation grid graph to obtain a second target grid of the first inspection point, and obtaining a second target grid threshold of the second target grid;
acquiring a preset advancing speed of the preset robot, and combining the target path planning to obtain a predicted advancing time length from the preset robot to the second target grid threshold;
starting and setting the first image acquisition equipment according to the predicted travelling time length to obtain a first starting time;
Acquiring a preset inspection image sampling scheme, wherein the preset inspection image sampling scheme comprises preset sampling frequency and preset sampling quantity;
determining a first off time according to the predetermined sampling frequency, the predetermined number of samples, and the first on time;
taking the first starting time and the first closing time as first optimal inspection parameters of the first image acquisition equipment;
and obtaining the optimal inspection parameters according to the first optimal inspection parameters.
8. A robotic architecture optimization system incorporating demand analysis, comprising:
the task reading module is used for reading a preset task of a preset robot, wherein the preset task is an intelligent patrol task for monitoring the running state of preset equipment;
the task decomposition module is used for decomposing and analyzing the intelligent inspection task to obtain a preset function requirement, wherein the preset function requirement comprises a first function requirement and a second function requirement;
the construction module is used for analyzing the path planning requirements in the first functional requirements and constructing a path planning unit, analyzing the motion control requirements in the second functional requirements and constructing a motion control unit to form an intelligent control module;
The inspection simulation module is used for simulating the inspection of the preset robot by the intelligent control module to obtain inspection simulation data, wherein the preset robot has preset structural parameters;
the parameter optimizing module is used for analyzing the inspection simulation data, obtaining an optimizing result of the preset structural parameter according to the analyzing result, and recording the optimizing result as an optimal structural parameter, wherein the optimal structural parameter is a setting parameter of an optimal space range of a preset robot inspection structure based on inspection scene requirements;
the matching module is used for reversely matching the intelligent inspection module with the second functional requirement, wherein the intelligent inspection module is internally embedded with optimal inspection parameters, and the optimal inspection parameters refer to setting parameters of image acquisition equipment, starting time and closing time corresponding to all inspection points when inspection is performed based on target path planning;
the structure optimization module is used for carrying out structure optimization on the preset robot by combining the optimal structure parameter and the optimal inspection parameter.
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