CN118528260A - Control method for grabbing power cabinet - Google Patents
Control method for grabbing power cabinet Download PDFInfo
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- CN118528260A CN118528260A CN202410696434.3A CN202410696434A CN118528260A CN 118528260 A CN118528260 A CN 118528260A CN 202410696434 A CN202410696434 A CN 202410696434A CN 118528260 A CN118528260 A CN 118528260A
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- 230000007613 environmental effect Effects 0.000 claims abstract description 7
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- 239000012636 effector Substances 0.000 claims description 10
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Classifications
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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
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1669—Programme controls characterised by programming, planning systems for manipulators characterised by special application, e.g. multi-arm co-operation, assembly, grasping
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- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Manipulator (AREA)
Abstract
The invention relates to the control field of construction equipment of a server machine room, in particular to a control method for grabbing an electric power cabinet, which comprises the steps that a first robot obtains environmental information of the machine room through a camera and a radar, the environmental information comprises the position, the size, the shape and an obstacle of the machine room, a second robot plans a path for moving to the grabbing machine room according to the environmental information and avoids the obstacle, a third robot runs to a service area on the periphery of the machine room through the path, the robot can sense the environment in real time when executing the task, adjust the path and the gesture according to the environment information, reduce the occurrence of unexpected events and ensure the safety of workplaces.
Description
Technical Field
The invention relates to the field of control of construction equipment of a server machine room, in particular to a control method for grabbing an electric power cabinet.
Background
There are many electric power cabinets in the computer lab that is equipped with the server, and electric power cabinet is used for installing the server, and what the transport of current known electric power cabinet adopted is fork truck with electric power cabinet scooping up, lifts up again, sends electric power cabinet to corresponding position on, puts down the back, on its reserved position of manual regulation again, this kind of mode inefficiency moreover causes the electric power cabinet to damage easily, needs the cooperation of many people to accomplish moreover.
In order to solve the problems, the electric cabinet can be lifted by the transfer robot and then sent to the position, but no accurate control method and a way for self-adjusting the planned path according to the working condition of the site exist at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a control method for grabbing an electric power cabinet, which can accurately position and plan a path and improve the carrying efficiency and the placement accuracy.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a control method for grabbing an electric power cabinet comprises the following steps:
S1, environment sensing, wherein the robot acquires environment information of a machine room through a camera and a radar, and the environment information comprises the position, the size, the shape and the obstacle of a cabinet.
S2, planning a path, namely planning a path for displacement to the grabbing cabinet by the robot according to the environmental information, and avoiding obstacles.
S3, grabbing, wherein the robot runs to a service area on the periphery of the cabinet through a path, and the mechanical arm is controlled to plan the path motion of grabbing the cabinet and perform grabbing operation.
S4, after the machine cabinet is grabbed and lifted by the robot, the machine cabinet is sent to a designated position through a planned path, and the task is completed.
The invention is further provided with: in the step S1, the robot collects field data through a camera and a radar, extracts the data through a central control center, builds a three-dimensional model or map of the environment through SLAM algorithm, so that the robot can better understand the surrounding environment,
The SLAM algorithm specific mathematical model is as follows:
Here, the Is the pose of the robot and is characterized in that,Is a map of the road,Is a historical observation of the robot and,Is a history control of the robot and,
The goal is to estimate the trajectory of the robotSum mapI.e. estimationOptimizing using a recursive Bayesian filter to obtain a state transition modelWhich in particular shows that the robot is performing controlAfter that, fromTo the point ofIs used to determine the probability of a state transition,
Modeling by a motion model, targeting according to the motion modelPredicting pose changes of a front robotThe concrete model is as follows:
Wherein, Is the distance that the robot is moved,AndIs the rotation angle of the robot and is used for the control of the robot,Is the current direction of the robot and is the current direction of the robot,AndIs the coordinate location.
The invention is further provided with: by mappingDivided into oneEach of which may be a space or a two-dimensional array of obstaclesTo represent a map of a machine room, whereinRepresent the firstLine 1The grid of columns is passable,Representing the position of the robot in the machine roomCan be expressed as a two-dimensional coordinate WhereinAndIs the abscissa of the robot on the map, using Manhattan distance as a heuristic function, i.eWhereinAndIs the coordinates of the location of the target,
The states to be investigated are stored by using an open list, the states which have been investigated are stored by using a closed list, the next state to be investigated is selected according to the value of the heuristic function in the searching process, and the states are realized by dynamically updating the neighbor nodes in the searching process, so that the robot cannot enter the obstacle area.
The invention is further provided with: the path planning steps are as follows:
initializing: will start position Add to the open list and heuristic value thereofSetting to an initial value, setting a target position；
And (3) circulation: selecting heuristic values from an open listMinimum stateIf the state isIs the target stateThe algorithm ends and the path is found;
Otherwise, the state is taken Move from open list to closed list and review and stateAdjacent states, for each adjacent stateCalculate its heuristic value；
If it isAdding the heuristic value of the heuristic value into the open list or the closed list when the heuristic value is not in the open list or the closed list;
If it is Checking whether the new heuristic is better than the old heuristic already in the open list, if so, updating its heuristic;
If the open list is empty and no target state is found, the algorithm fails, indicating that no path is feasible.
The invention is further provided with: in the step3, the mechanical arm planning and grabbing machine cabinet model is as follows:
expressed as: by inverse kinematics solution algorithm To target positionJoint angle mapped to robotic armWherein the joint angle of the mechanical arm is expressed asWhereinIs the number of joints of the mechanical arm, and the target position is expressed asThis is the coordinates of the rack grabbing position,
Direction vector expressed as end effectorThe required and target direction vectorIn parallel with each other,The target direction vector is the direction vector of the end effectorWhereinIs the current position of the robot and,Direction vector expressed as end effectorThe required and target direction vectorParallel.
The invention is further provided with: in the step 4, the gesture stability control model after the machine cabinet is grabbed and lifted by the robot is as follows:
Wherein, Is posture adjustment control output,Is the current posture,Is the target pose, feedback is real-time Feedback information for adjusting the control parameters, adaptiveDynamicAttitudeAdjustment is denoted as the control function.
The invention is further provided with: the specific steps of the control function are as follows:
acquiring a current gesture and a target gesture, and calculating a gesture error;
According to Feedback real-time information, adjusting control parameters or output to adapt to the change of system parameters and the influence of external environment, and synchronously sensing and outputting signals by an MEMS sensor through acquisition information of the Feedback real-time information;
And calculating attitude adjustment control output through a PID controller according to the adjusted control parameters, and returning the calculated attitude adjustment control output for the robot to execute attitude adjustment.
Compared with the defects in the prior art, the invention has the beneficial effects that:
The robot can sense the environment in real time when executing tasks, and adjust the path and the gesture according to the environment information, so that the occurrence of unexpected events is reduced, and the safety of workplaces is ensured.
The three-dimensional model or map of the environment is built by SLAM algorithm, the robot can better understand the surrounding environment, intelligent path planning and dynamic adjustment are realized, and the robot is suitable for different working scenes and environment changes.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
An embodiment of the present invention will be further described with reference to fig. 1.
A control method for grabbing an electric power cabinet comprises the following steps:
the first step of environment sensing, the robot obtains the environment information of the machine room through the camera and the radar, and the environment information comprises the position, the size, the shape and the obstacle of the cabinet.
The robot collects field data through the camera and the radar, extracts the data through the central control center, constructs a three-dimensional model or map of the environment through SLAM algorithm, so that the robot can better understand the surrounding environment,
The SLAM algorithm specific mathematical model is as follows:
Here, the Is the pose of the robot and is characterized in that,Is a map of the road,Is a historical observation of the robot and,Is a history control of the robot and,
The goal is to estimate the trajectory of the robotSum mapI.e. estimationOptimizing using a recursive Bayesian filter to obtain a state transition modelWhich in particular shows that the robot is performing controlAfter that, fromTo the point ofIs used to determine the probability of a state transition,
Modeling by a motion model, targeting according to the motion modelPredicting pose changes of a front robotThe concrete model is as follows:
Wherein, Is the distance that the robot is moved,AndIs the rotation angle of the robot and is used for the control of the robot,Is the current direction of the robot and is the current direction of the robot,AndIs the coordinate location.
By mappingDivided into oneEach of which may be a space or a two-dimensional array of obstaclesTo represent a map of a machine room, whereinRepresent the firstLine 1The grid of columns is passable,Representing the position of the robot in the machine roomCan be expressed as a two-dimensional coordinate WhereinAndIs the abscissa of the robot on the map, using Manhattan distance as a heuristic function, i.eWhereinAndIs the coordinates of the location of the target,
The states to be investigated are stored by using an open list, the states which have been investigated are stored by using a closed list, the next state to be investigated is selected according to the value of the heuristic function in the searching process, and the states are realized by dynamically updating the neighbor nodes in the searching process, so that the robot cannot enter the obstacle area.
Through perception and map construction, the robot can accurately understand the surrounding environment, so that more reliable path planning and navigation can be performed, obstacles can be avoided, and a target position can be reached quickly.
The state estimation is performed by adopting a recursive Bayesian filter, and the historical observation and control information of the robot are combined, so that the track of the robot and the map of the surrounding environment can be estimated more accurately.
The SLAM algorithm can estimate the track and the map of the robot in an unknown environment at the same time, and is suitable for various complex, dynamic or uncertain environments, such as indoor machine rooms, warehouses and other scenes.
The closed loop detection can find and correct errors possibly generated in the movement of the robot, so that the consistency and the precision of the map are optimized, and the quality and the usability of the map are improved.
And planning a path from the robot to the grabbing cabinet according to the environmental information, and avoiding the obstacle.
The path planning adopts an A algorithm, and the most promising path is selected preferentially by considering heuristic values of each state, so that obstacles and non-passable areas can be effectively avoided, and the robot can safely pass through the environment when planning the path.
The path planning steps are as follows:
initializing: will start position Add to the open list and heuristic value thereofSetting to an initial value, setting a target position；
And (3) circulation: selecting heuristic values from an open listMinimum stateIf the state isIs the target stateThe algorithm ends and the path is found; the entire path may be reconstructed by tracing back the parent node of each state, resulting in an optimal path from the starting location to the target location.
Otherwise, the state is takenMove from open list to closed list and review and stateAdjacent states, for each adjacent stateCalculate its heuristic value；
If it isAdding the heuristic value of the heuristic value into the open list or the closed list when the heuristic value is not in the open list or the closed list;
If it is Checking whether the new heuristic is better than the old heuristic already in the open list, if so, updating its heuristic; common heuristic functions include Manhattan distance, euclidean distance, chebyshev distance.
If the open list is empty and no target state is found, the algorithm fails, indicating that no path is feasible.
And thirdly, grabbing, namely, the robot runs to a service area on the periphery of the cabinet through a path, and controls the mechanical arm to plan and grab the path movement of the cabinet, and grabbing operation is performed.
The mechanical arm planning grabbing machine cabinet model is as follows:
expressed as: by inverse kinematics solution algorithm To target positionJoint angle mapped to robotic armWherein the joint angle of the mechanical arm is expressed asWhereinIs the number of joints of the mechanical arm, and the target position is expressed asThis is the coordinates of the rack grabbing position,
Direction vector expressed as end effectorThe required and target direction vectorIn parallel with each other,The target direction vector is the direction vector of the end effectorWhereinIs the current position of the robot and,Direction vector expressed as end effectorThe required and target direction vectorParallel.
The inverse kinematics solution algorithm can accurately calculate the angles of all joints of the mechanical arm, so that the end effector can accurately grasp the target position, the accuracy and stability of grasping operation are improved, and as the inverse kinematics solution algorithm considers the kinematics characteristics of the mechanical arm, the gesture and the motion path of the mechanical arm can be flexibly adjusted according to actual conditions, the mechanical arm can adapt to cabinets with different shapes, sizes and positions, the joint angles of the mechanical arm can be rapidly calculated, and accordingly the mechanical arm can rapidly respond and execute grasping actions, and the operation efficiency of the robot is improved.
And fourthly, after the machine cabinet is grabbed and lifted by the robot, the machine cabinet is sent to a designated position through a planned path, and the task is completed.
The gesture stability control model after the machine cabinet is grabbed and lifted by the robot is as follows:
Wherein, Is posture adjustment control output,Is the current posture,The target gesture and feed back are real-time Feedback information, and are used for adjusting control parameters, adaptiveDynamicAttitudeAdjustment is represented as a control function, and the specific steps of the control function are as follows:
acquiring a current gesture and a target gesture, and calculating a gesture error;
According to Feedback real-time information, adjusting control parameters or output to adapt to the change of system parameters and the influence of external environment, and synchronously sensing and outputting signals by an MEMS sensor through acquisition information of the Feedback real-time information;
And calculating attitude adjustment control output through a PID controller according to the adjusted control parameters, and returning the calculated attitude adjustment control output for the robot to execute attitude adjustment.
Through gesture stable control, the robot can keep stable gesture to effectively reduce the robot and rock and shake in handling, can reduce the robot because of unstable transport interruption or mistake that leads to of gesture, help reducing the robot to the collision or the damage of article in handling, thereby effectively protect the security and the integrality of transport electric power cabinet, real-time feedback information and self-adaptation control algorithm, gesture stable control model can carry out dynamic adjustment according to different operating condition and environmental change, has improved the adaptability and the stability of system.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the invention, but one skilled in the art can make common changes and substitutions within the scope of the technical solution of the present invention.
Claims (7)
1. The utility model provides a control method that electric power cabinet snatched which characterized in that: the method comprises the following steps:
S1, environment sensing, wherein a robot acquires environment information of a machine room through a camera and a radar, wherein the environment information comprises the position, the size, the shape and barriers of a cabinet;
S2, planning a path, namely planning a path for displacement to a grabbing cabinet by the robot according to environmental information, and avoiding obstacles;
s3, grabbing, wherein the robot runs to a service area on the periphery of the cabinet through a path, and the mechanical arm is controlled to plan the path motion of grabbing the cabinet and perform grabbing operation;
S4, after the machine cabinet is grabbed and lifted by the robot, the machine cabinet is sent to a designated position through a planned path, and the task is completed.
2. The control method for grabbing an electric power cabinet according to claim 1, wherein the control method comprises the following steps: in the step S1, the robot collects field data through a camera and a radar, extracts the data through a central control center, builds a three-dimensional model or map of the environment through SLAM algorithm, so that the robot can better understand the surrounding environment,
The SLAM algorithm specific mathematical model is as follows:
Here, the Is the pose of the robot and is characterized in that,Is a map of the road,Is a historical observation of the robot and,Is a history control of the robot and,
The goal is to estimate the trajectory of the robotSum mapI.e. estimationOptimizing using a recursive Bayesian filter to obtain a state transition modelWhich in particular shows that the robot is performing controlAfter that, fromTo the point ofIs used to determine the probability of a state transition,
Modeling by a motion model, targeting according to the motion modelPredicting pose changes of a front robotThe concrete model is as follows:
Wherein, Is the distance that the robot is moved,AndIs the rotation angle of the robot and is used for the control of the robot,Is the current direction of the robot and is the current direction of the robot,AndIs the coordinate location.
3. The control method for grabbing an electric power cabinet according to claim 2, wherein: by mappingDivided into oneEach of which may be a space or a two-dimensional array of obstaclesTo represent a map of a machine room, whereinRepresent the firstLine 1The grid of columns is passable,Representing the position of the robot in the machine roomCan be expressed as a two-dimensional coordinate WhereinAndIs the abscissa of the robot on the map, using Manhattan distance as a heuristic function, i.eWhereinAndIs the coordinates of the location of the target,
The states to be investigated are stored by using an open list, the states which have been investigated are stored by using a closed list, the next state to be investigated is selected according to the value of the heuristic function in the searching process, and the states are realized by dynamically updating the neighbor nodes in the searching process, so that the robot cannot enter the obstacle area.
4. A control method for capturing an electric power cabinet according to claim 3, wherein: the path planning steps are as follows:
initializing: will start position Add to the open list and heuristic value thereofSetting to an initial value, setting a target position；
And (3) circulation: selecting heuristic values from an open listMinimum stateIf the state isIs the target stateThe algorithm ends and the path is found;
Otherwise, the state is taken Move from open list to closed list and review and stateAdjacent states, for each adjacent stateCalculate its heuristic value；
If it isAdding the heuristic value of the heuristic value into the open list or the closed list when the heuristic value is not in the open list or the closed list;
If it is Checking whether the new heuristic is better than the old heuristic already in the open list, if so, updating its heuristic;
If the open list is empty and no target state is found, the algorithm fails, indicating that no path is feasible.
5. A control method for capturing an electric power cabinet according to claim 3, wherein:
In the step 3, the mechanical arm planning and grabbing machine cabinet model is as follows:
expressed as: by inverse kinematics solution algorithm To target positionJoint angle mapped to robotic armWherein the joint angle of the mechanical arm is expressed asWhereinIs the number of joints of the mechanical arm, and the target position is expressed asThis is the coordinates of the rack grabbing position,
Direction vector expressed as end effectorThe required and target direction vectorIn parallel with each other,The target direction vector is the direction vector of the end effectorWhereinIs the current position of the robot and,Direction vector expressed as end effectorThe required and target direction vectorParallel.
6. A control method for capturing an electric power cabinet according to claim 3, wherein:
In the step 4, the gesture stability control model after the machine cabinet is grabbed and lifted by the robot is as follows:
Wherein, Is posture adjustment control output,Is the current posture,Is the target pose, feedback is real-time Feedback information for adjusting the control parameters, adaptiveDynamicAttitudeAdjustment is denoted as the control function.
7. The control method for grabbing an electric power cabinet according to claim 6, wherein:
The specific steps of the control function are as follows:
acquiring a current gesture and a target gesture, and calculating a gesture error;
According to Feedback real-time information, adjusting control parameters or output to adapt to the change of system parameters and the influence of external environment, and synchronously sensing and outputting signals by an MEMS sensor through acquisition information of the Feedback real-time information;
And calculating attitude adjustment control output through a PID controller according to the adjusted control parameters, and returning the calculated attitude adjustment control output for the robot to execute attitude adjustment.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118700164A (en) * | 2024-08-29 | 2024-09-27 | 深圳市欣茂鑫实业有限公司 | Control method and system for clamping mechanical arm |
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- 2024-05-31 CN CN202410696434.3A patent/CN118528260A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118700164A (en) * | 2024-08-29 | 2024-09-27 | 深圳市欣茂鑫实业有限公司 | Control method and system for clamping mechanical arm |
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