A control system for a mobile operation composite robot

Abstract

The present invention belongs to the field of intelligent mobile operation robots, particularly related to the field of mobile operation robot controllers and control systems, and specifically relates to a mobile operation composite robot control system, including a robot master controller and a robot bottom-layer controller; the present invention adopts an overall architecture composed of a robot master controller and a robot bottom-layer controller; the robot master controller is responsible for processing the sensor signals of the entire robot and controlling the entire robot, such as performing trajectory planning calculations, etc.; the robot bottom-layer controller is responsible for processing relevant I / O signals, battery power management, sensing signals, motion control signals, etc. of the robot, as well as performing edge computing and executing the motion control of the mobile platform and the robotic arm. This architecture makes the communication architecture simple, the system architecture compact and concise, the system integration degree high, and the reliability improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent mobile operation robots, particularly to the field of controllers and control systems for mobile operation robots, and specifically relates to a control system for a mobile operation composite robot. Background Art

[0002] Mobile operation composite robots are increasingly widely used in the field of intelligent manufacturing. A mobile operation composite robot essentially consists of a mobile platform and a robotic arm. The mobile platform is used to expand the application range and operation space of the robotic arm. The mobile platform usually has at least two main drive wheels; the robotic arm usually has multiple degrees of freedom and is composed of several servo joints. The controller on the mobile operation composite robot is used to fuse and process various sensor signals and robot trajectory planning, and generate motion control instructions for the mobile platform and the robotic arm to make corresponding actions.

[0003] In the existing technology, the mobile platform generally has a separate controller. This controller receives the control signal from the robot controller, and after parsing and processing, it transmits the signal to the drive wheel servo driver; similarly, the robotic arm controller receives the control signal sent by the robot controller, parses and processes it, and then sends it to each joint servo driver for control. Under the existing technology, the robot controller issues instructions to the robotic arm controller and the mobile platform controller respectively. The robotic arm controller then issues instructions to each joint servo driver of the robotic arm, and the mobile platform controller then issues instructions to the mobile platform drive wheel servo driver. Therefore, at least three controllers are required for a mobile operation composite robot to complete corresponding control. The data interaction between the three controllers will bring certain parsing and operation delays, reducing the operation accuracy of the mobile operation composite robot, resulting in a complex system, more wiring harnesses, and the three controllers occupying more internal space of the robot, which is not conducive to space heat dissipation and reliability improvement. Summary of the Invention

[0004] In view of this, the present invention proposes a control system for a mobile operation composite robot, which adopts an overall architecture composed of a robot master controller and a robot bottom controller; the robot master controller is responsible for processing the sensor signals of the whole robot and controlling the whole robot, such as performing trajectory planning calculations, etc.; the robot bottom controller is responsible for processing relevant I / O signals, battery power management, sensing signals, motion control signals, etc. of the robot, as well as performing edge computing and performing motion control on the mobile platform and the robotic arm. This architecture makes the communication architecture simple, the system architecture compact and concise, the system integration degree high, and the reliability improved.

[0005] In order to achieve the above technical objectives, the specific technical solutions adopted by the present invention are as follows:

[0006] A mobile operation composite robot control system for controlling the production actions of the robot, including those provided on the robot:

[0007] A robot master controller for generating motion control instructions;

[0008] A robot low-level controller communicating with the robot master controller, for generating a mobile platform control instruction and a robotic arm control instruction based on the motion control instruction and respectively transmitting them to the mobile platform and the robotic arm of the robot;

[0009] An edge computing module provided on the robot low-level controller, for performing data analysis and performance prediction based on the historical operation data of the robot.

[0010] Further, the robot low-level controller is built based on an FPGA and a first digital signal processor that communicate with each other; the FPGA is used for processing functional data; the first digital signal processor is used for running algorithms and decision-making programs.

[0011] Further, a sensor information processing module, an I / O information processing module, a battery management module, and a communication processing module are integrated in the FPGA, and an I / O signal / LED signal interface, a sensor signal interface, a battery signal interface, and a communication interface are provided.

[0012] Further, the first digital signal processor is used for running a protection function decision algorithm, a motion instruction parsing algorithm, a battery charge and discharge management algorithm, and a controller function setting algorithm.

[0013] Further, the robot low-level controller further includes a flash memory and a second digital signal processor; the flash memory communicates with the first digital signal processor and the FPGA, for storing the historical operation data of the robot; at least a part of the edge computing module is built based on the second digital signal processor that communicates with the flash memory.

[0014] Further, the edge computing module includes a proximal edge computing module and a distal edge computing module; the proximal edge computing module is provided in the second digital signal processor, for performing data analysis and performance prediction on the simple and short-term historical operation data.

[0015] Further, the second digital signal processor communicates with the robot master controller, and the distal computing module is provided in the robot master controller, for performing data analysis and performance prediction on the complex and long-term historical operation data.

[0016] Further, the proximal edge computing module collects the operation data of the robot and stores it in the flash memory, reads the historical operation data from the flash memory for data analysis and AI learning, performs performance analysis and prediction, and transmits the analysis and prediction results to the communication module.

[0017] Further, the analysis and prediction results include: the battery attenuation rate prediction result, the analysis result of the total energy consumption law extraction curve of the robot, and the analysis and prediction of the vibration frequency of the robotic arm.

[0018] Further, the robot bottom controller further includes a data bus; the FPGA, the first digital signal processor, and the second digital signal processor communicate based on the data bus; the robot bottom controller communicates with the robot main controller, the mobile platform, and the robotic arm based on the communication interface.

[0019] Adopting the above technical solution, the present invention can also bring the following beneficial effects:

[0020] An edge computing module is provided in the robot bottom controller of the present invention. Based on the analysis of the historical operation data of the robot and AI learning, the mobile operation composite robot control system of the present invention can have the performance prediction ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 Schematic diagram of the architecture of the mobile operation composite robot control system in the specific embodiment of the present invention;

[0023] Figure 2 Schematic diagram of the composition framework of the robot bottom controller in the specific embodiment of the present invention;

[0024] Figure 3 An internal physical architecture of the robot bottom controller in the specific embodiment of the present invention;

[0025] Figure 4 Another internal physical architecture of the robot bottom controller in the specific embodiment of the present invention;

[0026] Figure 5 Schematic diagram of the control architecture of the mobile operation composite robot control system in the specific embodiment of the present invention;

[0027] Figure 6It is the motion control instruction parsing and communication architecture of the mobile operation composite robot control system in the specific embodiment of the present invention;

[0028] Figure 7 It is the operation flow chart of the proximal edge computing module in the specific embodiment of the present invention. Specific Embodiment

[0029] The following describes the embodiments of the present disclosure in detail with reference to the accompanying drawings.

[0030] The following illustrates the embodiments of the present disclosure through specific examples. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of them. The present disclosure can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts belong to the scope of protection of the present disclosure.

[0031] It should be noted that the following describes various aspects of the embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, this device and / or this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0032] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present disclosure in a schematic manner. The diagrams only show the components related to the present disclosure, rather than being drawn according to the number, shape and size of the components in actual implementation. The type, quantity and proportion of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0033] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0034] In an embodiment of the present invention, a mobile operation composite robot control system is proposed for controlling the production actions of the robot, such as Figure 1 , 3 , as shown in Figure 4, including the following components provided on the robot:

[0035] A robot master controller for controlling the entire robot and generating motion control instructions;

[0036] A robot low-level controller communicates with the robot master controller and is used to generate a mobile platform control instruction and a robotic arm control instruction based on the motion control instruction and transmit them to the mobile platform and the robotic arm of the robot respectively; an edge computing module is provided on the robot low-level controller, and the edge computing module is used to perform data analysis and performance prediction based on the historical operation data of the robot.

[0037] The robot master controller in this embodiment is the master controller of the mobile operation composite robot, communicates with the outside, is used to receive external commands, call internal data storage or parse external commands, perform trajectory planning calculations, and generate motion control instructions for completing production actions. The motion control instructions include a mobile platform control instruction and a robotic arm control instruction; after receiving the above motion instructions, the robot low-level controller in this embodiment parses them, and at the same time collects sensor data on the robot related to the response actions of the mobile platform and the robotic arm, and finally generates control data that can directly control the action execution components such as the servo controllers on the mobile platform and the robotic arm to complete the above production actions after analyzing the sensor data. The robot low-level controller in this embodiment is responsible for processing relevant I / O signals, battery power management, sensing signals, motion control signals, etc. of the robot, as well as performing edge computing and executing motion control of the mobile platform and the robotic arm. The edge computing module enables the mobile operation composite robot control system in this embodiment to have performance prediction capabilities based on the analysis of the robot's historical operation data and AI learning. This architecture makes the communication architecture simple, the system architecture compact and concise, the system integration degree high, and the reliability improved.

[0038] In this embodiment, as Figure 3 , 4 shown, the robot low-level controller is built based on an FPGA and a first digital signal processor that communicate with each other; the FPGA is used to process functional data; the first digital signal processor is used to run algorithms and decision-making programs.

[0039] In this embodiment, as Figure 2 shown, the FPGA integrates a sensor information processing module, an I / O information processing module, a battery management module, and a communication processing module, and is provided with an I / O signal / LED signal interface, a sensor signal interface, a battery signal interface, and a communication interface.

[0040] In this embodiment, as Figure 3 , 4 shown, the first digital signal processor is used to run protection function decision algorithms, motion instruction parsing algorithms, battery charge and discharge management algorithms, and controller function setting algorithms.

[0041] The control system further includes a flash memory and a second digital signal processor; the flash memory communicates with the first digital signal processor and the FPGA, and is used to store the historical operation data of the robot; at least a part of the edge computing module is built based on the second digital signal processor that communicates with the flash memory.

[0042] In one embodiment, as Figure 3 shown, the edge computing module is all integrated on the second digital signal processor.

[0043] In one embodiment, as Figure 4 shown, the edge computing module includes a proximal edge computing module and a distal edge computing module; the proximal edge computing module is arranged in the second digital signal processor and is used to perform data analysis and performance prediction on the simple and short-term historical operation data;

[0044] The second digital signal processor communicates with the robot master controller, and the distal computing module is arranged in the robot master controller and is used to perform data analysis and performance prediction on the complex and long-term historical operation data.

[0045] In this embodiment, the proximal edge computing module collects the operation data of the robot and stores it in the flash memory, reads the historical operation data from the flash memory for data analysis and AI learning, and then performs performance analysis and prediction, and transmits the analysis and prediction results to the communication module.

[0046] The analysis and prediction results include: battery attenuation rate prediction results, analysis results of the robot's total energy consumption law extraction curve, and analysis and prediction of the vibration frequency of the robotic arm.

[0047] In this embodiment, as Figure 3 , 4 shown, the robot bottom controller further includes a data bus; the FPGA, the first digital signal processor, and the second digital signal processor communicate based on the data bus.

[0048] The robot bottom controller communicates with the robot master controller, the mobile platform, and the robotic arm based on the communication interface.

[0049] The above embodiments disclose an integrated robot low-level controller applicable to a mobile operation composite robot. This control system integrates both the control function of the robotic arm and the control function of the mobile platform, combining the control of the two into an integrated robot low-level controller. This controller receives control instructions from the robot's main controller, and after parsing and processing, sends control instructions to the servo drivers of the drive wheels of the mobile platform and the servo drivers of the robotic arm joints, generating corresponding movement and robotic arm operation actions, such as Figure 1 as shown

[0050] The internal control architecture of this integrated robot low-level controller consists of various modules, such as monitoring, processing, and controlling I / O port signals, sensor signals, battery management modules, communication signals, etc., as Figure 2 shown

[0051] as Figure 2 shown. As shown, each information processing module runs in parallel. The present invention adopts an FPGA / DSP (Digital Signal Processor) hybrid architecture design, and the integrated functional modules run in parallel and in real time to provide high-performance processing capabilities and control speeds.

[0052] Furthermore, the following introduces the specific technical solutions based on the following aspects:

[0053] ◆ The internal physical architecture of the robot low-level controller: Hardware composition;

[0054] ◆ Control architecture: Motion control; Communication module parsing unit;

[0055] ◆ Edge computing: Edge computing process;

[0056] ◆ Function parameter setting of the robot low-level controller;

[0057] 1. The internal physical architecture of the robot low-level controller

[0058] Adopt an FPGA / DSP hybrid design architecture. The internal physical architecture of the robot low-level controller is as Figure 3 shown. The core of the robot low-level controller adopts a DSP / FPGA hybrid structure, and data interaction is carried out through an internal data bus. In the FPGA, a modular parallel processing design is adopted to separately process functional data such as sensor information, I / O information, battery management, and communication processing, and at the same time correspond to data interaction interfaces. The DSP or DSP core is responsible for running various algorithms and decision-making programs, such as protection function decision-making algorithms, motion instruction parsing algorithms, battery charge and discharge management algorithms, and controller function setting algorithms.

[0059] The robot's underlying controller also includes another DSP or DSP core for running edge computing algorithms, and a FlashDisk for storing the robot's historical operation data for edge computing use.

[0060] Furthermore, it should be noted that more complex functions of this edge computing unit can also be implemented by the robot's main controller. The edge computing DSP or DSP core only performs operations such as data analysis and performance prediction on relatively simple and short-term data (proximal edge computing). Longer-term and more data will be realized by the upper-level robot control with embedded edge computing algorithms (remote edge computing), as Figure 4 shown.

[0061] 2. Control Architecture

[0062] As Figure 5 shown, the core function of the robot's underlying controller is to receive motion control instructions from the robot's main controller and parse them into motion control instructions for the mobile robot's drive wheels and robotic arm, so that the robot can complete the planned movement and operation.

[0063] The robot's main controller is usually based on an industrial computer and runs the robot operating system ROS. The robot operating system generates a robot operation path plan according to information from lidar sensors and vision sensors, and issues control instructions to the mobile robot controller in a communication manner. The mobile robot controller parses the instructions and sends the motion control instructions to each servo driver through the communication unit.

[0064] The communication method between the industrial computer and the mobile robot controller is usually Ethernet, CanOpen or EtherCat.

[0065] As shown in Figure 6 , after the robot controller receives the motion control instructions transmitted by the robot's main controller, it parses the motion control instructions. One of the main contents of the parsing is to distinguish the control instructions for the mobile platform and the robotic arm, and convert them into motion control instructions adapted to the servo drivers at the execution end. In addition, according to the control instructions, using the high-speed parallel and high-speed processing capabilities of the FPGA, interpolation operation processing is performed on the motion control instructions, and then the control instructions are transmitted to each servo driver through the communication bus.

[0066] In this technical solution, various servo drivers and encoders can be adapted through software settings.

[0067] 3. Edge Computing

[0068] The integrated robot bottom controller integrates another DSP or DSP core, which is dedicated to running edge computing algorithms. As Figure 7 shown in the proximal edge computing algorithm block diagram.

[0069] As Figure 7 shown, the robot operation data is stored in a large-capacity FlashDisk in real time. The edge computing DSP or DSP core reads the historical operation data, such as battery energy consumption data, robot energy consumption data, robotic arm joint motor current and voltage data. The DSP extracts and analyzes these data at certain time intervals, and uses AI algorithms to train and learn the data to obtain relevant operation law curves, which are used as the basis for robot performance prediction to predict relevant performance indicators. The predicted data can be transmitted to the communication module for the robot master controller or external devices to read for further analysis.

[0070] As Figure 7 shown in the proximal edge computing content, by analyzing the battery historical data, the battery attenuation rate is predicted, providing a basis for maintenance.

[0071] By analyzing and learning the historical data of the robot energy consumption status, the total energy consumption law curve of the robot can be extracted. This curve actually reflects the operation law of the robot. From this law, information such as the energy consumption peak value and energy consumption period of the robot can be seen, so as to correspond to the actual operation process, providing a basis for further optimizing the operation process or improving the process.

[0072] By analyzing and intelligently learning the current data of each servo joint of the robotic arm, the vibration information of the robotic arm can be extracted, providing a basis for optimizing control and trajectory planning.

[0073] More complex and more data analysis functions can be completed by the robot master controller. The robot master controller collects a large amount of operation data by means of communication reading. Relying on the powerful data processing ability of the industrial computer core processor and the advantage of more data storage space, it can analyze, learn and predict more data, providing strong help for equipment maintenance, performance prediction, etc.

[0074] 4. Controller Function Parameter Setting

[0075] The controller can be set through the upper computer software in a communication manner. The main functions that can be set are as follows:

[0076] Robot axis number setting; it can support up to a 6-axis robotic arm.

[0077] Mobile platform drive wheel number setting; it can support up to 4 active drive wheel controls.

[0078] Communication settings with the robot master controller; supports Ethernet / CanOpen / EtherCat communication.

[0079] Communication settings with the servo driver; supports CanOpen / EtherCat / RS485 communication.

[0080] Parameter settings for various sensors / encoders; supports input of multiple lidar sensors, multiple ultrasonic sensors, multiple 2D / 3D vision sensors, etc.

[0081] Battery management parameter settings; supports input in the voltage range of 24V to 72V.

[0082] Maximum movement speed / acceleration settings; supports setting the maximum speed and acceleration of each servo joint of the mobile platform and the robotic arm as the over-limit protection setting value.

[0083] Robotic arm limit parameter settings;

[0084] Safety I / O signal polarity parameter settings; supports setting the access function of various switch signals and control switch signals.

[0085] As described above, it is only the specific implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present disclosure should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A mobile operation composite robot control system for controlling the production actions of the robot, Characterized in that: It includes components provided on the robot: A robot general controller for generating motion control instructions; A robot low-level controller, which communicates with the robot general controller and is used to generate a mobile platform control instruction and a robotic arm control instruction based on the motion control instruction and transmit them to the mobile platform and the robotic arm of the robot respectively; the core of the robot low-level controller adopts a DSP / FPGA hybrid structure and conducts data interaction through an internal data bus; among them, modular parallel processing design is adopted in the FPGA; the DSP or the DSP core is responsible for algorithm operation and decision-making procedures; The robot low-level controller also includes a flash memory and a second digital signal processor; An edge computing module is provided on the robot low-level controller for data analysis and performance prediction based on the historical operation data of the robot; at least a part of the edge computing module is built based on the second digital signal processor communicating with the flash memory; the edge computing module includes a proximal edge computing module and a distal edge computing module; Among them, the proximal edge computing module is provided in the second digital signal processor and is used for data analysis and performance prediction of the simple and short-term historical operation data; The distal edge computing module is provided in the robot general controller and is used for data analysis and performance prediction of the complex and long-term historical operation data.

2. The mobile operation composite robot control system according to claim 1, Characterized in that, The robot low-level controller is built based on an FPGA and a first digital signal processor that communicate with each other; the FPGA is used to process functional data; the first digital signal processor is used to run algorithms and decision-making procedures; the flash memory communicates with the first digital signal processor and the FPGA and is used to store the historical operation data of the robot.

3. The mobile operation composite robot control system according to claim 2, Characterized in that, A sensor information processing module, an I / O information processing module, a battery management module and a communication processing module are integrated in the FPGA, and an I / O signal / LED signal interface, a sensor signal interface, a battery signal interface and a communication interface are provided.

4. The mobile operation composite robot control system according to claim 2, Characterized in that, The first digital signal processor is used to run protection function decision-making algorithms, motion instruction parsing algorithms, battery charge and discharge management algorithms and controller function setting algorithms.

5. The mobile operation composite robot control system according to claim 1, Characterized in that, The second digital signal processor communicates with the robot general controller.

6. The mobile operation composite robot control system according to claim 1, Characterized in that, The proximal edge computing module collects the operation data of the robot and stores it in the flash memory. The historical operation data is read from the flash memory for data analysis and AI learning, and then performance analysis and prediction are carried out. The analysis and prediction results are transmitted to the communication module.

7. The mobile operation composite robot control system according to claim 6, wherein, the analysis and prediction results include: the battery attenuation rate prediction result, the analysis result of the robot's total energy consumption law extraction curve, and the analysis and prediction of the manipulator vibration frequency.

8. The mobile operation composite robot control system according to claim 3, wherein, the robot bottom controller further includes a data bus; the FPGA, the first digital signal processor and the second digital signal processor communicate based on the data bus; the robot bottom controller communicates with the robot main controller, the mobile platform and the manipulator based on the communication interface.

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