CN117001663A - Mechanical arm movement path planning method and system and storage medium - Google Patents
Mechanical arm movement path planning method and system and storage medium Download PDFInfo
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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
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Abstract
The application relates to the technical field of motion control, in particular to a method and a system for planning a motion path of a mechanical arm, a storage medium and electronic equipment. The method comprises the following steps: image acquisition is carried out on the mechanical arm and the working environment, and image data are obtained; performing three-dimensional reconstruction by using the acquired image data to acquire a corresponding three-dimensional model; the three-dimensional model comprises a mechanical arm, an obstacle and position information of an object to be sorted; calculating the target pose of the mechanical arm according to the position information of the target to be sorted; and planning a path of the mechanical arm based on the initial pose and the target pose of the mechanical arm so as to acquire a planned motion path. The method can be used for planning the movement path of the mechanical arm for sorting complex workpieces.
Description
Technical Field
The application relates to the technical field of motion control, in particular to a mechanical arm motion path planning method, a mechanical arm motion path planning system, a storage medium and electronic equipment.
Background
According to the international robot alliance published report in 2018, it is shown that over 300 ten thousand industrial robots have been produced worldwide, and China also shows that in China, 2025, the direction of the manufacturing industry is changed from traditional manpower to automation. And under the background that domestic human cost gradually increases, the mechanical arm can greatly improve production efficiency and reduce cost due to the stability, so that the mechanical arm plays an important role in industry, especially in the sorting process. However, with the increase in productivity, conventional sorting systems have failed to cope with a vast number of item sorting scenarios, embodied as: 1. the traditional mechanical arm is modeled by a D-H method, errors possibly exist in actual movement, and joint singular is easily caused; 2. the traditional mechanical arm motion path planning algorithm has the defects of low accuracy and timeliness, and the consideration of collision detection is insufficient; 3. most of the traditional sorting systems only have one mechanical arm for working, so that the working efficiency is limited; 4. the traditional sorting system has the defect of insufficient automation and requires additional labor cost.
Therefore, in order to improve the efficiency of the industrial sorting system and reduce the production cost of enterprises, it is necessary to design a mechanical arm movement path planning system and method for sorting complex workpieces.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure provides a mechanical arm movement path planning method, a mechanical arm movement path planning system, a storage medium and an electronic device, which can ensure path safety and improve search speed at the same time to acquire an optimal path.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a method for planning a movement path of a robot arm, the method comprising:
image acquisition is carried out on the mechanical arm and the working environment, and image data are obtained; performing three-dimensional reconstruction by using the acquired image data to acquire a corresponding three-dimensional model; the three-dimensional model comprises a mechanical arm, an obstacle and position information of an object to be sorted;
calculating the target pose of the mechanical arm according to the position information of the target to be sorted;
and planning a path of the mechanical arm based on the initial pose and the target pose of the mechanical arm so as to acquire a planned motion path.
According to a second aspect of the present disclosure, there is provided a robotic arm motion path planning system comprising:
the image processing module is used for carrying out image acquisition on the mechanical arm and the working environment to obtain image data; performing three-dimensional reconstruction by using the acquired image data to acquire a corresponding three-dimensional model; the three-dimensional model comprises a mechanical arm, an obstacle and position information of an object to be sorted;
the pose calculating module is used for calculating the target pose of the mechanical arm according to the position information of the target to be sorted;
and the path planning module is used for planning the path of the mechanical arm based on the initial pose and the target pose of the mechanical arm so as to acquire a planned motion path.
According to a third aspect of the present disclosure, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the robot arm movement path planning method as described in the above embodiments.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the robot motion path planning method according to the above embodiment via execution of the executable instructions.
According to the method provided by the embodiment of the disclosure, image data are acquired by carrying out image acquisition on the mechanical arm and the working environment; performing three-dimensional reconstruction by using the acquired image data to acquire a corresponding three-dimensional model; calculating the target pose of the mechanical arm according to the position information of the target to be sorted; planning a path of the mechanical arm based on the initial pose and the target pose of the mechanical arm so as to acquire a planned motion path; the method can be used for planning the movement path of the mechanical arm for sorting complex workpieces. The rigid motion state is described from the global coordinate system, and the defect that the D-H model is easy to generate singular values relative to local parameters of the coordinate system is overcome. The search speed is improved and the optimal path is obtained while the path safety is ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 schematically illustrates a schematic diagram of a robotic arm motion path planning method in an exemplary embodiment of the present disclosure;
a schematic diagram of an automated intelligent sorting system architecture in an exemplary embodiment of the present disclosure is schematically shown in fig. 2;
a zero-position robotic arm joint graph is schematically illustrated in fig. 3;
a method schematic of a motion planning procedure in an exemplary embodiment of the present disclosure is schematically illustrated in fig. 4;
a schematic diagram of control barrier function construction method logic in combination with linear quadratic programming in an exemplary embodiment of the present disclosure is schematically shown in fig. 5;
a schematic diagram of a distance function distribution in an exemplary embodiment of the present disclosure is schematically shown in fig. 6;
a schematic diagram of the components of a robotic arm motion path planning system in an exemplary embodiment of the present disclosure is schematically illustrated in fig. 7;
a schematic diagram of a storage medium in an exemplary embodiment of the present disclosure is schematically illustrated in fig. 8;
a schematic diagram of an electronic device in an exemplary embodiment of the present disclosure is schematically illustrated in fig. 9.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
In the related art, the traditional mechanical arm is mostly modeled by a D-H method, errors possibly exist in actual movement, and joint singular is easy to cause; the traditional mechanical arm motion path planning algorithm has the defects of low accuracy and timeliness, and the consideration of collision detection is insufficient; most of the traditional sorting systems only have one mechanical arm for working, so that the working efficiency is limited; the traditional sorting system has the defect of insufficient automation and requires additional labor cost.
In view of the shortcomings and drawbacks of the prior art, a method for planning a motion path of a mechanical arm for sorting complex workpieces is provided in the present exemplary embodiment. Referring to fig. 1, the method may include:
step S11, image acquisition is carried out on the mechanical arm and the working environment, and image data are obtained; performing three-dimensional reconstruction by using the acquired image data to acquire a corresponding three-dimensional model; the three-dimensional model comprises a mechanical arm, an obstacle and position information of an object to be sorted;
step S12, calculating the target pose of the mechanical arm according to the position information of the target to be sorted;
and S13, planning a path of the mechanical arm based on the initial pose and the target pose of the mechanical arm so as to acquire a planned motion path.
In this example embodiment, referring to fig. 2, a system for complex workpiece sorting is provided, including an infeed conveyor 221, the infeed conveyor 221 being configured to transfer workpieces 11 to be inspected to an inspection conveyor 223. Downstream of the infeed conveyor 221 is provided a detection conveyor 223, the detection conveyor 223 being adapted to transport the work pieces 11 to the detection zone 12. The detection zone 12 may be a sized zone on the detection conveyor 223. The robot arm a and the robot arm B are provided on both sides of the detection area 12. Detection devices a212 and B213 are provided on both sides of the detection region 12. A discharge conveyor 222 is provided downstream of the detection conveyor 223 for conveying the work pieces provided with good product marks. At one end of the inspection conveyor 223, e.g. the other side opposite to the infeed conveyor, a reject conveyor 224 may be provided for transporting the workpieces provided with reject marks. Above the detection area, a camera 211 may be provided for capturing an image of the workpiece. The mechanical arm 241 includes a connection/disconnection ratio a and a mechanical arm B, which are respectively disposed outside the detection area, and may be used for sorting the workpieces in the detection area. And the controller 242 is connected with the internal detection device and the mechanical arm and is used for sending control instructions to the image acquisition assembly, the internal detection device and the mechanical arm. And the upper computer 241 is connected with the controller 241 and the image acquisition component, and is used for receiving the image data uploaded by the image acquisition component and sending instruction information to the controller.
In this exemplary embodiment, in the above-mentioned step S11, referring to the system shown in fig. 2, a mobile industrial camera scans to collect images, point cloud data is generated, and the data is sent to an upper computer through ethernet; and establishing a real-time three-dimensional map including target and obstacle positions for the whole sorting system by a real-time positioning and map creation (SLAM) function in the upper computer.
In this example embodiment, in the step S12 described above, an exponential product method based on the spin theory may be designed to model the mechanical arm according to the mechanical arm model parameter, and calculate the target pose of the mechanical arm near the target.
Specifically, the calculating the target pose of the mechanical arm according to the position information of the target to be sorted includes:
and constructing a mechanical arm model, and calculating the target pose of the mechanical arm at the position information of the target to be sorted according to the mechanical arm model.
In this example embodiment, the constructing the mechanical arm model includes:
defining a working tail end and a screw shaft of the mechanical arm;
the spiral transformation matrix of each joint is determined according to the working tail end of the mechanical arm and the spiral shaft;
and defining a forward kinematics formula of the robot based on the spiral theory based on the spiral transformation matrix, and determining the position information of the working tail end of the mechanical arm according to the forward kinematics formula of the robot.
Specifically, the exponential product method based on the rotation theory refers to the mechanical arm joint coordinate diagram when the mechanical arm is in the zero position shown in fig. 3, including but not limited to the position, structure and form of the mechanical arm at the initial moment. For modeling the robotic arm to provide data support.
The working end of the mechanical arm is defined, and the motion model end which can be supported by the rotation theory is defined as follows:
the matrix form is:
when defining the screw axis, the UR5 robot arm has 6 rotation axes in total, and the screw axis direction is defined as:
screw axis S i ＝(ω i ，v i ) I=1, the coordinates are shown in table 1 below:
TABLE 1
Wherein the robot rotation follows the right hand rule.
Defining a spiral transformation matrix:
wherein,the index product coordinates of the ith joint of the UR5 mechanical arm.
Defining a forward kinematics formula of the UR5 robot based on the spiral theory:
and the T (theta) is the end coordinate of the UR5 mechanical arm, and the exponential product modeling of the mechanical arm based on the rotation theory is completed by calculating the T (theta).
In this example embodiment, in the step S13, the constructing the mechanical arm model includes:
defining a working tail end and a screw shaft of the mechanical arm;
the spiral transformation matrix of each joint is determined according to the working tail end of the mechanical arm and the spiral shaft of the mechanical arm;
and defining a forward kinematics formula of the robot based on the spiral theory based on the spiral transformation matrix, and determining the position information of the working tail end of the mechanical arm according to the forward kinematics formula of the robot.
In this example embodiment, path planning is performed on a robot arm, including:
defining an initial node and a target node, and initializing a tree structure;
setting an initial node and a target node as starting points of a tree structure respectively, and expanding the tree structure to a next node through local optimization; when the initial node and the target node intersect, determining the planned node information;
all feasible nodes are connected to obtain the path with the minimum cost as the planned motion path.
In this example embodiment, the expanding the tree structure to the next node through local optimization includes: track nodes are selected based on the paranoid sampling and rerouting loops.
In this example embodiment, the selecting a trace node based on a paranoid sampling and rerouting cycle includes:
sampling from uniform distribution in configuration space to obtain sampling nodes in each iteration process;
determining a neighboring node set of the sampling node, and determining the nearest neighboring node;
performing track planning based on the sampling node and the nearest node to acquire an end node;
judging whether the angle of the connecting line between the end node and the target node is within a preset range or not;
calculating the track cost of each adjacent node in the terminal node and the adjacent node set by utilizing the father node function so as to obtain a minimum cost track;
the minimum cost trajectory is updated with a rerouting function.
In this example embodiment, the method further includes:
and carrying out offset sampling on the planned node information to discard invalid sampling points.
Specifically, in order to improve the precision and the speed of the motion planning of the mechanical arm and further improve the sorting efficiency of the mechanical arm, the application provides an algorithm for quickly exploring a random tree by combining the improvement of a control barrier function of linear quadratic programming. The improved algorithm for quickly exploring a random tree in combination with the control barrier function of the linear quadratic programming is mainly used for planning a feasible path for sorting and grabbing the mechanical arm, and optionally, in some embodiments, the motion planning of the mechanical arm is allowed to be different, and the actual path is automatically generated according to the real-time change of the state and the surrounding environment of the mechanical arm. Specifically, as shown in fig. 4, the following steps may be included:
s4-1, for initial state x init And a target state x goal Initializing, and initializing a treeWherein->Is a graph, epsilon is an edge. Respectively let x init 、x goal Set as the starting point of the tree, recorded as +.>
S4-2, by local optimizationExpanding towards x, creating x new . Wherein x is new Indicating the next state.
S4-3, before meeting the conditions, performing the following cycle:
s4-3-1, randomly sampling. During each iteration, x is sampled from an even distribution over the configuration space samp . Wherein x is samp Representing the sampling state.
S4-3-2、Adjacent nodes are selected. Will x samp As input to the nearbyode, find a set of nodes in its vicinityWherein, nearbynode represents adjacent node function, record all adjacent nodes, and +.>Representing a set of neighboring nodes.
S4-3-3, selecting the nearest neighbor node. From the slaveIs selected to be a nearest->As x nearest . Wherein x is nearest Representing the state of the nearest neighbor node.
S4 - 3-4, selecting a track. Will x samp X is a group nearest Sigma is obtained as an input to the LQR-CBF-steer. Wherein LQR represents linear quadratic programming, CBF represents control obstacle function, sampling in the reachable set and kinematic construction of the mechanical arm are used for collision detection. Sigma represents the trajectory, and the end node of sigma is x new 。
S4-3-5, judging the new node. For a pair ofGenerated x new Judging, x new The angle of the line with the target point should be controlled within a certain range r, otherwise execution is continued (S4-3-4).
S4-3-6, selecting a father node. Receiving a computed x using chooseParend new And is provided withTo calculate the slave x new To->The state trajectories and corresponding costs of all the nearby nodes defined in (a) obtain σ min Wherein ChooseParenct represents selecting a parent node function, σ min Representing a minimum cost trajectory with an end point x min Representing a minimum cost node state.
S4-3-7, rewiring. Detection of nearby each belonging by LQR-CBF-steerX can be optimized min Using Rewire, return xmin is re-linked to x new And the corresponding costs are updated. Where Rewire represents a rewiring function.
S4-4, judging whether path planning is completed or not. When (when)When crossing, it indicates complete planning, return +.>
S4-5, judging the optimal planning. If the path is not the optimal path, all feasible x are connected, and one path with the minimum cost is selected as the optimal path.
S4-6, offset sampling is carried out, part of invalid sampling points are discarded, and the subsequent calculation speed is improved. And then completing the motion planning of the mechanical arm.
In this example embodiment, the method further includes: and constructing a control barrier function, and carrying out collision detection on the planned motion path by combining the position information of the obstacle.
In this example embodiment, the constructing a control barrier function includes:
constructing a continuous time dynamic control system;
constructing a linear quadratic programming device based on a continuous time dynamic control system;
the control barrier function is constructed in conjunction with a linear quadratic planner.
Specifically, in order to avoid collision during operation of the double mechanical arms and improve safety of the mechanical arms and enable the mechanical arms to perform collision detection, the application provides a method for controlling a barrier function by combining linear quadratic programming. The method for controlling the barrier function by combining the linear quadratic programming is mainly used for judging whether the mechanical arm can strike an obstacle in the motion programming process, and provides a collision detection function for a feasible path. Optionally, in some embodiments, collision detection of the robot motion plan allows for different, specific obstacle settings to be changed in real time according to the robot and the surrounding environment. The step of constructing a control barrier function may comprise:
s5-1, constructing a continuous time dynamic control system:
where x represents a state, f (x), g (x) represents a function about the state, u represents a control input,representing the differentiation of the state.
S5-2, constructing a linear quadratic programming device, which comprises the following specific steps:
s5-2-1, defining a cost function of an infinite time range:
where Q and R are weighting functions of x and u, respectively, and J is a cost function over an infinite time range.
S5-2-2, linearizing:
for a nonlinear system, its linearized form can be obtained by taylor expansion of the vicinity of the local state and controlled to:
wherein A is a state matrix of the system, and B is a control input matrix of the system.
S5-2 - 3. Calculating a gain matrix K LQR . By a given systemCost functionComputing gain matrix K for LQR using algebraic Riccati equation LQR Wherein, the cost matrix P is:
A T P+PA-PBR-B T ＝0#(16)
K LQR ＝R-B T P#(17)
s5-2-4, defining an optimal control strategy. Optimal control strategy pi * (x) Is a sum gain matrix K LQR Negative correlation, defined as:
π * (x)＝-K LQR x#(18)
s5-2-5, performing first-order Taylor expansion on the nonlinear system:
wherein F (x, u) is a nonlinear system, x eq 、u eq The balance points of the state and control inputs, respectively.
Based on the balance point, the linearization system can be rewritten as:
wherein,differential representing linearized state, +.>And->Representing the linearized state and control inputs respectively,the state matrix and the control input matrix are respectively corresponding to the linearized state matrix and the control input matrix.
S5-3, defining a control barrier function. The control obstacle function is mainly obtained through an unsafe set and an accessible space set determined by the distance function, and referring specifically to fig. 5, fig. 5 shows a schematic diagram of the distance function and the accessible space, and the construction of the control obstacle function is understood in connection with this specific embodiment.
S5-3-1, defining an unsafe set. Referring to fig. 6, the unsafe set is defined as a distance function h (x) between the robot arm and the obstacle, where h (x) =0 represents a safe reachable boundary, h (x) < 0 represents unsafe, and h (x) > 0 represents safe.
S5-3-2, calculating a reachable set. The motion of the robotic arm at each discrete time segment is limited, and the legend measure of the reachable set of the robotic arm is bounded, defined as c (x).
S5-3-3, solving the square sum programming problem. And combining the kinematics of the mechanical arm, solving the square and planning problems, and constructing a control barrier function.
The method provided by the disclosure can be applied to planning the movement path of the mechanical arm for sorting complex workpieces. Compared with the traditional D-H modeling, the method adopts exponential product modeling based on the spin theory, and only needs 2 coordinate systems, so that the defect of lack of completeness of the D-H model is avoided; the rigid motion state is described from the global coordinate system, and the defect that the D-H model is easy to generate singular values relative to the local parameters of the coordinate system is overcome; and has more geometric meaning, and is convenient to select when solving the inversion. Compared with the traditional searching algorithm, the method for planning the mechanical arm motion path of the control barrier function-combined improved rapid exploration random tree is provided, searching is conducted in two directions from an initial point and a target point, offset sampling and rerouting are conducted in real time, and the searching speed is improved and the optimal path is obtained while the path safety is ensured.
In an exemplary embodiment of the present disclosure, reference is made to fig. 7. There is also provided a robotic arm motion path planning system 70 comprising:
the image processing module 701 is configured to perform image acquisition on the mechanical arm and the working environment, and acquire image data; performing three-dimensional reconstruction by using the acquired image data to acquire a corresponding three-dimensional model; the three-dimensional model comprises a mechanical arm, an obstacle and position information of an object to be sorted;
the pose calculating module 702 is configured to calculate a target pose of the mechanical arm according to position information of a target to be sorted;
the path planning module 703 is configured to perform path planning on the robotic arm based on the initial pose and the target pose of the robotic arm, so as to obtain a planned motion path.
Because each functional module of the mechanical arm movement path planning system in the embodiment of the present application is the same as that in the embodiment of the mechanical arm movement path planning method, the description thereof is omitted herein.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
In an exemplary embodiment of the present disclosure, referring to fig. 8, there is also provided a computer-readable storage medium 30 having stored thereon a program product capable of implementing the method described above in the present specification. In some possible embodiments, the various aspects of the application may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the application as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.
A program product for implementing the above-described method according to an embodiment of the present application may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
As shown in fig. 9, the electronic device 800 is embodied in the form of a general purpose computing device. Components of electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one storage unit 820, a bus 830 connecting the different system components (including the storage unit 820 and the processing unit 810), and a display unit 840.
Wherein the storage unit stores program code that is executable by the processing unit 810 such that the processing unit 810 performs steps according to various exemplary embodiments of the present application described in the above section of the "exemplary method" of the present specification.
The storage unit 820 may include volatile storage units such as a Random Access Memory (RAM) 8201 and/or a cache memory 8202, and may further include a Read Only Memory (ROM) 8203.
Storage unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 830 may include a data bus, an address bus, and a control bus.
The electronic device 800 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.) via an input/output (I/O) interface 850. The electronic device 800 further comprises a display unit 840 connected to an input/output (I/O) interface 850 for displaying. Also, electronic device 800 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 over bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 800, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
Furthermore, the above-described drawings are only schematic illustrations of processes included in the method according to the exemplary embodiment of the present application, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for planning a motion path of a mechanical arm, the method comprising:
image acquisition is carried out on the mechanical arm and the working environment, and image data are obtained; performing three-dimensional reconstruction by using the acquired image data to acquire a corresponding three-dimensional model; the three-dimensional model comprises a mechanical arm, an obstacle and position information of an object to be sorted;
calculating the target pose of the mechanical arm according to the position information of the target to be sorted;
and planning a path of the mechanical arm based on the initial pose and the target pose of the mechanical arm so as to acquire a planned motion path.
2. The method according to claim 1, wherein the method further comprises:
and constructing a control barrier function, and carrying out collision detection on the planned motion path by combining the position information of the obstacle.
3. The method of claim 1, wherein calculating the target pose of the robotic arm based on the position information of the target to be sorted comprises:
and constructing a mechanical arm model, and calculating the target pose of the mechanical arm at the position information of the target to be sorted according to the mechanical arm model.
4. A method according to claim 3, wherein said constructing a manipulator model comprises:
defining a working tail end and a screw shaft of the mechanical arm;
the spiral transformation matrix of each joint is determined according to the working tail end of the mechanical arm and the spiral shaft of the mechanical arm;
and defining a forward kinematics formula of the robot based on the spiral theory based on the spiral transformation matrix, and determining the position information of the working tail end of the mechanical arm according to the forward kinematics formula of the robot.
5. The method of claim 1, wherein routing the robotic arm comprises:
defining an initial node and a target node, and initializing a tree structure;
setting an initial node and a target node as starting points of a tree structure respectively, and expanding the tree structure to a next node through local optimization; when the initial node and the target node intersect, determining the planned node information;
all feasible nodes are connected to obtain the path with the minimum cost as the planned motion path.
6. The method of claim 5, wherein expanding the tree structure to a next node by local optimization comprises: selecting track nodes based on paranoid sampling and rewiring circulation;
wherein, based on the paranoid sampling, rerouting cycle select trajectory node, include:
sampling from uniform distribution in configuration space to obtain sampling nodes in each iteration process;
determining a neighboring node set of the sampling node, and determining the nearest neighboring node;
performing track planning based on the sampling node and the nearest node to acquire an end node;
judging whether the angle of the connecting line between the end node and the target node is within a preset range or not;
calculating the track cost of each adjacent node in the terminal node and the adjacent node set by utilizing the father node function so as to obtain a minimum cost track;
the minimum cost trajectory is updated with a rerouting function.
7. The method of claim 5, wherein the method further comprises:
and carrying out offset sampling on the planned node information to discard invalid sampling points.
8. The method of claim 2, wherein the constructing a control barrier function comprises:
constructing a continuous time dynamic control system;
constructing a linear quadratic programming device based on a continuous time dynamic control system;
the control barrier function is constructed in conjunction with a linear quadratic planner.
9. A robotic arm motion path planning system, the system comprising:
the image processing module is used for carrying out image acquisition on the mechanical arm and the working environment to obtain image data; performing three-dimensional reconstruction by using the acquired image data to acquire a corresponding three-dimensional model; the three-dimensional model comprises a mechanical arm, an obstacle and position information of an object to be sorted;
the pose calculating module is used for calculating the target pose of the mechanical arm according to the position information of the target to be sorted;
and the path planning module is used for planning the path of the mechanical arm based on the initial pose and the target pose of the mechanical arm so as to acquire a planned motion path.
10. A storage medium having stored thereon a computer program which when executed by a processor implements the robot arm movement path planning method according to any one of claims 1 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118177965A (en) * | 2024-02-20 | 2024-06-14 | 武汉长江激光科技有限公司 | Track planning method of osteotomy robot |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118177965A (en) * | 2024-02-20 | 2024-06-14 | 武汉长江激光科技有限公司 | Track planning method of osteotomy robot |
| CN118177965B (en) * | 2024-02-20 | 2024-10-08 | 武汉长江激光科技有限公司 | Track planning method of osteotomy robot |
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