A multi-manipulator synchronous control system based on position instructions

Abstract

This invention relates to the field of automation control technology and discloses a multi-robotic arm synchronous control system based on position commands. The system includes a trajectory discretization module, a synchronization clock signal generation module, an error evaluation module, an error compensation module, an AI error prediction module, and a trajectory optimization module. It improves synchronization accuracy by introducing a feedback error compensation mechanism and an AI prediction model, significantly reducing position errors during the synchronization process of multiple robotic arms. It is highly adaptable, capable of adapting to robotic arms with different speed characteristics and designing personalized trajectory planning schemes for different robotic arms. It requires no speed control interface; the system relies solely on position information commands for control, making it suitable for robotic arm systems that cannot be controlled by speed.

Description

Technical Field

[0001] This invention relates to the field of automation control technology, and more specifically to a multi-robotic arm synchronous control system based on position commands. Background Technology

[0002] With the rapid development of industrial automation and robotics, multi-robotic arm collaborative operations have been widely used in manufacturing, logistics, assembly, and other fields. Existing multi-robotic arm control systems typically rely on precise speed, acceleration, and position control to ensure that multiple robotic arms can maintain synchronization on their trajectories.

[0003] However, in some applications, the control system of a robotic arm only supports issuing commands through position information and cannot directly control the speed of the robotic arm. Because the speed of the robotic arm is uncontrollable, traditional multi-robotic arm synchronization methods are difficult to apply, making it difficult to achieve synchronized cooperation among multiple robotic arms, thus affecting production efficiency and control accuracy.

[0004] Therefore, how to achieve synchronous control of multiple robotic arms through position information commands when the speed is uncontrollable is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The technical problem to be solved by this invention is: how to solve the problem of synchronous control of multiple robotic arms through position information commands when the speed is uncontrollable, and to provide a multi-robotic arm synchronous control system based on position commands.

[0006] The present invention solves the above-mentioned technical problems through the following technical solution: a multi-robotic arm synchronous control system based on position commands, comprising a trajectory discretization module, a synchronization clock signal generation module, an error evaluation module, an error compensation module, an AI error prediction module, and a trajectory optimization module;

[0007] The trajectory discretization module is used to discretize the target trajectory of each robotic arm into multiple key points, and use the multiple key points as the target position of each robotic arm in each clock cycle.

[0008] The synchronization clock signal generation module is used to generate a global synchronization clock signal so that all robotic arms receive new target position commands within the same clock cycle.

[0009] The evaluation error module compares the actual position of each robotic arm at the end of each clock cycle with the preset target position to obtain the evaluation error;

[0010] The error compensation module adjusts the target position of the robotic arm in the next clock cycle based on the evaluation error of each robotic arm at the end of each clock cycle, and implements error compensation.

[0011] The AI ​​error prediction module predicts the evaluation error for the next clock cycle and dynamically adjusts the target position based on the historical operating data and current state of each robotic arm.

[0012] The trajectory optimization module is used to optimize the trajectory synchronization strategy of multiple robotic arms.

[0013] Optionally, the control system further includes a real-time feedback module, which is used to acquire the actual position of each robotic arm and transmit the actual position to the evaluation error module.

[0014] Optionally, the specific process of predicting the evaluation error of the next clock cycle and dynamically adjusting the target position is as follows:

[0015] Based on historical operating data and current status, an error prediction algorithm is trained through supervised learning or deep learning models, and the error prediction algorithm is used to predict the error trend of each robotic arm in the next clock cycle.

[0016] Based on the predicted error trend, the target position for the next clock cycle is adjusted in advance.

[0017] Optionally, the historical operation data includes the actual position, target position, evaluation error, and error compensation of each robotic arm over multiple preset clock cycles in the past;

[0018] The current state includes the actual position, target position, and evaluation error of each robotic arm within the current clock cycle.

[0019] Optionally, the optimization method for the trajectory synchronization strategy includes trajectory optimization using reinforcement learning and nonlinear trajectory planning.

[0020] Optionally, the trajectory optimization of the reinforcement learning includes optimizing the target trajectories of multiple robotic arms using a preset reinforcement learning model to synchronize the target trajectories of the multiple robotic arms.

[0021] Optionally, the nonlinear trajectory planning includes dynamic trajectory adjustment and cooperative trajectory generation;

[0022] The dynamic trajectory adjustment process involves dynamically generating corresponding nonlinear trajectories based on the different speed characteristics of the robotic arm.

[0023] The collaborative trajectory generation process is based on the AI ​​error prediction module, which dynamically adjusts the trajectories of multiple robotic arms in each clock cycle according to their actual operating trajectories, and generates a preset trajectory adjustment scheme.

[0024] The present invention has the following advantages over the prior art:

[0025] The synchronization accuracy is improved by introducing a feedback error compensation mechanism and an AI prediction model, which can significantly reduce the positional error of multiple robotic arms during the synchronization process.

[0026] It is highly adaptable and can adapt to robotic arms with different speed characteristics, and can design personalized trajectory planning schemes for different robotic arms.

[0027] No speed control interface is required; the system relies solely on position information commands for control, making it suitable for robotic arm systems that cannot be controlled by speed. Attached Figure Description

[0028] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0029] The embodiments of the present invention are described in detail below. These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0030] like Figure 1 As shown, this embodiment provides a technical solution: a multi-robotic arm synchronous control system based on position commands, including a trajectory discretization module, a synchronization clock signal generation module, an error evaluation module, an error compensation module, an AI error prediction module, and a trajectory optimization module;

[0031] The trajectory discretization module is used to discretize the target trajectory of each robotic arm into multiple key points, and use the multiple key points as the target position of each robotic arm in each clock cycle.

[0032] The synchronization clock signal generation module is used to generate a global synchronization clock signal so that all robotic arms receive new target position commands within the same clock cycle.

[0033] The evaluation error module compares the actual position of each robotic arm with the preset target position at the end of each clock cycle to obtain the evaluation error; the control system also includes a real-time feedback module, which is used to obtain the actual position of each robotic arm and transmit the actual position to the evaluation error module.

[0034] The real-time feedback module can be a sensor on the robotic arm (such as an encoder, position sensor, etc.) that can accurately monitor and obtain the actual position of the robotic arm in real time.

[0035] The error compensation module adjusts the target position of each robotic arm in the next clock cycle based on the evaluation error at the end of each clock cycle, thus implementing error compensation. If a robotic arm fails to reach the preset target position on time at the end of a clock cycle, its target position is adjusted in the next cycle, and the amount of position adjustment is the error compensation, gradually restoring synchronization. The adjustment of the target position can be carried out gradually, and the error within one clock cycle can be compensated for in subsequent clock cycles, ultimately achieving synchronization between the actual position and the target position.

[0036] The AI ​​error prediction module predicts the evaluation error for the next clock cycle and dynamically adjusts the target position based on the historical operating data and current state of each robotic arm. The historical operating data includes the actual position, target position, evaluation error, and error compensation of each robotic arm in the past multiple preset clock cycles.

[0037] The current state includes the actual position, target position, and evaluation error of each robotic arm within the current clock cycle.

[0038] The AI ​​error prediction module can provide model data for the AI ​​error prediction model, including historical operating data and current status, thereby making the model's predictions more accurate.

[0039] The specific process by which the AI ​​error prediction model predicts the evaluation error for the next clock cycle and dynamically adjusts the target position is as follows:

[0040] Based on historical operating data and current status, an error prediction algorithm is trained through supervised learning or deep learning models. The error prediction algorithm is then used to predict the error trend of each robotic arm in the next clock cycle. Specifically, a deep neural network is used for error prediction.

[0041] Based on the predicted error trend, the target position for the next clock cycle is adjusted in advance.

[0042] The trajectory optimization module is used to optimize the trajectory synchronization strategy of multiple robotic arms. Specifically, the optimization methods for the trajectory synchronization strategy include reinforcement learning-based trajectory optimization and nonlinear trajectory planning.

[0043] The trajectory optimization of the reinforcement learning includes optimizing the target trajectories of multiple robotic arms using a preset reinforcement learning model to synchronize the target trajectories of multiple robotic arms. The reinforcement learning model is based on reinforcement learning algorithms.

[0044] Specifically, the nonlinear trajectory planning includes dynamic trajectory adjustment and cooperative trajectory generation;

[0045] The dynamic trajectory adjustment process involves dynamically generating corresponding nonlinear trajectories based on the different speed characteristics of the robotic arms. Specifically, a nonlinear trajectory planning algorithm is used to generate personalized nonlinear trajectories for different robotic arms. These nonlinear trajectories may vary in the middle section depending on the current state of the robotic arm, but they are synchronized at key position points (target positions).

[0046] The collaborative trajectory generation process is based on the AI ​​error prediction module, which dynamically adjusts the trajectories of multiple robotic arms in each clock cycle according to their actual operating trajectories, and generates a preset trajectory adjustment scheme.

[0047] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0048] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "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 the present invention. 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. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0049] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A multi-robotic arm synchronous control system based on position commands, characterized in that, It includes a trajectory discretization module, a synchronous clock signal generation module, an error evaluation module, an error compensation module, an AI error prediction module, and a trajectory optimization module; The trajectory discretization module is used to discretize the target trajectory of each robotic arm into multiple key points, and use the multiple key points as the target position of each robotic arm in each clock cycle. The synchronization clock signal generation module is used to generate a global synchronization clock signal so that all robotic arms receive new target position commands within the same clock cycle. The evaluation error module compares the actual position of each robotic arm at the end of each clock cycle with the preset target position to obtain the evaluation error; The error compensation module adjusts the target position of the robotic arm in the next clock cycle based on the evaluation error of each robotic arm at the end of each clock cycle, and implements error compensation. The AI ​​error prediction module predicts the evaluation error for the next clock cycle and dynamically adjusts the target position based on the historical operating data and current state of each robotic arm. The trajectory optimization module is used to optimize the trajectory synchronization strategy of multiple robotic arms.

2. The multi-robotic arm synchronous control system based on position commands according to claim 1, characterized in that: The control system also includes a real-time feedback module, which is used to obtain the actual position of each robotic arm and transmit the actual position to the evaluation error module.

3. The multi-robotic arm synchronous control system based on position commands according to claim 1, characterized in that: The specific process of predicting the evaluation error of the next clock cycle and dynamically adjusting the target position is as follows: Based on historical operating data and current status, an error prediction algorithm is trained through supervised learning or deep learning models, and the error prediction algorithm is used to predict the error trend of each robotic arm in the next clock cycle. Based on the predicted error trend, the target position for the next clock cycle is adjusted in advance.

4. The multi-robotic arm synchronous control system based on position commands according to claim 1, characterized in that: The historical operation data includes the actual position, target position, evaluation error, and error compensation of each robotic arm in multiple preset clock cycles in the past. The current state includes the actual position, target position, and evaluation error of each robotic arm within the current clock cycle.

5. A multi-robotic arm synchronous control system based on position commands according to claim 1, characterized in that: The optimization methods for the trajectory synchronization strategy include trajectory optimization using reinforcement learning and nonlinear trajectory planning.

6. A multi-robotic arm synchronous control system based on position commands according to claim 5, characterized in that: The trajectory optimization of the reinforcement learning includes optimizing the target trajectories of multiple robotic arms using a preset reinforcement learning model, so that the target trajectories of multiple robotic arms are synchronized.

7. A multi-robotic arm synchronous control system based on position commands according to claim 5, characterized in that: The nonlinear trajectory planning includes dynamic trajectory adjustment and collaborative trajectory generation; The dynamic trajectory adjustment process involves dynamically generating corresponding nonlinear trajectories based on the different speed characteristics of the robotic arm; The collaborative trajectory generation process is based on the AI ​​error prediction module, which dynamically adjusts the trajectories of multiple robotic arms in each clock cycle according to their actual operating trajectories, and generates a preset trajectory adjustment scheme.

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