Mechanical hand control method and device and electronic equipment

Abstract

This application discloses a robotic arm control method, device, and electronic device. The method includes: acquiring a first task; determining a first neural network based on the first task, wherein the control unit of the robotic arm is configured with multiple pre-trained neural networks, and the first neural network is the neural network matched with the first task; reconstructing the first task using the first neural network to obtain a control strategy for the reconstructed first task, the control strategy including a series of basic control instructions; acquiring control parameters for controlling the robotic arm based on the control strategy; and controlling the robotic arm to perform control actions according to the control parameters, the control parameters including parameters corresponding to the basic control instructions. Since this method utilizes a neural network configured in the robotic arm control unit to achieve spatial position control, it can well adapt to application scenarios without network connectivity and does not require additional computing resources from the control unit itself.

Description

Technical Field

[0001] This application relates to the field of robotic arm technology, and particularly to a robotic arm control method, device, and electronic device. Background Technology

[0002] There are various control methods for robotic arms, such as traditional PID control (proportional, integral, and derivative control) and fuzzy control. PID control is most suitable when a system and the controlled object are not fully understood, or when system parameters cannot be obtained through effective measurement methods. In practice, PI and PD control also exist. A PID controller calculates the control quantity based on the system error using proportional, integral, and derivative functions. In fuzzy control, the input quantity is fuzzified into a fuzzy variable. The fuzzy variable is then used to infer a fuzzy output through fuzzy rules. After defuzzification, a clear output quantity is obtained for control.

[0003] Spatial computing has many applications in the control of robotic arms. However, how to control the spatial position of a robotic arm without a network connection, i.e. with limited computing power, is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in related technologies. To this end, this application proposes a robotic arm control method, device, and electronic device, which can realize flexible and versatile self-learning control of the robotic arm without network connectivity, effectively solving the problem of limited computing power of the robotic arm.

[0005] In a first aspect, this application provides a robotic arm control method, the method comprising:

[0006] A first task is obtained, and a first neural network is determined based on the first task. The control unit is configured with multiple pre-trained neural networks, and the first neural network is the neural network that matches the first task.

[0007] The first task is reconstructed using the first neural network to obtain the control strategy of the reconstructed first task, and the control strategy includes a series of basic control instructions.

[0008] Based on the control strategy, control parameters for controlling the robotic arm are obtained, and the robotic arm is controlled to perform control actions according to the control parameters. The control parameters include parameters corresponding to the basic control commands.

[0009] In some embodiments, the first task of acquiring the robotic arm includes:

[0010] Obtain the first task and the corresponding environmental state information, wherein the environmental state information includes the surrounding environment information and the manipulator's own state information in the simulation environment;

[0011] The process of reconstructing the first task using a pre-trained first neural network to obtain a control strategy for the reconstructed first task includes:

[0012] The surrounding environment information, the self-state information, and the first task are input into the first neural network to obtain the reconstructed control strategy of the first task. The control strategy includes the basic control instructions and the scene control instructions.

[0013] In some embodiments, obtaining control parameters for controlling the robotic arm based on the control strategy, and controlling the robotic arm to perform control actions according to the control parameters, includes:

[0014] Based on the control strategy, control parameters and scene parameters for controlling the robotic arm are obtained, wherein the scene parameters are the parameters corresponding to the scene control commands.

[0015] Predict the spatial pre-operation trajectory of the robotic arm based on the control parameters;

[0016] When the scenario parameters determine that there are no obstacles on the pre-running trajectory in space, the robot arm is controlled to run according to the pre-running trajectory.

[0017] In some embodiments, if the scene parameters determine that there is an obstacle to the pre-running spatial trajectory, the spatial reconstruction running trajectory of the robot is obtained according to the control parameters and the scene parameters, and the robot is controlled to run according to the spatial reconstruction running trajectory.

[0018] In some embodiments, obtaining the spatial reconstruction trajectory of the robotic arm based on the control parameters and the scene parameters, and controlling the robotic arm to run according to the spatial reconstruction trajectory, includes:

[0019] The control parameters are added or deleted according to the scene parameters to obtain the reconstructed control parameters, and the spatial reconstruction operation trajectory is obtained according to the reconstructed control parameters. The robot arm is then controlled to run according to the spatial reconstruction operation trajectory.

[0020] In some embodiments, if there are still operational obstacles in the spatial reconstruction trajectory obtained based on the reconstructed control parameters, an alarm message indicating that the first task cannot be completed is issued.

[0021] In some embodiments, obtaining control parameters for controlling the robotic arm based on the control strategy, and controlling the robotic arm to perform control actions according to the control parameters, includes:

[0022] Based on the control strategy, control parameters and scene parameters for controlling the robotic arm are obtained, wherein the scene parameters are the parameters corresponding to the scene control commands.

[0023] Based on the similarity scene measurement algorithm, the corresponding historical control parameters in historical similar scenes are obtained according to the scene parameters;

[0024] The control parameters are reconstructed based on the historical control parameters to obtain the reconstructed control parameters. The spatial reconstructed running trajectory is obtained based on the reconstructed control parameters, and the robot is controlled to run according to the spatial reconstructed running trajectory.

[0025] In some embodiments, each control parameter corresponds to a basic action, and the step of obtaining the spatial reconstruction trajectory based on the reconstructed control parameters and controlling the robot to run according to the spatial reconstruction trajectory includes:

[0026] The basic movements of the robotic arm and the execution order of each basic movement are obtained based on the reconstructed control parameters, and the robotic arm is controlled to execute the basic movements in sequence according to the execution order to complete the operation of the spatial reconstruction trajectory.

[0027] Secondly, this application provides a robotic arm control device, which includes:

[0028] An acquisition module is used to acquire a first task and determine a first neural network based on the first task. The control unit is configured with multiple pre-trained neural networks, and the first neural network is the neural network that matches the first task.

[0029] The reconstruction module is used to reconstruct the first task using the first neural network to obtain the control strategy of the reconstructed first task, wherein the control strategy includes a series of basic control instructions.

[0030] The control module is used to obtain control parameters for controlling the robot arm based on the control strategy, and to control the robot arm to perform control actions according to the control parameters. The control parameters include parameters corresponding to the basic control commands.

[0031] In some embodiments, the acquisition module is further configured to acquire the first task and the environmental state information corresponding to the first task, wherein the environmental state information includes the surrounding environment information and the manipulator's own state information in the simulation environment;

[0032] The reconstruction module is further configured to input the surrounding environment information, the self-state information, and the first task into the first neural network to obtain the reconstructed control strategy of the first task, wherein the control strategy includes the basic control instructions and the scene control instructions.

[0033] In some embodiments, the control module is further configured to: acquire control parameters and scene parameters for controlling the robotic arm based on the control strategy, wherein the scene parameters are parameters corresponding to the scene control command; predict the spatial pre-running trajectory of the robotic arm based on the control parameters; if the scene parameters determine that there are no obstacles on the spatial pre-running trajectory, control the robotic arm to run according to the spatial pre-running trajectory; if the scene parameters determine that there are obstacles on the spatial pre-running trajectory, obtain the spatial reconstructed running trajectory of the robotic arm based on the control parameters and the scene parameters, and control the robotic arm to run according to the spatial reconstructed running trajectory.

[0034] In some embodiments, the control module is further configured to add or delete control parameters according to the scene parameters to obtain reconstructed control parameters, obtain the spatial reconstruction running trajectory according to the reconstructed control parameters, and control the robot to run according to the spatial reconstruction running trajectory.

[0035] In some embodiments, the control module is further configured to issue an alarm message indicating that the first task cannot be completed if there are still operational obstacles in the spatial reconstruction trajectory obtained based on the reconstructed control parameters.

[0036] In some embodiments, the control module is further configured to: acquire control parameters and scene parameters for controlling the robotic arm based on the control strategy, wherein the scene parameters are parameters corresponding to the scene control commands; obtain historical control parameters corresponding to historical similar scenes based on the scene parameters using a similarity scene measurement algorithm; reconstruct the control parameters based on the historical control parameters to obtain reconstructed control parameters; obtain a spatial reconstruction running trajectory based on the reconstructed control parameters; and control the robotic arm to run according to the spatial reconstruction running trajectory.

[0037] In some embodiments, the control module is further configured to obtain the basic movements of the manipulator and the execution order of each basic movement according to the reconstructed control parameters, and control the manipulator to execute the basic movements sequentially according to the execution order, so as to complete the operation of the spatial reconstruction running trajectory.

[0038] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the robotic arm control method described in the first aspect above.

[0039] Fourthly, this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the robotic arm control method as described in the first aspect above.

[0040] Fifthly, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the robotic arm control method as described in the first aspect.

[0041] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the robotic arm control method as described in the first aspect above.

[0042] The above-described one or more technical solutions in the embodiments of this application have at least the following technical effects:

[0043] The robotic arm control method, device, and electronic device provided in this application embodiment are as follows: The robotic arm control method obtains a first task through the control unit of the robotic arm, selects and determines a first neural network based on the first task, the first neural network being the neural network that matches the first task among multiple pre-trained neural networks configured in the control unit, and reconstructs the first task using the first neural network to obtain a control strategy for the reconstructed first task. The control strategy includes a series of basic control instructions, and control parameters for controlling the robotic arm are obtained based on the series of basic control instructions. Finally, the robotic arm is controlled to perform control actions according to the control parameters to complete the corresponding movement. Since this method uses the neural network configured in the control unit of the robotic arm to complete spatial position control, it can be well adapted to application scenarios without network connectivity, and does not require additional consumption of the control unit's own computing resources, effectively solving the problem of limited computing power for the robotic arm in spatial position control.

[0044] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0045] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0046] Figure 1This is a schematic diagram of the structural frame of the robotic arm provided in an embodiment of this application;

[0047] Figure 2 This is a flowchart illustrating the robotic arm control method provided in an embodiment of this application;

[0048] Figure 3 This is a schematic diagram of the structure of the robotic arm control device provided in the embodiments of this application;

[0049] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0051] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0052] First, a general description of the robotic arm's structural framework will be provided. Please refer to [link / reference]. Figure 1 , Figure 1 The diagram shows the structural framework of the robotic arm. The robotic arm includes a control unit, which contains multiple neural networks and a skill library corresponding to each network. This allows the control unit to match the appropriate neural network from among the multiple networks after a task is input, and then select the skill from the corresponding skill library to complete the task. It is understood that pre-configuring the neural networks and corresponding skill libraries in the control unit does not affect the control unit's original computing resources; rather, it alleviates the burden of calculating control position that would otherwise be handled by the control unit itself.

[0053] The robotic arm control method, device, and electronic device provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios. The robotic arm control method can be applied to a robotic arm and can be executed by the hardware or software within the robotic arm.

[0054] like Figure 2 As shown, this robotic arm control method is applied to the control unit of a robotic arm, and the method includes the following steps:

[0055] S100, obtain the first task of the robotic arm, determine the first neural network based on the first task, wherein the control unit is configured with multiple pre-trained neural networks, and the first neural network is the neural network that matches the first task among the neural networks.

[0056] It is understood that the first task in this embodiment can be obtained by the control unit of the robotic arm, or it can be obtained directly by the service and then transmitted to the control unit of the robotic arm. Specifically, the robotic arm / server receives the first task input by the operator, that is, the first task can be input through the receiving interface of the hardware device; it can also be a pre-input task, from which the robotic arm / server can select the first task; or it can be a pre-set task generation rule, so that the robotic arm can generate the first task according to the generation rule. In some embodiments, the generation rule can be self-learned to form tasks of increasing difficulty. For example, if the initial task is for the robotic arm to clench its fist, the generated first task, which is more difficult than the initial task, can be for the robotic arm to switch between clenching and opening its fist, switching every 3 seconds; another example is that the generated first task, which is even more difficult, can be for the robotic arm to pick up a cup from the table. In some embodiments, the first task can be a control command, which can be an action command such as picking up, lifting, dragging, or pulling.

[0057] It should be noted that the robotic arm in this embodiment can be an intelligent robot. The control unit of the robotic arm may include a main processor, an AI processor, or an image processor, etc., and is not limited here.

[0058] S200, the first task is reconstructed using the first neural network to obtain the control strategy of the reconstructed first task, the control strategy including a series of basic control instructions.

[0059] In some embodiments, after obtaining a first task, the server determines a first neural network based on the task, and then uses the first neural network to reconstruct the first task to obtain the control strategy corresponding to the reconstructed first task. It can be understood that the first neural network can be interpreted as retrieving the path and result for completing the first task from a knowledge base or skill base, and breaking it down into basic control instructions, thereby forming the control strategy for the first task. Each skill in the skill base or knowledge base can be specifically represented as a neural network or as an operational rule. For example, a specific skill in the knowledge base or skill base could be picking up an empty cup with one hand, picking up a watermelon with both hands, or picking up an object weighing more than 10 kg with both hands.

[0060] In some embodiments, the first neural network can be pre-trained on its skill base or knowledge base using different first tasks and then configured into the control unit of the robotic arm. It should be noted that before utilizing the first neural network, a step of acquiring the first neural network may be included, i.e., pre-downloading multiple neural networks from a server and configuring them into the control unit of the robotic arm. Alternatively, they can be directly trained within the control unit of the robotic arm. For example, the control unit of the robotic arm may be configured with five skill bases and five neural networks corresponding to each of the five skill bases, used to perform five tasks: single-handed grasping, single-handed lifting, single-handed throwing, single-handed scratching, and single-handed pushing and pulling. If the control unit of the robotic arm determines that the first task is single-handed grasping, then the robotic arm needs to acquire the neural network corresponding to single-handed grasping. This example is not intended to limit the scope of this solution.

[0061] It is understandable that the control unit can store multiple neural networks and skill libraries. The first neural network can be a neural network trained based on the simulation environment corresponding to the second task. That is, the first neural network can be a mature neural network among at least one pre-trained neural network. Correspondingly, the control unit can determine the skill library corresponding to the first neural network as the skill library. More specifically, the operator can select the first neural network from at least one pre-trained neural network, and then the control unit can obtain the first neural network selected by a person skilled in the art; or the control unit can autonomously select the first neural network from at least one pre-trained neural network, wherein the semantic information of the first task is similar to the semantic information of the second task. Specifically, the similarity of semantic information between the first task and the second task can refer to several factors. First, the semantic information of the first task can be obtained using a neural network and compared to determine their similarity. Second, the constraints obtained from decomposing the first and second tasks can be similar. For example, if the result of decomposing the first task is to lift something, and the constraint of decomposing the second task is to lift something that is particularly heavy or light, then the semantic information of the first and second tasks can be considered similar. Third, the operating environments of the first and second tasks can be similar. For example, if the first task is to lift something, and the second task is to support something, then the semantic information of the first and second tasks can be considered similar. Of course, other methods can also be used to determine the similarity of semantic information between the first and second tasks. The examples here are only for the convenience of understanding this solution and do not exhaustively list all implementation methods.

[0062] In some embodiments, after determining the first task and the type of the first neural network, the control unit may initialize a first neural network and, based on the simulation environment corresponding to the first task, use a reinforcement learning algorithm to initially train a skill library. In another implementation, after determining the first task and the type of the first neural network, the control unit may initialize a first neural network, and then, those skilled in the art may configure at least one skill in the skill library according to the first task. Since the skills in the skill library can be expanded in subsequent steps, the number of skills in the skill library does not need to be particularly large. The skill library contains some basic action components of the embodied robot, such as drinking water, carrying, and walking.

[0063] In some embodiments, step S100 includes: acquiring the first task and the environmental state information corresponding to the first task, wherein the environmental state information includes the surrounding environment information and the manipulator's own state information in the simulation environment.

[0064] In this embodiment, the control unit inputs environmental state information related to the first task into the first neural network to obtain the skills selected by the first neural network from the skill library. Specifically, the environmental state information includes the surrounding environment information of the robot in the simulation environment corresponding to the first task and the robot's own state information. Specifically, it may include map information around the intelligent robot, interaction information of the intelligent robot, movement information of adjacent intelligent robots, etc. As an example, the environmental state information in this embodiment may include positioning information, map information, spatial information, interaction information, task information, operation information, etc. Specifically, attitude, position, coordinates, and other information can be obtained by inertial measurement units, motion sensors, gravity sensors, etc., installed on the robot.

[0065] Step S200 includes: inputting the surrounding environment information, the self-state information, and the first task into the first neural network to obtain the reconstructed control strategy of the first task, wherein the control strategy includes the basic control instructions and the scene control instructions.

[0066] In this embodiment, in addition to considering the instruction information of the first task itself, the surrounding environment information and the robot's own state information are also fully considered. The control strategy reconstructed by considering this information is more in line with the robot's operation, achieving accurate motion control. For example, if the first task is to reach a certain destination, without considering the surrounding environment information, the robot would move directly in a straight line. This way of moving might cause the robot to encounter obstacles and be unable to move. The control strategy that considers the surrounding environment information will avoid obstacles and use a straight-line + turning instruction to control the robot to complete the action. Considering the robot's own state information can avoid conflicts between two tasks. For example, if the robot is currently performing a task, it needs to wait for that task to be completed before executing the first task.

[0067] In some embodiments, the environmental state information can be in the form of images, sequence data, or other data formats. When inputting this environmental state information into the first neural network, the type of the first neural network can be determined according to the data type of the input data. For example, if the input data is image data, the first neural network can be a convolutional neural network (CNN); if the input data is sequence data, the first neural network can be a recurrent neural network (RNN), etc. Other cases will not be listed here.

[0068] In some embodiments, when the first neural network outputs a control strategy, it predicts the skill to be selected based on the input first task and the corresponding environmental state information, and outputs the sequence number of the corresponding skill. The server then selects the appropriate skill from the skill library based on these output sequence numbers. It is understood that a unique sequence number can be pre-configured for each skill in the skill library. Each skill can be understood as the most basic motion control command when the robotic arm performs an action, such as forward, backward, left turn, right turn, etc.

[0069] It is understandable that the process of the first neural network reconstructing the first task is equivalent to decomposing and recombining the first task to obtain a series of basic control instructions.

[0070] S300, based on the control strategy, obtain the control parameters for controlling the robot arm, so as to control the robot arm to perform control actions according to the control parameters.

[0071] In some embodiments, step S300 includes: obtaining the basic actions for controlling the robotic arm and the execution order of each basic action according to the control parameters, and controlling the robotic arm to execute each basic action sequentially according to the execution order.

[0072] Once the control strategy of the robotic arm is obtained, it is equivalent to obtaining a series of basic control instructions for the robotic arm. Each control instruction corresponds to a control parameter. By adjusting the control parameters of the robotic arm, the robotic arm can execute each basic action in sequence according to the execution order, so as to realize the set path and / or action operation.

[0073] Furthermore, in some embodiments, the basic movements of the robotic arm and the execution order of each basic movement are obtained based on the control parameters and / or the scene parameters, and the robotic arm is controlled to execute the basic movements sequentially according to the execution order to complete the corresponding operation. Specifically, when controlling the robotic arm to execute the basic movements sequentially according to the execution order, performance parameters such as time, computing power, and resources used, as well as energy, force, angle, speed, and arm / finger degrees of freedom used in the basic movements, are estimated, and the robotic arm is controlled according to the estimated performance parameters. Specifically, when outputting the control strategy, the degrees of freedom of the robotic arm need to be considered. That is, when determining the control strategy, the relationship between the degrees of freedom required to complete the first task and the degrees of freedom of the robotic arm itself is determined. When the control strategy determines that the degrees of freedom required to complete the first task are greater than the degrees of freedom of the robotic arm itself, the control strategy is further simplified; when the control strategy determines that the degrees of freedom required to complete the first task are less than the degrees of freedom of the robotic arm itself, the control strategy for the first task is executed. When the degrees of freedom required to complete the first task by the control strategy are less than the degrees of freedom of the robotic arm itself, it indicates that the degrees of freedom of the control strategy for the first task are redundant and belong to a better control strategy, which can be learned by the neural network more.

[0074] In some embodiments, step S300 includes: obtaining control parameters and scene parameters for controlling the robotic arm based on the control strategy, wherein the scene parameters are parameters corresponding to the scene control command; predicting the spatial pre-running trajectory of the robotic arm based on the control parameters; if it is determined using the scene parameters that there are no obstacles on the spatial pre-running trajectory, controlling the robotic arm to run according to the spatial pre-running trajectory; if it is determined using the scene parameters that there are obstacles on the spatial pre-running trajectory, obtaining the spatial reconstructed running trajectory of the robotic arm based on the control parameters and the scene parameters, and controlling the robotic arm to run according to the spatial reconstructed running trajectory.

[0075] In this embodiment, the control strategy includes basic control commands and scene control commands. Control parameters are obtained based on the basic control commands, and scene parameters are obtained based on the scene control commands. When controlling the robot based on the control parameters and scene parameters, the scene parameters are first used to determine whether there are any obstacles in the spatial pre-running trajectory predicted by the control parameters. If so, the spatial running trajectory of the robot is reconstructed according to the control parameters and the scene parameters to obtain the spatial reconstructed running trajectory, and the robot is controlled to run according to the spatial reconstructed running trajectory. If not, the robot runs directly according to the spatial pre-running trajectory without reconstruction, avoiding unnecessary parameter addition and deletion processes and improving the operating efficiency of the robot.

[0076] Specifically, in some embodiments, obtaining the spatial reconstruction trajectory of the robotic arm based on the control parameters and the scene parameters, and controlling the robotic arm to run according to the spatial reconstruction trajectory, includes: adding or deleting control parameters based on the scene parameters to obtain reconstructed control parameters, obtaining the spatial reconstruction trajectory based on the reconstructed control parameters, and controlling the robotic arm to run according to the spatial reconstruction trajectory.

[0077] If the pre-trajectory spatial path of the robotic arm, obtained from the control parameters, contains obstacles, the space and the trajectory need to be reconstructed. During reconstruction, the original control parameters are added to or deleted based on the scene parameters to obtain the reconstructed control parameters. The reconstructed spatial trajectory is then derived from these parameters, allowing the robotic arm to move according to the reconstructed trajectory and achieve obstacle avoidance. For example, if the robotic arm needs to move from point A to point B, the pre-trajectory spatial path predicted by the control parameters is a straight line between A and B. However, if the scene parameters indicate an obstacle on the straight line between A and B, the original straight-line control parameters need to be deleted and replaced with a right turn, straight line, left turn, straight line, left turn, straight line, thus enabling the robotic arm to move without obstacles. For example, in the action of drinking water, the goal is to drink water, and the result is to actually drink it. The process involves lifting the cup to drink, while simultaneously paying attention to key parameters such as whether there is water in the cup, the cup's color, material, position, size, weight, and whether there are other objects around the cup. If, based on scene parameters, it is known that there is only one cup on the table and that the cup is occupied, then parameters such as the cup's color, material, size, weight, and the presence of other objects around the cup can be removed. Only the control parameters for cup position recognition and grasping and lifting the cup can be retained, avoiding overly complex and unnecessary movements. As another example, in the above drinking action, the most basic control is lifting the cup. If, based on scene parameters, the current scene is a banquet scene, then the basic control action of toasting can be added to match the current environment.

[0078] It is worth noting that the refactoring can only be performed once, because if the refactoring is performed too many times, the prototype of the first task may be lost, and the first task will not be able to be executed.

[0079] In some embodiments, if there are still operational obstacles in the spatial reconstruction trajectory obtained based on the reconstructed control parameters, an alarm message indicating that the first task cannot be completed is issued.

[0080] In some scenarios, such as those with dense spatial objects, obstacles cannot be avoided even after multiple reconstructions. In such cases, an alarm message will be issued indicating that the first task cannot be completed, informing the user that a suitable path cannot be found to complete the first task and that the user needs to re-enter the first task or abandon the first task.

[0081] In some embodiments, step S300 further includes: obtaining historical control parameters corresponding to historical similar scenes based on scene parameters using a similarity scene measurement algorithm, wherein the scene parameters are parameters corresponding to the scene control command; reconstructing the control parameters based on the historical control parameters to obtain reconstructed control parameters; obtaining the spatial reconstruction running trajectory based on the reconstructed control parameters; and controlling the robot to run according to the spatial reconstruction running trajectory.

[0082] This embodiment can be understood as an equivalent embodiment of the aforementioned method of using scene parameters to determine whether there are any obstacles in the spatial pre-running trajectory predicted by control parameters. This embodiment simplifies the idea of ​​determining the running trajectory using scene parameters, that is, it directly uses scene parameters to find the corresponding historical control parameters in similar historical scenes, and reconstructs the control parameters based on the historical control parameters. Based on the reconstructed control parameters, the spatial reconstructed running trajectory is obtained, and the robot arm is controlled to run according to the spatial reconstructed running trajectory. This embodiment achieves higher efficiency and speed, and improves the control efficiency of the robot arm.

[0083] In some embodiments, step S300 further includes: obtaining the basic movements of the robot arm and the execution order of each basic movement according to the reconstructed control parameters, and controlling the robot arm to execute the basic movements sequentially according to the execution order, so as to complete the operation of the spatial reconstruction running trajectory.

[0084] The robotic arm control method provided in this application obtains a first task through the control unit of the robotic arm, selects and determines a first neural network based on the first task, which is a neural network that matches the first task among multiple pre-trained neural networks configured in the control unit, and reconstructs the first task using the first neural network to obtain a control strategy for the reconstructed first task. The control strategy includes a series of basic control instructions, and control parameters for controlling the robotic arm are obtained based on the series of basic control instructions. Finally, the robotic arm is controlled to perform control actions according to the control parameters to complete the corresponding movement. Since this method uses a neural network configured in the control unit of the robotic arm to complete spatial position control, it can be well adapted to application scenarios without network connectivity, and does not require additional consumption of the control unit's own computing resources, effectively solving the problem of limited computing power for the robotic arm in spatial position control. Furthermore, by using the neural network, self-learning of the robotic arm control can be achieved, meeting the flexible and ever-changing control needs of the robotic arm.

[0085] In some embodiments, based on a reinforcement learning algorithm, the control strategy is used to train a neural network model, obtain feedback parameters, and adjust the feedback parameters to obtain a trained second neural network.

[0086] By using reinforcement learning algorithms, the control strategy is memorized and trained to enable rapid control execution when encountering similar scenarios again.

[0087] It should be noted that when the control unit controls the robotic arm's movements based on the simulation environment corresponding to the first task, it will first complete the first task using at least one skill selected by the first neural network. After the control unit controls the intelligent robotic arm to execute the first task, it will acquire the data from the execution of the first task (such as control parameters and scene parameters), and use a reinforcement learning algorithm to retrain the parameters of the first neural network to obtain an updated first neural network. Of course, the above training process can also be implemented on the server side, thereby reducing the computational burden on the robotic arm.

[0088] More specifically, the server inputs environmental state information into the first neural network to obtain the skill selected by the first neural network. The environmental state information includes the environmental information around the intelligent robot in the simulation environment corresponding to the first task and the intelligent robot's own state information. Through the skill selected by the first neural network to execute the first task, basic control commands are obtained. Then, the intelligent robot can be controlled in the simulator to perform operations corresponding to the basic control commands. During execution, the server performs an execution state acquisition operation for the skill selected by the first neural network every preset time interval until the execution state of the skill selected by the first neural network is "execution completed". The server obtains data generated during the intelligent robot's execution of the operation corresponding to the control commands. The data includes one or more of the intelligent robot's operation path, operation speed, or operation destination. Based on the data, the server updates the parameters of the first neural network using a reinforcement learning algorithm. The concepts of intelligent robot, preset time interval, and execution state have been described in detail above and will not be repeated here. In this embodiment, the server determines whether the skill selected by the first neural network has finished executing by acquiring the execution status of the skill selected by the first neural network every preset time interval. In this way, the server can iteratively update the new skill strategy and the parameters of the new skill in a timely manner based on the operation behavior information of the intelligent robotic arm, which is beneficial to improving the accuracy of the training process.

[0089] In some embodiments, the method further includes: after each basic action is performed, determining whether the first task has been completed; if completed, ending the first task; if not completed, returning to step S100, so as to adjust the control actions of the robot arm in a timely manner according to the current environmental state parameters of the robot arm.

[0090] In determining whether the first task has been completed, it can be based on whether the last basic action of the first task has been completed, or it can be based on the operating status of the robot arm collected by the robot arm's motion sensors.

[0091] In some embodiments, when reconstructing the control parameters of the robotic arm according to the first task, an execution timestamp for each basic action can be generated, and the robotic arm can be controlled to complete the corresponding basic action within the corresponding execution timestamp to improve execution efficiency. If the execution timestamp corresponding to the last basic action ends, the first task is determined to be completed.

[0092] The robotic arm control method provided in this application can be executed by a robotic arm control device. This application uses the example of a robotic arm control device executing the robotic arm control method to illustrate the robotic arm control device provided in this application.

[0093] This application also provides a robotic arm control device, such as... Figure 3 As shown, the robotic arm control device includes: an acquisition module 100, used to acquire a first task and determine a first neural network based on the first task, wherein the control unit is configured with multiple pre-trained neural networks, and the first neural network is the neural network that matches the first task among the neural networks.

[0094] The reconstruction module 200 is used to reconstruct the first task using the first neural network to obtain the control strategy of the reconstructed first task, the control strategy including a series of basic control instructions.

[0095] The control module 300 is used to obtain control parameters for controlling the robot arm based on the control strategy, and to control the robot arm to perform control actions according to the control parameters. The control parameters include parameters corresponding to the basic control instructions.

[0096] The robotic arm control device and method provided in this application embodiment obtain a first task through the robotic arm's control unit, select and determine a first neural network based on the first task, which is a neural network that matches the first task among multiple pre-trained neural networks configured in the control unit, and reconstruct the first task using the first neural network to obtain a control strategy for the reconstructed first task. The control strategy includes a series of basic control instructions, and control parameters for controlling the robotic arm are obtained based on the series of basic control instructions. Finally, the robotic arm is controlled to perform control actions according to the control parameters to complete the corresponding movement. Since this method uses a neural network configured in the robotic arm's control unit to complete spatial position control, it can adapt well to application scenarios without network connectivity and does not require additional consumption of the control unit's own computing resources, effectively solving the problem of limited computing power for robotic arms in spatial position control. Furthermore, by using the neural network, self-learning of robotic arm control can be achieved, meeting the flexible and ever-changing control needs of the robotic arm.

[0097] In some embodiments, the acquisition module is further configured to acquire the first task and the environmental state information corresponding to the first task, wherein the environmental state information includes the surrounding environment information and the manipulator's own state information in the simulation environment;

[0098] The reconstruction module is further configured to input the surrounding environment information, the self-state information, and the first task into the first neural network to obtain the reconstructed control strategy of the first task, wherein the control strategy includes the basic control instructions and the scene control instructions.

[0099] In some embodiments, the control module is further configured to: acquire control parameters and scene parameters for controlling the robotic arm based on the control strategy, wherein the scene parameters are parameters corresponding to the scene control command; predict the spatial pre-running trajectory of the robotic arm based on the control parameters; if the scene parameters determine that there are no obstacles on the spatial pre-running trajectory, control the robotic arm to run according to the spatial pre-running trajectory; if the scene parameters determine that there are obstacles on the spatial pre-running trajectory, obtain the spatial reconstructed running trajectory of the robotic arm based on the control parameters and the scene parameters, and control the robotic arm to run according to the spatial reconstructed running trajectory.

[0100] In some embodiments, the control module is further configured to add or delete control parameters according to the scene parameters to obtain reconstructed control parameters, and obtain the spatial reconstruction operation trajectory according to the reconstructed control parameters, and control the robot to run according to the spatial reconstruction operation trajectory; if there are still operation obstacles in the spatial reconstruction operation trajectory obtained based on the reconstructed control parameters, an alarm message indicating that the first task cannot be completed is issued.

[0101] In some embodiments, the control module is further configured to: acquire control parameters and scene parameters for controlling the robotic arm based on the control strategy, wherein the scene parameters are parameters corresponding to the scene control commands; obtain historical control parameters corresponding to historical similar scenes based on the scene parameters using a similarity scene measurement algorithm; reconstruct the control parameters based on the historical control parameters to obtain reconstructed control parameters; obtain a spatial reconstruction running trajectory based on the reconstructed control parameters; and control the robotic arm to run according to the spatial reconstruction running trajectory.

[0102] In some embodiments, each of the control parameters corresponds to a basic action. The control module is further configured to obtain the basic action of the robot and the execution order of each basic action according to the reconstructed control parameters, and control the robot to execute the basic actions sequentially according to the execution order to complete the operation of the spatial reconstruction trajectory.

[0103] In some embodiments, the model training module trains the first neural network based on a reinforcement learning algorithm using the control strategy, obtains feedback parameters, and adjusts the first neural network according to the feedback parameters to obtain an updated first neural network.

[0104] The robotic arm control device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the specific type of device.

[0105] The robotic arm control device in this application embodiment can be a device with an operating system. This operating system can be a Microsoft (Windows) operating system, an Android operating system, an iOS operating system, or other possible operating systems; this application embodiment does not specifically limit it.

[0106] The robotic arm control device provided in this application embodiment can achieve... Figure 2 To avoid repetition, the various processes implemented in the method embodiments shown will not be described again here.

[0107] The robotic arm control method provided in this application embodiment can be executed by an electronic device or a functional module or entity in an electronic device that can implement the control method. The electronic devices mentioned in this application embodiment include, but are not limited to, mobile phones, tablets, computers, cameras, and wearable devices. The control method provided in this application embodiment is described below using an electronic device as the execution subject.

[0108] In some embodiments, such as Figure 4As shown, this application embodiment also provides an electronic device 800, including a processor 801, a memory 802, and a computer program stored in the memory 802 and executable on the processor 801. When the program is executed by the processor 801, it implements the various processes of the above-described robotic arm control method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0109] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0110] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described robotic arm control method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0111] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0112] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described robotic arm control method.

[0113] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0114] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described robotic arm control method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0115] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0116] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the related technology, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0118] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0119] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0120] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for controlling a robotic arm, characterized in that, The method, applied to the control unit of the robotic arm, includes: A first task is obtained, and a first neural network is determined based on the first task. The control unit is configured with multiple pre-trained neural networks, and the first neural network is the neural network that matches the first task. The first task is reconstructed using the first neural network to obtain a control strategy for the reconstructed first task. The control strategy includes a series of basic control instructions. The process of reconstructing the first task using the first neural network is equivalent to decomposing and recombining the first task to obtain a series of basic control instructions. Based on the control strategy, control parameters for controlling the robotic arm are obtained, and the robotic arm is controlled to perform control actions according to the control parameters. The control parameters include parameters corresponding to the basic control commands. The step of obtaining control parameters for controlling the robotic arm based on the control strategy, and controlling the robotic arm to perform control actions according to the control parameters, includes: Based on the control strategy, control parameters and scene parameters for controlling the robotic arm are obtained, wherein the scene parameters are the parameters corresponding to the scene control commands. Predict the spatial pre-operation trajectory of the robotic arm based on the control parameters; When the scenario parameters determine that there are no obstacles on the pre-running spatial trajectory, the robotic arm is controlled to run according to the pre-running spatial trajectory. If the scene parameters determine that there is an obstacle in the pre-running spatial trajectory, then the spatial reconstruction running trajectory of the robot is obtained according to the control parameters and the scene parameters, and the robot is controlled to run according to the spatial reconstruction running trajectory. The step of obtaining the spatial reconstruction trajectory of the robotic arm based on the control parameters and the scene parameters, and controlling the robotic arm to run according to the spatial reconstruction trajectory, includes: The control parameters are added or deleted according to the scene parameters to obtain the reconstructed control parameters, and the spatial reconstruction operation trajectory is obtained according to the reconstructed control parameters. The robot arm is then controlled to run according to the spatial reconstruction operation trajectory.

2. The method according to claim 1, characterized in that, The acquisition of the first task includes: Obtain the first task and the corresponding environmental state information, wherein the environmental state information includes the surrounding environment information and the manipulator's own state information in the simulation environment; The process of reconstructing the first task using a pre-trained first neural network to obtain a control strategy for the reconstructed first task includes: The surrounding environment information, the self-state information, and the first task are input into the first neural network to obtain the reconstructed control strategy of the first task. The control strategy includes the basic control instructions and the scene control instructions.

3. The method according to claim 1, characterized in that, If the spatial reconstruction trajectory obtained based on the reconstructed control parameters still has operational obstacles, an alarm message indicating that the first task cannot be completed will be issued.

4. The method according to claim 2, characterized in that, The step of obtaining control parameters for controlling the robotic arm based on the control strategy, and controlling the robotic arm to perform control actions according to the control parameters, includes: Based on the control strategy, control parameters and scene parameters for controlling the robotic arm are obtained, wherein the scene parameters are the parameters corresponding to the scene control commands. Based on the similarity scene measurement algorithm, the corresponding historical control parameters in historical similar scenes are obtained according to the scene parameters; The control parameters are reconstructed based on the historical control parameters to obtain the reconstructed control parameters. The spatial reconstructed running trajectory is obtained based on the reconstructed control parameters, and the robot is controlled to run according to the spatial reconstructed running trajectory.

5. The method according to claim 1 or 4, characterized in that, Each control parameter corresponds to a basic action. The step of obtaining the spatial reconstruction trajectory based on the reconstructed control parameters and controlling the robotic arm to run according to the spatial reconstruction trajectory includes: The basic movements of the robotic arm and the execution order of each basic movement are obtained based on the reconstructed control parameters, and the robotic arm is controlled to execute the basic movements in sequence according to the execution order to complete the operation of the spatial reconstruction trajectory.

6. A robotic arm control device, characterized in that, The device includes: An acquisition module is used to acquire a first task and determine a first neural network based on the first task. The control unit of the robotic arm is configured with multiple pre-trained neural networks, and the first neural network is the neural network that matches the first task. The reconstruction module is used to reconstruct the first task using the first neural network to obtain the control strategy of the reconstructed first task. The control strategy includes a series of basic control instructions. The process of the first neural network reconstructing the first task is equivalent to decomposing and recombining the first task to obtain a series of basic control instructions. The control module is used to acquire control parameters for controlling the robotic arm based on the control strategy, and to control the robotic arm to perform control actions according to the control parameters. The control parameters include parameters corresponding to the basic control instructions. The step of obtaining control parameters for controlling the robotic arm based on the control strategy, and controlling the robotic arm to perform control actions according to the control parameters, includes: Based on the control strategy, control parameters and scene parameters for controlling the robotic arm are obtained, wherein the scene parameters are the parameters corresponding to the scene control commands. Predict the spatial pre-operation trajectory of the robotic arm based on the control parameters; When the scenario parameters determine that there are no obstacles on the pre-running spatial trajectory, the robotic arm is controlled to run according to the pre-running spatial trajectory. If the scene parameters determine that there is an obstacle in the pre-running spatial trajectory, then the spatial reconstruction running trajectory of the robot is obtained according to the control parameters and the scene parameters, and the robot is controlled to run according to the spatial reconstruction running trajectory. The step of obtaining the spatial reconstruction trajectory of the robotic arm based on the control parameters and the scene parameters, and controlling the robotic arm to run according to the spatial reconstruction trajectory, includes: The control parameters are added or deleted according to the scene parameters to obtain the reconstructed control parameters, and the spatial reconstruction operation trajectory is obtained according to the reconstructed control parameters. The robot arm is then controlled to run according to the spatial reconstruction operation trajectory.

7. An electronic 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 program, it implements the robotic arm control method as described in any one of claims 1-5.

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