CN117762149B - Slag dragging robot control method, device, equipment and medium - Google Patents
Slag dragging robot control method, device, equipment and medium Download PDFInfo
- Publication number
- CN117762149B CN117762149B CN202410194636.8A CN202410194636A CN117762149B CN 117762149 B CN117762149 B CN 117762149B CN 202410194636 A CN202410194636 A CN 202410194636A CN 117762149 B CN117762149 B CN 117762149B
- Authority
- CN
- China
- Prior art keywords
- path
- node
- point
- slag dragging
- starting point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002893 slag Substances 0.000 title claims abstract description 108
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000012545 processing Methods 0.000 claims abstract description 22
- 230000000694 effects Effects 0.000 claims abstract description 19
- 238000011156 evaluation Methods 0.000 claims abstract description 13
- 238000003860 storage Methods 0.000 claims description 10
- 230000004888 barrier function Effects 0.000 claims description 9
- 230000007613 environmental effect Effects 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 5
- 230000003993 interaction Effects 0.000 claims description 4
- 230000006835 compression Effects 0.000 claims description 3
- 238000007906 compression Methods 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
- 230000009191 jumping Effects 0.000 claims description 3
- 238000001228 spectrum Methods 0.000 claims description 3
- 238000001931 thermography Methods 0.000 claims description 3
- 238000012876 topography Methods 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 2
- 238000004891 communication Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 239000003638 chemical reducing agent Substances 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000012636 effector Substances 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Manipulator (AREA)
Abstract
The disclosure relates to a method, a device, equipment and a medium for controlling a slag dragging robot, and relates to the technical field of intelligent control, wherein the method comprises the following steps: the slag dragging path strategy generator acquires environment information and generates an initial obstacle avoidance path between a path starting point and a path ending point; the slag dragging path effect evaluator scores the initial obstacle avoidance path according to path length, coverage rate and smoothness evaluation indexes, and sets different weights for the indexes; if the score reaches a preset value, generating a target path; otherwise, repeating the initial steps until the score reaches a preset value; dynamic window processing is used on the target path to generate a smooth and continuous motion trail. By adopting the scheme, the intelligent control and the accurate path planning of the slag dragging robot can be realized, so that the safety and the accuracy of a working result are ensured.
Description
Technical Field
The disclosure relates to the technical field of intelligent control, in particular to a method, a device, equipment and a medium for controlling a slag dragging robot.
Background
The slag dragging robot is an important auxiliary device in modern industrial production, and can realize high-efficiency and low-cost operation in high-temperature, high-pressure and severe environments.
The traditional slag dragging robot mainly adopts the following two types of motion control methods:
1. Presetting a fixed repetition path
The method needs to manually determine the operation area of the slag robot and a plurality of repeated paths which are covered in parallel, and the paths contain the coordinate information of a large number of road points. When executing tasks, the robot sequentially accesses the points along the path in sequence to complete repeated traversal of the designated region.
The fixed track planning is simple to set, but only can process a small-range area of a regular rectangle, and has poor adaptability to complex terrains. And the redundant operation of the repeated path is large and the efficiency is low.
2. Remote control
The motion trail of the robot is manually controlled by using camera visual feedback in a manual remote control mode. The special personnel is required to operate in a unattended manner, so that the labor intensity is high; meanwhile, due to view angle limitation, tracking control of the whole area is difficult to realize.
In the prior art, the motion control of the slag dragging robot mostly needs manual support, the efficiency is limited, the intelligent control and path planning of the slag dragging robot cannot be realized, and breakthrough and optimization are needed urgently.
Disclosure of Invention
The disclosure provides a method, a device, equipment and a medium for controlling a slag dragging robot, which are used for realizing intelligent control and path planning of the slag dragging robot.
In a first aspect, the present disclosure provides a method of controlling a slag dragging robot, comprising:
Step 1: the slag dragging path strategy generator acquires environment information and generates an initial obstacle avoidance path between a path starting point and a path ending point; the generating the initial obstacle avoidance path includes:
acquiring a starting point and an ending point of the slag dragging robot;
establishing a circle taking a starting point and an ending point as diameters, and dividing the circle into two semicircles according to the diameters;
respectively calculating the density of the barriers in the two semicircles;
generating a node tree by taking a starting point as an original node: firstly, generating a random node in a semicircle with smaller obstacle concentration by a preset angle, finding a node closest to the random node in known nodes as an adjacent node, connecting the random node with the adjacent node to generate a smooth curve, taking the random node as a starting point on the smooth curve, and selecting a node with a designated length as a new node to be added into a node tree;
Generating the same node tree by taking the end point as the original node until the nodes of the two node trees which respectively take the start point and the end point as the original node are connected;
Step 2: the slag dragging path effect evaluator scores the initial obstacle avoidance path according to path length, coverage rate and smoothness evaluation indexes, and sets different weights for the indexes;
step 3: if the score reaches a preset value, generating a target path; otherwise, repeating the step 1 until the score reaches a preset value;
step 4: dynamic window processing is used on the target path to generate a smooth and continuous motion trail.
According to the slag robot control method provided by the disclosure, generating the initial obstacle avoidance path further includes node compression:
respectively calculating the depths of two node trees generated by taking a starting point and an ending point as original nodes, and merging the tree with smaller depth into the tree with larger depth;
And/or the number of the groups of groups,
After the nodes of the two node trees with the starting point and the ending point as the original nodes are connected, the nodes are restored by taking the connected nodes of the two node trees as root nodes.
According to the slag dragging robot control method provided by the disclosure, the RGB camera and the infrared thermal imaging camera are used for acquiring image data of a target slag dragging area from different spectrums, and a two-dimensional grid map is constructed through an image processing algorithm; a sonar sensor is used for acquiring three-dimensional point cloud information, and a target slag-fishing area topography and an obstacle model are obtained;
And setting a voice interaction interface, wherein a user can set starting and ending points, tolerance and regional parameter requirements of slag dragging through voice instructions.
The slag dragging robot control method provided by the disclosure further comprises the following steps:
(1) Planning a path at the tail end of a mechanical arm of the slag dragging robot, and forming a connecting path avoiding an obstacle between a starting point and a stopping point;
(2) Selecting a path point sequence on a connecting path, and calculating a mechanical arm joint variable sequence corresponding to the path point sequence at the tail end of the mechanical arm through inverse kinematics processing;
(3) Judging whether the variable sequence of the mechanical arm joint also meets the obstacle avoidance condition of the robot, if not, jumping to the step (1) and re-planning; if the condition is met, carrying out the next step;
(4) After the judgment of all the path point sequences is completed, starting from the starting point, each path point simultaneously meets the obstacle avoidance requirements of the tail end of the mechanical arm and the joints of the mechanical arm, and finally, an initial obstacle avoidance path from the starting point to the ending point is formed.
According to the slag dragging robot control method provided by the disclosure, the path length weight is a, the coverage rate weight is b, and the smoothness weight is c;
The path length score calculated by the slag dragging path effect evaluator is m, the coverage rate score is n, and the smoothness score is p; s is a score;
scoring was performed according to the following formula:
。
According to the control method of the slag dragging robot, a window with an odd length is set, coordinate values of all path points in the window are obtained, an average value of the coordinate values is calculated, and coordinate values of the middle position of the window are replaced by the average value;
calculating the obstacle concentration of a coordinate point at the middle position of the window in real time, and translating the sliding window with a first preset step length when the obstacle concentration is large; when the density of the barriers is smaller, translating the sliding window with a second preset step length; the first preset step length is smaller than the second preset step length;
Until the slide covers all the path points.
According to the slag dragging robot control method provided by the disclosure, when the obstacle is updated or the termination point is changed, the step 1 is triggered again to generate a new initial obstacle avoidance path, and the steps are repeated to re-plan the path.
In a second aspect, the present disclosure also provides a slag dragging robot control device, including:
The slag dragging path strategy generation module: the slag dragging path strategy generator acquires environment information and generates an initial obstacle avoidance path between a path starting point and a path ending point; the generating the initial obstacle avoidance path includes:
acquiring a starting point and an ending point of the slag dragging robot;
establishing a circle taking a starting point and an ending point as diameters, and dividing the circle into two semicircles according to the diameters;
respectively calculating the density of the barriers in the two semicircles;
generating a node tree by taking a starting point as an original node: firstly, generating a random node in a semicircle with smaller obstacle concentration by a preset angle, finding a node closest to the random node in known nodes as an adjacent node, connecting the random node with the adjacent node to generate a smooth curve, taking the random node as a starting point on the smooth curve, and selecting a node with a designated length as a new node to be added into a node tree;
Generating the same node tree by taking the end point as the original node until the nodes of the two node trees which respectively take the start point and the end point as the original node are connected;
And a slag dragging path effect evaluation module: the slag dragging path effect evaluator scores the initial obstacle avoidance path according to path length, coverage rate and smoothness evaluation indexes, and sets different weights for the indexes;
And a judging module: if the score reaches a preset value, generating a target path; otherwise, repeatedly setting a slag dragging path strategy generating device until the score reaches a preset value;
and a smoothing module: dynamic window processing is used on the target path to generate a smooth and continuous motion trail.
Compared with the prior art, the slag dragging robot control method, the slag dragging robot control device, the slag dragging robot control equipment and the slag dragging robot control medium have the advantages that the slag dragging path strategy generator obtains environmental information, and an initial obstacle avoidance path is generated between a path starting point and a path ending point; the slag dragging path effect evaluator scores the initial obstacle avoidance path according to path length, coverage rate and smoothness evaluation indexes, and sets different weights for the indexes; if the score reaches a preset value, generating a target path; otherwise, repeating the initial steps until the score reaches a preset value; dynamic window processing is used on the target path to generate a smooth and continuous motion trail. By adopting the scheme, the intelligent control and the accurate path planning of the slag dragging robot can be realized, so that the safety and the accuracy of a working result are ensured.
Drawings
In order to more clearly illustrate the technical solutions of the present disclosure, the following description will briefly introduce the drawings required for embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow chart of a method of controlling a slag dragging robot provided by the present disclosure;
FIG. 2 is a schematic illustration of an initial obstacle avoidance path provided by the present disclosure;
FIG. 3 is a schematic diagram of a slag robot control device provided by the present disclosure;
Fig. 4 is a frame structure diagram of an electronic device provided by the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present disclosure more apparent, the technical solutions in the present disclosure will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are some, but not all, embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
Fig. 1 is a flowchart of a control method of a slag dragging robot provided in the present disclosure, as shown in fig. 1, the method includes:
step 1: the slag dragging path strategy generator acquires environment information and generates an initial obstacle avoidance path between a path starting point and a path ending point;
As shown in fig. 2, the generating the initial obstacle avoidance path includes:
acquiring a starting point (S) and an ending point (E) of the slag dragging robot;
Establishing a circle taking a starting point and an ending point as diameters, and dividing the circle into two semicircles according to the diameters; the dashed lines in fig. 2 are diameters of the connection start point and the connection end point, with which the circle is divided into two semicircles C1, C2;
Respectively calculating the density of the barriers in the two semicircles; the obstacle concentration refers to the degree of concentration of the obstacle encountered by the slag robot in the environment, and "x" in fig. 2 indicates the obstacle. An obstacle in the environment can be detected by using a sensor: the slag robot is typically equipped with various sensors, such as infrared sensors, ultrasonic sensors, etc., which can detect obstacles in the environment and calculate the position and number of the obstacles, thereby calculating the obstacle concentration. Or using a machine learning algorithm: the model may be trained using machine learning algorithms that enable it to automatically calculate the obstacle concentration of the robot in different environments. For example, a model may be trained using a deep learning algorithm that enables it to automatically detect obstacles in the environment and calculate their concentration.
Generating a node tree by taking a starting point (S) as an original node: firstly, generating a random node in a semicircle with smaller obstacle concentration (C1) at a preset angle, wherein the preset angle determines the direction of the generated random node, if the preset angle is too steep, the generated random node can be far away from the target termination point, and the random node can be more towards the target termination point by a proper preset angle; finding a node closest to the random node from the known nodes as an adjacent node, connecting the random node with the adjacent node to generate a smooth curve, taking the random node as a starting point on the smooth curve, selecting a node with a designated length as a new node, and adding the new node into a node tree;
generating the same node tree by taking the end point (E) as an original node until nodes of two node trees respectively taking the start point and the end point as the original node are connected;
Step 2: the slag dragging path effect evaluator scores the initial obstacle avoidance path according to path length, coverage rate and smoothness evaluation indexes, and sets different weights for the indexes;
in the path evaluation process, multiple factors such as indexes of path length, coverage rate, smoothness and the like need to be comprehensively considered, and the indexes can be used for evaluating whether the quality of an initial obstacle avoidance path of the slag dragging robot meets the standard or not;
The path length, coverage rate and smoothness are path planning evaluation indexes used for evaluating the quality of the path. The specific meaning is as follows:
Path length: path length refers to the distance or total distance from a start point to an end point, and is typically used to measure the length and distance of a path. In path planning, the shorter the path length, the better the quality of the path is explained.
Coverage rate: coverage refers to the number of nodes or locations that a path passes through, or the area or volume covered by the path. In path planning, the higher the coverage if the path covers the desired node or location.
Smoothness: smoothness refers to the continuity and smoothness of the path. In path planning, the smoothness is higher if the path is continuous and gentle.
In addition, the slag dragging path effect evaluator can be customized according to different task requirements so as to adapt to different scenes. For example, in some scenarios, the robot may need to complete the slag-fishing task as quickly as possible, at which point the path length is weighted higher; in other situations, the robot may need to complete as many slag-fishing tasks as possible, and the corresponding coverage weight may be high. Therefore, the slag dragging path effect evaluator needs to set different weights according to task requirements so as to meet the requirements of the slag dragging robot in different scenes.
Step 3: if the score reaches a preset value, generating a target path; otherwise, repeating the step 1 until the score reaches a preset value;
And (3) if the score of the initial obstacle avoidance path does not reach a preset value, repeating the step (1) until the score reaches the preset value. This process may be implemented through multiple iterations to ensure that the slag robot is able to generate a high quality target path. In each iteration, the initial obstacle avoidance path can be updated according to the real-time state of the robot, environmental change and other factors, and path planning and scoring can be conducted again.
Step 4: dynamic window processing is used on the target path to generate a smooth and continuous motion trail.
The real-time tracking of the target path and the generation of the smooth motion track are realized by continuously and dynamically adjusting the size of the window, calculating the motion track and updating the track.
Compared with the prior art, the slag dragging path strategy generator acquires environmental information, and generates an initial obstacle avoidance path between a path starting point and a path ending point; the slag dragging path effect evaluator scores the initial obstacle avoidance path according to path length, coverage rate and smoothness evaluation indexes, and sets different weights for the indexes; if the score reaches a preset value, generating a target path; otherwise, repeating the initial step 1 until the score reaches a preset value; dynamic window processing is used on the target path to generate a smooth and continuous motion trail. By adopting the scheme, the path planning of the slag dragging robot can be scientifically carried out, and the robot is helped to more effectively finish the slag dragging task.
In one embodiment, the number of nodes in the node tree in step 1 is very large, which seriously affects the use of storage space and the search efficiency. The generating the initial obstacle avoidance path further includes node compression:
respectively calculating the depths of two node trees generated by taking a starting point and an ending point as original nodes, and merging the tree with smaller depth into the tree with larger depth;
And/or the number of the groups of groups,
After the nodes of the two node trees with the starting point and the ending point as the original nodes are connected, the nodes are restored by taking the connected nodes of the two node trees as root nodes.
By compressing the nodes by the two methods, the number of the nodes can be reduced, so that the storage space is reduced, and meanwhile, the searching times can be reduced, so that the searching efficiency is improved.
In one embodiment, the slag path strategy generator obtains environmental information including:
acquiring image data of a target slag fishing area from different spectrums by using an RGB camera and an infrared thermal imaging camera, and constructing a two-dimensional grid map through an image processing algorithm; a sonar sensor is used for acquiring three-dimensional point cloud information, and a target slag-fishing area topography and an obstacle model are obtained;
And setting a voice interaction interface, wherein a user can set starting and ending points, tolerance and regional parameter requirements of slag dragging through voice instructions.
The starting point and the ending point of the slag robot refer to the starting and ending positions of the robot when the robot executes the slag-fishing task, and the working range of the slag robot is defined so as to operate in a given area. Tolerance refers to the adaptability of the slag dragging robot to environmental changes, including the tolerance to temperature, pressure, concentration and other changes. The regional parameter requirement information refers to parameter limits which need to be met when the robot works, and the regional parameter requirement information comprises limits on the operating distance, the height, the angle and the like of the robot so as to prevent the slag dragging robot from crossing the boundary or invading other objects.
In one embodiment, the prior art slag robot obstacle avoidance only considers the obstacle avoidance at the end of the mechanical arm, and ignores the obstacle avoidance at the joint of the mechanical arm. The obstacle avoidance of the tail end of the mechanical arm and the obstacle avoidance of the joint of the mechanical arm are important measures for ensuring the safe and stable operation of the slag dragging robot.
The slag robot needs to avoid the obstacle at the tail end of the mechanical arm and the joint of the mechanical arm so as to ensure that the robot cannot collide with the obstacle when working. The obstacle avoidance of the tail end of the mechanical arm means that the tail end of the mechanical arm can avoid objects or other obstacles close to the operation end of the mechanical arm when the mechanical arm executes a work task so as to ensure the safety and the accuracy of the operation. The obstacle avoidance of the mechanical arm joint means that the whole mechanical arm system can avoid obstacles close to the joint in the moving process. The mechanical arm can be prevented from colliding with surrounding objects in the moving process, so that the stability and the safety of the mechanical arm are guaranteed.
The tail end of the mechanical arm of the slag dragging robot and the joint of the mechanical arm are in a mutually matched and mutually restricted relation. The end of the arm refers to the last joint of the arm, also known as the end effector, which is responsible for completing the movement and operation of the arm. The mechanical arm joints are various moving parts of the mechanical arm, including joint rods, joint bearings, joint reducers and the like, and are responsible for realizing various movement modes of the mechanical arm, such as rotation, straight line, swing and the like.
In the relation between the tail end of the mechanical arm of the slag dragging robot and the joint of the mechanical arm, the tail end of the mechanical arm is responsible for executing slag dragging operation, and the joint of the mechanical arm is responsible for controlling the movement of the tail end of the mechanical arm. The tail end of the mechanical arm is connected with the mechanical arm joint through a joint speed reducer, so that mechanical energy is converted into kinetic energy of an end effector, and slag dragging operation is realized. At the same time, the mutual restriction relationship between the tail end of the mechanical arm and the joint of the mechanical arm is also very important. During operation, if the end of the mechanical arm collides or stops moving, the stability and safety of the whole mechanical arm can be affected. Therefore, when the design of the slag dragging robot is carried out, the motion relation between the tail end of the mechanical arm and the joint of the mechanical arm needs to be reasonably coordinated, so that the stability and the safety of the robot are ensured.
The generating the initial obstacle avoidance path further comprises the following obstacle avoidance steps:
(1) Planning a path at the tail end of a mechanical arm of the slag dragging robot, and forming a connecting path avoiding an obstacle between a starting point and a stopping point;
(2) Selecting a path point sequence on a connecting path, and calculating a mechanical arm joint variable sequence corresponding to the path point sequence at the tail end of the mechanical arm through inverse kinematics processing;
(3) Judging whether the variable sequence of the mechanical arm joint also meets the obstacle avoidance condition of the robot, if not, jumping to the step (1) and re-planning; if the condition is met, carrying out the next step;
(4) After the judgment of all the path point sequences is completed, starting from the starting point, each path point simultaneously meets the requirements of the tail end of the mechanical arm and the obstacle avoidance of the mechanical arm, and finally, an initial obstacle avoidance path from the starting point to the ending point is formed.
In one embodiment, path length, coverage and smoothness are indicators of interactions in path planning. If the path length is short, but coverage is low, the path may not cover all nodes or locations that need to be traversed; if the path length is long, but the smoothness is high, the path may not be continuous enough. Therefore, these three indexes need to be comprehensively considered in path planning to select an optimal path scheme.
The setting different weights for the indicators includes:
the path length weight is a, the coverage rate weight is b, and the smoothness weight is c;
The path length score calculated by the slag dragging path effect evaluator is m, the coverage rate score is n, and the smoothness score is p; s is a score;
scoring was performed according to the following formula:
。
in one embodiment, the employing dynamic window processing on the target path includes:
setting a window with odd length, obtaining coordinate values of all path points in the window, calculating an average value of the coordinate values, and replacing the coordinate value of the middle position of the window with the average value;
calculating the obstacle concentration of a coordinate point at the middle position of the window in real time, and translating the sliding window with a first preset step length when the obstacle concentration is large; when the density of the barriers is smaller, translating the sliding window with a second preset step length; the first preset step length is smaller than the second preset step length;
Until the slide covers all the path points.
In one embodiment, step 4 further comprises:
And when the obstacle update or termination point changes, the step 1 is triggered again to generate a new initial obstacle avoidance path, and the steps are repeated to re-plan the path so as to ensure that the path is safer and more reasonable.
The following describes a slag dragging robot control device provided by the present disclosure, and a detection system described below and a detection method described above may be referred to correspondingly.
As shown in fig. 3, a slag dragging robot control device includes:
The slag dragging path strategy generation module: the slag dragging path strategy generator acquires environment information and generates an initial obstacle avoidance path between a path starting point and a path ending point; the generating the initial obstacle avoidance path includes:
acquiring a starting point and an ending point of the slag dragging robot;
establishing a circle taking a starting point and an ending point as diameters, and dividing the circle into two semicircles according to the diameters;
respectively calculating the density of the barriers in the two semicircles;
generating a node tree by taking a starting point as an original node: firstly, generating a random node in a semicircle with smaller obstacle concentration by a preset angle, finding a node closest to the random node in known nodes as an adjacent node, connecting the random node with the adjacent node to generate a smooth curve, taking the random node as a starting point on the smooth curve, and selecting a node with a designated length as a new node to be added into a node tree;
Generating the same node tree by taking the end point as the original node until the nodes of the two node trees which respectively take the start point and the end point as the original node are connected;
And a slag dragging path effect evaluation module: the slag dragging path effect evaluator scores the initial obstacle avoidance path according to path length, coverage rate and smoothness evaluation indexes, and sets different weights for the indexes;
And a judging module: if the score reaches a preset value, generating a target path; otherwise, repeatedly setting a slag dragging path strategy generating device until the score reaches a preset value;
and a smoothing module: dynamic window processing is used on the target path to generate a smooth and continuous motion trail.
As shown in fig. 4, an electronic device provided by an embodiment of the present application includes a processor 402 and a memory 401, where the memory stores a computer program that can be run on the processor, and the processor implements the steps of the method provided by the foregoing embodiment when executing the computer program.
Referring to fig. 4, the electronic device further includes: a bus 403 and a communication interface 404, the processor 402, the communication interface 404 and the memory 401 being connected by the bus 403; the processor 402 is used to execute executable modules, such as computer programs, stored in the memory 401.
The memory 401 may include a high-speed random access memory (Random Access Memory, abbreviated as RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 404 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 403 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
The memory 401 is configured to store a program, and the processor 402 executes the program after receiving an execution instruction, and a method executed by the apparatus for defining a process according to any of the foregoing embodiments of the present application may be applied to the processor 402 or implemented by the processor 402.
The processor 402 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the methods described above may be performed by integrated logic circuitry in hardware or instructions in software in processor 402. The processor 402 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but may also be a digital signal processor (DIGITAL SIGNAL Processing, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 401 and the processor 402 reads the information in the memory 401 and in combination with its hardware performs the steps of the above method.
Corresponding to the above-mentioned slag robot control method, the embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer executable instructions, and the computer executable instructions cause a processor to operate the steps of the above-mentioned slag robot control method when the computer executable instructions are called and operated by the processor.
The slag dragging robot control device provided by the embodiment of the application can be specific hardware on equipment or software or firmware installed on the equipment and the like. The device provided by the embodiment of the present application has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity, the specific operation of the system, apparatus and unit described above may refer to the corresponding process in the above method embodiment, which is not described in detail herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
As another example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments provided in the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product stored in a storage medium, comprising instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the slag robot control method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, or an optical disk, etc., which can store program codes.
It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above examples are only specific embodiments of the present application, and are not intended to limit the scope of the present application, but it should be understood by those skilled in the art that the present application is not limited thereto, and that the present application is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit of the corresponding technical solutions. Are intended to be encompassed within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (9)
1. The control method of the slag dragging robot is characterized by comprising the following steps of:
Step 1: the slag dragging path strategy generator acquires environment information and generates an initial obstacle avoidance path between a path starting point and a path ending point; the generating the initial obstacle avoidance path includes:
acquiring a starting point and an ending point of the slag dragging robot;
establishing a circle taking a starting point and an ending point as diameters, and dividing the circle into two semicircles according to the diameters;
respectively calculating the density of the barriers in the two semicircles;
generating a node tree by taking a starting point as an original node: firstly, generating a random node in a semicircle with smaller obstacle concentration by a preset angle, finding a node closest to the random node in known nodes as an adjacent node, connecting the random node with the adjacent node to generate a smooth curve, taking the random node as a starting point on the smooth curve, and selecting a node with a designated length as a new node to be added into a node tree;
Generating the same node tree by taking the end point as the original node until the nodes of the two node trees which respectively take the start point and the end point as the original node are connected;
Step 2: the slag dragging path effect evaluator scores the initial obstacle avoidance path according to path length, coverage rate and smoothness evaluation indexes, and sets different weights for the indexes; wherein, the path length refers to the distance from the starting point to the ending point; the coverage rate refers to the number of nodes or positions through which a path passes; the smoothness refers to the continuity and stability of the path; the setting different weights for the indicators includes:
the path length weight is a, the coverage rate weight is b, and the smoothness weight is c;
The path length score calculated by the slag dragging path effect evaluator is m, the coverage rate score is n, and the smoothness score is p; s is a score;
scoring was performed according to the following formula:
；
step 3: if the score reaches a preset value, generating a target path; otherwise, repeating the step 1 until the score reaches a preset value;
step 4: dynamic window processing is used on the target path to generate a smooth and continuous motion trail.
2. The method of claim 1, wherein generating the initial obstacle avoidance path further comprises node compression:
respectively calculating the depths of two node trees generated by taking a starting point and an ending point as original nodes, and merging the tree with smaller depth into the tree with larger depth;
And/or the number of the groups of groups,
After the nodes of the two node trees with the starting point and the ending point as the original nodes are connected, the nodes are restored by taking the connected nodes of the two node trees as root nodes.
3. The slag robot control method of claim 1, wherein: the slag dragging path strategy generator obtains environmental information including:
acquiring image data of a target slag fishing area from different spectrums by using an RGB camera and an infrared thermal imaging camera, and constructing a two-dimensional grid map through an image processing algorithm; a sonar sensor is used for acquiring three-dimensional point cloud information, and a target slag-fishing area topography and an obstacle model are obtained;
Setting a voice interaction interface, wherein a user can set a starting point and an ending point of slag dragging, tolerance and regional parameter requirements through voice instructions, wherein the tolerance refers to the adaptability of a slag dragging robot to environmental changes; the regional parameter requirements refer to parameter limits which need to be met when the robot works.
4. The method of claim 1, wherein the generating an initial obstacle avoidance path further comprises the steps of:
(1) Planning a path at the tail end of a mechanical arm of the slag dragging robot, and forming a connecting path avoiding an obstacle between a starting point and a stopping point;
(2) Selecting a path point sequence on a connecting path, and calculating a mechanical arm joint variable sequence corresponding to the path point sequence at the tail end of the mechanical arm through inverse kinematics processing;
(3) Judging whether the variable sequence of the mechanical arm joint also meets the obstacle avoidance condition of the robot, if not, jumping to the step (1) and re-planning; if the condition is met, carrying out the next step;
(4) After the judgment of all the path point sequences is completed, starting from the starting point, each path point simultaneously meets the obstacle avoidance requirements of the tail end of the mechanical arm and the joints of the mechanical arm, and finally, an initial obstacle avoidance path from the starting point to the ending point is formed.
5. The method of claim 1, wherein the employing dynamic window processing on the target path comprises:
setting a window with odd length, obtaining coordinate values of all path points in the window, calculating an average value of the coordinate values, and replacing the coordinate value of the middle position of the window with the average value;
calculating the obstacle concentration of a coordinate point at the middle position of the window in real time, and translating the sliding window with a first preset step length when the obstacle concentration is large; when the density of the barriers is smaller, translating the sliding window with a second preset step length; the first preset step length is smaller than the second preset step length;
Until the slide covers all the path points.
6. The method of controlling a slag dragging robot according to claim 1, further comprising, after the step 4:
And (3) when the obstacle update or the termination point change, re-triggering the step (1) to generate a new initial obstacle avoidance path, and repeating the steps to re-plan the path.
7. A slag dragging robot control device, comprising:
slag dragging path strategy generating device: the slag dragging path strategy generator acquires environment information and generates an initial obstacle avoidance path between a path starting point and a path ending point; the generating the initial obstacle avoidance path includes:
acquiring a starting point and an ending point of the slag dragging robot;
establishing a circle taking a starting point and an ending point as diameters, and dividing the circle into two semicircles according to the diameters;
respectively calculating the density of the barriers in the two semicircles;
generating a node tree by taking a starting point as an original node: firstly, generating a random node in a semicircle with smaller obstacle concentration by a preset angle, finding a node closest to the random node in known nodes as an adjacent node, connecting the random node with the adjacent node to generate a smooth curve, taking the random node as a starting point on the smooth curve, and selecting a node with a designated length as a new node to be added into a node tree;
Generating the same node tree by taking the end point as the original node until the nodes of the two node trees which respectively take the start point and the end point as the original node are connected;
Slag dragging path effect evaluation device: the slag dragging path effect evaluator scores the initial obstacle avoidance path according to path length, coverage rate and smoothness evaluation indexes, and sets different weights for the indexes; wherein, the path length refers to the distance from the starting point to the ending point; the coverage rate refers to the number of nodes or positions through which a path passes; the smoothness refers to the continuity and stability of the path; the setting different weights for the indicators includes:
the path length weight is a, the coverage rate weight is b, and the smoothness weight is c;
The path length score calculated by the slag dragging path effect evaluator is m, the coverage rate score is n, and the smoothness score is p; s is a score;
scoring was performed according to the following formula:
；
The judging device: if the score reaches a preset value, generating a target path; otherwise, repeatedly setting a slag dragging path strategy generating device until the score reaches a preset value;
smoothing device: dynamic window processing is used on the target path to generate a smooth and continuous motion trail.
8. An electronic device, comprising: a processor; a memory storing a program, wherein the program comprises instructions that when executed by the processor cause the processor to perform the method of any of claims 1-6.
9. A non-transitory computer readable storage medium storing computer instructions, wherein the computer readable storage medium has instructions or a computer program stored therein, which when run on a device, cause the device to perform the method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410194636.8A CN117762149B (en) | 2024-02-22 | 2024-02-22 | Slag dragging robot control method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410194636.8A CN117762149B (en) | 2024-02-22 | 2024-02-22 | Slag dragging robot control method, device, equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117762149A CN117762149A (en) | 2024-03-26 |
| CN117762149B true CN117762149B (en) | 2024-05-17 |
Family
ID=90316778
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410194636.8A Active CN117762149B (en) | 2024-02-22 | 2024-02-22 | Slag dragging robot control method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117762149B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112462785A (en) * | 2020-12-04 | 2021-03-09 | 厦门大学 | Mobile robot path planning method and device and storage medium |
| CN112686429A (en) * | 2020-12-16 | 2021-04-20 | 天津城建大学 | Mobile robot and path planning method thereof based on adaptive genetic algorithm |
| CN113103236A (en) * | 2021-04-22 | 2021-07-13 | 山东大学 | Rapid and gradual optimal mechanical arm obstacle avoidance path planning method |
| CN113219998A (en) * | 2021-06-15 | 2021-08-06 | 合肥工业大学 | Improved bidirectional-RRT-based vehicle path planning method |
| CN113341984A (en) * | 2021-06-15 | 2021-09-03 | 桂林电子科技大学 | Robot path planning method and device based on improved RRT algorithm |
| CN115454061A (en) * | 2022-08-31 | 2022-12-09 | 安徽机电职业技术学院 | Robot path obstacle avoidance method and system based on 3D technology |
| CN115903827A (en) * | 2022-12-02 | 2023-04-04 | 华南农业大学 | Agricultural machinery parking path planning and tracking control method based on GNSS |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3779928B2 (en) * | 2002-01-22 | 2006-05-31 | 矢崎総業株式会社 | Power window pinching detector |
-
2024
- 2024-02-22 CN CN202410194636.8A patent/CN117762149B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112462785A (en) * | 2020-12-04 | 2021-03-09 | 厦门大学 | Mobile robot path planning method and device and storage medium |
| CN112686429A (en) * | 2020-12-16 | 2021-04-20 | 天津城建大学 | Mobile robot and path planning method thereof based on adaptive genetic algorithm |
| CN113103236A (en) * | 2021-04-22 | 2021-07-13 | 山东大学 | Rapid and gradual optimal mechanical arm obstacle avoidance path planning method |
| CN113219998A (en) * | 2021-06-15 | 2021-08-06 | 合肥工业大学 | Improved bidirectional-RRT-based vehicle path planning method |
| CN113341984A (en) * | 2021-06-15 | 2021-09-03 | 桂林电子科技大学 | Robot path planning method and device based on improved RRT algorithm |
| CN115454061A (en) * | 2022-08-31 | 2022-12-09 | 安徽机电职业技术学院 | Robot path obstacle avoidance method and system based on 3D technology |
| CN115903827A (en) * | 2022-12-02 | 2023-04-04 | 华南农业大学 | Agricultural machinery parking path planning and tracking control method based on GNSS |
Non-Patent Citations (2)
| Title |
|---|
| Research on Path Planning of Substation Inspection Robot Based on Improved A* Algorithm;Ping Luo;《2022 China Automation Congress》;20221231;全文 * |
| 面向观测任务的无人艇路径规划策略;孟祥根;《智能计算机与应用》;20200831;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117762149A (en) | 2024-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109709945B (en) | Path planning method and device based on obstacle classification and robot | |
| JP5860081B2 (en) | Robot simulation device that generates robot motion path | |
| CN110632921B (en) | Robot path planning method and device, electronic equipment and storage medium | |
| CN109509210B (en) | Obstacle tracking method and device | |
| CN110009718B (en) | Three-dimensional high-precision map generation method and device | |
| CN102541057B (en) | Moving robot obstacle avoiding method based on laser range finder | |
| CN113227927A (en) | Apparatus, method and object for facilitating motion planning in an environment with dynamic objects | |
| CN108459599B (en) | Motion path planning method and device | |
| CN113618742B (en) | Robot obstacle avoidance method and device and robot | |
| CN111201448B (en) | Method and device for generating an inverted sensor model and method for identifying obstacles | |
| CN109682336B (en) | Automatic planning and optimizing method for three-coordinate measurement path for vehicle body precision detection | |
| JP5902275B1 (en) | Autonomous mobile device | |
| CN107289938B (en) | Local path planning method for ground unmanned platform | |
| JP6907206B2 (en) | Exercise planning methods, exercise planning equipment and non-temporary computer-readable recording media | |
| CN115609594B (en) | Planning method and device for mechanical arm path, upper control end and storage medium | |
| WO2022095514A1 (en) | Image detection method and apparatus, electronic device, and storage medium | |
| Zhang et al. | Safe and efficient robot manipulation: Task-oriented environment modeling and object pose estimation | |
| CN117762149B (en) | Slag dragging robot control method, device, equipment and medium | |
| CN113375686A (en) | Path planning method and device and intelligent conveying system | |
| KR101475210B1 (en) | Appraratus for simulation of vitural robot | |
| Bobkov et al. | Vision-based navigation method for a local maneuvering of the autonomous underwater vehicle | |
| CN114200920A (en) | Path planning method, device and control system | |
| CN115981311A (en) | Path planning method and system for complex pipeline maintenance robot | |
| CN115016510A (en) | Robot navigation obstacle avoidance method and device and storage medium | |
| Zhu et al. | Online motion generation using accumulated swept volumes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Zhang Shuai Inventor after: Sun Wenchu Inventor after: Zhan Kai Inventor after: Liu Xinyu Inventor after: Gao Yang Inventor before: Zhang Shuai Inventor before: Sun Jianchu Inventor before: Zhan Kai Inventor before: Liu Xinshi Inventor before: Gao Yang |
|
| CB03 | Change of inventor or designer information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |