A distributed multi-redundant robotic arm target tracking method, device and storage medium

Abstract

The present invention discloses a distributed multi-redundant mechanical arm target tracking method, device and storage medium, the method comprising step 1: using a fully distributed multi-winner-takes-all network to generate decision variables, determining the first k redundant mechanical arms with the shortest distance to the target to perform the target tracking task; k is a positive integer; step 2: the redundant mechanical arm calculates the position of the end of the mechanical arm through the decision variables in step 1 and the redundant mechanical arm control rate; step 3: updating the position of the end of the redundant mechanical arm according to the position of the end of the mechanical arm calculated in step 2, and then executing step 1 until the target is tracked. The method of the present invention not only simplifies the control process, improves the robustness and scalability of the system, but also reduces the maintenance cost and improves the overall efficiency. Its excellent performance and wide application potential make it have a good industrialization prospect.

Description

Technical Field

[0001] The present invention relates to the field of redundant robotic arm control, and in particular to a distributed multi-redundant robotic arm target tracking method, device and storage medium. Background Art

[0002] In various industrial production and logistics transportation scenarios, redundant robotic arms are often needed to track and capture parts or items. Therefore, target tracking control of redundant robotic arms is extremely critical. It requires the use of precise sensors and algorithms to achieve high-precision control. This control not only improves production efficiency and transportation efficiency, but also reduces labor costs and reduces the risk of human error. Through the use of redundant robotic arms, we can optimize work processes, improve logistics processing capabilities, and better adapt to different environments and task requirements, injecting new vitality into the fields of industrial production and logistics transportation.

[0003] At present, the target tracking control methods for multiple redundant robotic arms are mainly centralized, because they all need to collect information from all redundant robotic arms. This method has poor single-point robustness and scalability, and collecting information from all redundant robotic arms is not conducive to the protection of privacy data. Summary of the invention

[0004] In order to solve at least one technical problem existing in the background technology, the present invention provides a distributed multi-redundant robot arm target tracking method, device and storage medium.

[0005] To achieve the above object, the technical solution of the present invention is:

[0006] In a first aspect, the present invention provides a distributed multi-redundant robotic arm target tracking method, comprising:

[0007] Step 1: Use a fully distributed multi-winner-takes-all network to generate decision variables and determine the first k redundant manipulators with the shortest distance to the target to perform the target tracking task; k is a positive integer;

[0008] Step 2: The redundant robot arm calculates the position of the end of the robot arm by using the decision variables in step 1 and combining the redundant robot arm control rate;

[0009] Step 3: Update the redundant robotic arm end position according to the robotic arm end position calculated in step 2, and then execute step 1 until the target is tracked.

[0010] In a second aspect, the present invention provides a distributed multi-redundant robotic arm target tracking device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described above when executing the computer program.

[0011] In a third aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein the computer program implements the steps of the method described above when executed by a processor.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] The distributed multi-redundant robotic arm target tracking control method provided by the present invention not only simplifies the control process and improves the robustness and scalability of the system, but also reduces maintenance costs and improves overall efficiency. Its excellent performance and wide application potential give it a good industrialization prospect, indicating that it will be widely used in many fields such as automation, intelligent manufacturing, service robots, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a distributed multi-redundant robotic arm target tracking control method provided in Example 1 of the present invention;

[0015] Figure 2 It is the communication topology diagram of target tracking control of multiple redundant manipulators;

[0016] Figure 3 It is a flow chart of the target tracking control steps of multiple redundant manipulators;

[0017] Figure 4 A schematic diagram of the composition of a distributed multi-redundant robotic arm target tracking control device provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0019] Embodiment 1:

[0020] See also Figure 1 As shown, the distributed multi-redundant robotic arm target tracking method provided in this embodiment mainly includes the following steps:

[0021] Step 1: Use a fully distributed multi-winner-takes-all network to generate decision variables and determine the first k redundant robotic arms with the shortest distance to the target to perform the target tracking task; k is a positive integer.

[0022] Thus, through the above steps, the efficient use of the robot arm can be ensured, and the target position can be quickly adapted to changes. Unlike the traditional target tracking control scheme, this method step does not require complex central coordination during the control process, but achieves target tracking through simple auxiliary variable exchange between the robot arms.

[0023] Step 2: The redundant robot arm calculates the position of the end of the robot arm by using the decision variables in step 1 and combining the redundant robot arm control rate;

[0024] Step 3: Update the redundant robotic arm end position according to the robotic arm end position calculated in step 2, and then execute step 1 until the target is tracked.

[0025] In a specific embodiment, the above step 1 includes:

[0026] The first k robotic arms closest to the target are selected from n redundant robotic arms to control them to perform the target tracking task, and the other redundant robotic arms remain stationary. Compared with all robotic arms tracking one target, this target tracking method greatly improves the working efficiency of the redundant robotic arms. In order to generate decision variables, the fully distributed k-winners take all (k-WTA) network in formula (1) is used. The k-WTA network (1) used has the characteristics of distribution, fast convergence, weak convergence conditions and scalability, and its properties are superior to other distributed k-WTA networks.

[0027]

[0028] Among them, P Ω (·) As shown in (2):

[0029]

[0030] where z i is the output of the k-WTA network, i.e., the decision variable, ε>0∈R, α is a given parameter, q i and λ i It is an intermediate variable, which assists in calculating the decision variable z i ; n robotic arms are topologically connected through sensors or wireless networks; N(i) is the set of neighbor nodes of redundant robotic arm i; a ij is the element in the i-th row and j-th column of the adjacency matrix of the communication topology graph; v i is the input of the k-WTA network.

[0031] Thus, from formula (1), we can see that each robot arm uses the λ of the neighboring node j and q j, can independently calculate its own decision variables z_i, thus eliminating the dependence on the central node, and is a fully distributed method. Single-point robustness and scalability are the advantages of distributed solutions. In other words, this method is the first fully distributed redundant robotic arm target tracking task allocation scheme, which eliminates the dependence on the central node. Each robotic arm can work independently and collaborate with adjacent robotic arms. This distributed feature not only reduces the complexity of the system, but also improves the robustness of the system. Even if a robotic arm fails, it will not affect the operation of the entire system. In addition, this method has excellent scalability. When a new robot needs to join the system, it only needs to exchange information with the adjacent robots, without the need for complex reconfiguration of the entire system. This feature enables the system to easily adapt to the needs of increasing the number of robots in the future.

[0032] In order to make the k-WTA model suitable for target tracking control of redundant manipulators, the selection of the first k maximum inputs is transformed into the selection of the first k redundant manipulators with the smallest distance to the target. Therefore, the input v of formula (1) is defined as i as follows:

[0033]

[0034] Where u i represents the end coordinates of the i-th redundant manipulator, u t are the coordinates of the target; therefore, z i The definition of is shown in formula (4):

[0035]

[0036] In a specific embodiment, in the above step 2, the design process of the redundant robot arm control rate is as follows:

[0037] According to the movement of the robot arm, u i It can be expressed as:

[0038] u i =f i (θ i ) (5)

[0039] represents the joint angle vector of a redundant robot with p joint angles; for the i-th robot, f i (·) is a differentiable nonlinear function with known structure and parameters; along v i The gradient descent can make the end position of the redundant robotic arm approach the target continuously, that is:

[0040]

[0041] Among them, Ji is the Jacobian matrix of robot i; represents the speed of the redundant robot arm joint angle. By controlling the speed of the redundant robot arm joint angle, the end position of the robot arm can be controlled, and the following dynamic formula is obtained:

[0042]

[0043] Combined with the decision variables z generated in step 1 i The final redundant manipulator control law is obtained:

[0044]

[0045] Combining (1) and (8), we can obtain the target tracking control scheme of multiple redundant manipulators:

[0046]

[0047] Among them, z i is a decision variable, when z i =1, it means that the i-th robot is the first k robot closest to the target and performs the target tracking task. Otherwise, z i = 0, the robot arm remains stationary. i and It is an intermediate variable, which assists in calculating the decision variable z i , is the velocity vector of the redundant manipulator joint angle

[0048] According to Euler difference, the continuous time algorithm formula (9) can also be extended to discrete time, where m∈0,1,2,… represents the sampling index, h>0 represents the sampling interval, and h should be set to a small value;

[0049]

[0050] in,

[0051] Therefore, each robotic arm can update its end position according to formula (10) to track the target.

[0052] The following is a further explanation of this method with an application scenario example:

[0053] like Figure 2 As shown in Figure 1, it is the topology diagram of the proposed multi-redundant manipulator target tracking control. Figure 2 For example, the four redundant robotic arms communicate with each other to form an undirected connected graph. Among the four redundant robotic arms, the first two redundant robotic arms closest to the target are selected to track the target, that is, n = 4, k = 2. According to the communication topology graph, the adjacency matrix A of the multi-redundant robotic arm system can be obtained as [aij ], when |ij|≤1, a ij =1, otherwise a ij = 0. So for Figure 2 The topological graph adjacency matrix A is as follows:

[0054]

[0055] The adjacency matrix A can be used to obtain the neighbor node set N(i) of each node.

[0056] Figure 3 This is a flowchart of the target tracking control steps of multiple redundant robotic arms. Figure 3 First, initialize the parameters and variables of the robot arm, set the parameters n = 4, k = 2, α = 0.1, ε = 1e-3, c 0 =10, m=0, h=1e-4. Set the initial variables Where i=1,2,…n.

[0057] Then repeat the following steps:

[0058] 1) Obtain the end position u of the redundant robot arm i through the redundant robot arm sensor i [m] and the target position u t [m] .

[0059] 2) Calculate By v i [m] , using the intermediate information of neighbor robot j The decision variables of redundant manipulator i are calculated through the k-WTA network And intermediate variables

[0060] 3) Calculate the joint angular velocity of the redundant robotic arm through the redundant robotic arm control rate

[0061] 4) The redundant robot arm calculates the joint angular velocity move.

[0062] 5) Let m=m+1.

[0063] Until the target is tracked,

[0064] The present invention proposes an innovative distributed multi-redundant robotic arm target tracking control method, which has significant advantages and brings new possibilities for the development and practical application of robotic arm technology.

[0065] First, our method can intelligently select the first k redundant manipulators closest to the target from n redundant manipulators to track the target. This selection mechanism ensures efficient utilization of the manipulators and can quickly adapt to changes in the target position. Unlike traditional target tracking control schemes, our method does not require complex central coordination during the control process, but achieves target tracking through simple auxiliary variable exchange between manipulators.

[0066] Secondly, this method is the first fully distributed redundant robotic arm target tracking task allocation scheme, which eliminates the dependence on the central node. Each robotic arm can work independently and cooperate with neighboring robotic arms. This distributed feature not only reduces the complexity of the system, but also improves the robustness of the system. Even if a robotic arm fails, it will not affect the operation of the entire system.

[0067] In addition, this method has excellent scalability. When a new robot needs to join the system, it only needs to exchange information with adjacent robots, without the need for complex reconfiguration of the entire system. This feature allows the system to easily adapt to the needs of increasing the number of robots in the future.

[0068] In summary, the distributed multi-redundant manipulator target tracking control method provided by the present invention not only simplifies the control process, improves the robustness and scalability of the system, but also reduces maintenance costs and improves overall efficiency. Its excellent performance and wide application potential give it a good industrialization prospect, indicating that it will be widely used in many fields such as automation, intelligent manufacturing, and service robots.

[0069] Embodiment 2:

[0070] See also Figure 4 As shown, the distributed multi-redundant robot arm target tracking device provided in this embodiment includes a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable on the processor 41, such as a distributed multi-redundant robot arm target tracking program. When the processor 41 executes the computer program 43, the steps of the above embodiment 1 are implemented, such as Figure 1 Steps shown.

[0071] Exemplarily, the computer program 43 may be divided into one or more modules / units, which are stored in the memory 42 and executed by the processor 41 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 43 in the distributed multi-redundant manipulator target tracking device.

[0072] The processor 41 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0073] The memory 42 may be an internal storage unit of the distributed multi-redundant mechanical arm target tracking device, such as a hard disk or memory of the distributed multi-redundant mechanical arm target tracking device. The memory 42 may also be an external storage device of the distributed multi-redundant mechanical arm target tracking device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the distributed multi-redundant mechanical arm target tracking device. Further, the memory 42 may also include both an internal storage unit and an external storage device of the distributed multi-redundant mechanical arm target tracking device. The memory 42 is used to store the computer program and other programs and data required by the distributed multi-redundant mechanical arm target tracking device. The memory 42 may also be used to temporarily store data that has been output or is to be output.

[0074] Embodiment 3:

[0075] This embodiment provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0076] The computer-readable medium shown can be any device that can contain, store, communicate, propagate or transmit a program for use with an instruction execution system, device or apparatus or in conjunction with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable media on which the program can be printed, such as by optically scanning the paper or other medium, then editing, interpreting or processing in other suitable ways as necessary to obtain the program electronically, and then storing it in a computer memory.

[0077] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed multi-redundant robotic arm target tracking method, characterized in that: include: Step 1: Use a fully distributed multi-winner-takes-all network to generate decision variables and determine the first k redundant manipulators with the shortest distance to the target to perform the target tracking task; k is a positive integer; Step 2: The redundant robot arm calculates the position of the end of the robot arm by using the decision variables in step 1 and combining the redundant robot arm control rate; Step 3: Update the redundant robotic arm end position according to the robotic arm end position calculated in step 2, and then execute step 1 until the target is tracked; The step 1 comprises: The first k robotic arms closest to the target are selected from the n redundant robotic arms to control them to perform the target tracking task, while the other redundant robotic arms remain stationary, and a fully distributed multi-winner-takes-all network is used to generate decision variables. The fully distributed multi-winner-takes-all network is shown in formula (1): Among them, P Ω (·) As shown in (2): Among them, z i is the output of the fully distributed winner-takes-all network, i.e., the decision variable, ε>0∈R, α is a given parameter; q i and λ i It is an intermediate variable, which assists in calculating the decision variable z i ; n robotic arms are topologically connected through sensors or wireless networks; N(i) is the set of neighbor nodes of redundant robotic arm i; a ij is the element in the i-th row and j-th column of the adjacency matrix of the communication topology graph; v i is the input of the fully distributed winner-takes-all network; n is a natural number greater than 0; ε is a given parameter in the fully distributed winner-takes-all network, and R represents a set of real numbers; The step 1 also includes: The selection of the first k largest inputs is transformed into the selection of the first k redundant manipulators with the smallest distance to the target. The input v of formula (1) is i as follows: Where u i represents the end coordinates of the i-th redundant manipulator, u t are the coordinates of the target; z i The definition of is shown in formula (4): In step 2, the redundant manipulator control rate is obtained by: According to the movement of the robot arm, u i It is expressed as: you i =f i (i i ) (5) represents the joint angle vector of a redundant robot with p joint angles; for the i-th robot, f i (·) is a differentiable nonlinear function with known structure and parameters, along v i The gradient descent makes the end position of the redundant robotic arm approach the target continuously, that is: Among them, J i is the Jacobian matrix of robot i; represents the speed of the redundant robot arm joint angle. By controlling the speed of the redundant robot arm joint angle, the end position of the robot arm can be controlled, and the following dynamic formula is obtained: Combined with the decision variables z generated in step 1 i Get the final redundant robot arm control rate:

2. The distributed multi-redundant robotic arm target tracking method according to claim 1, characterized in that: Combining equations (1) and (8), we can obtain the target tracking control scheme of multiple redundant manipulators: Among them, z i is a decision variable, when z i = 1, it means that the i-th robot is the first k robot closest to the target and performs the target tracking task; otherwise, z i =0, the robot arm remains stationary; q i and λ i It is an intermediate variable, which assists in calculating the decision variable z i , is the velocity vector of the redundant manipulator joint angles.

3. The distributed multi-redundant robot arm target tracking method according to claim 2, characterized in that: According to Euler difference, the continuous time algorithm formula (9) is extended to discrete time; let m∈0,1,2,… represent the sampling index, and h>0 represent the sampling interval; in, Each robot arm updates its end position according to formula (10) to track the target.

4. A distributed multi-redundant robotic arm target tracking device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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