A control system and method for a mobile operating robot

Abstract

The present invention provides a control system and method for a mobile operation robot, the system comprising a mobile operation robot controller, a walking control module, an integrated control module, a trajectory planning module, a trajectory decomposition calculation module, an inverse kinematics model and a decoupling calculation module; the walking control module controls the movement of the mobile operation robot body under the walking control mode; the integrated control module coordinates the movement of the mobile operation robot body and the movement of the mechanical arm under the integrated control mode; the trajectory planning module performs trajectory planning, and the trajectory decomposition calculation module decomposes the planned trajectory; the inverse kinematics model solves the modeling description of the walking control module and the integrated control module; the decoupling calculation module decouples the output of the modeling description of the integrated control module solved by the inverse kinematics model into a motion quantity. The present invention is based on a control loop of coupling and decoupling mechanisms, and uses different compensation algorithms to achieve integrated coordinated control of the robot and performance improvement.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent robot control, and in particular to a control system and method for a mobile operating robot. Background Art

[0002] In the field of intelligent manufacturing, intelligent mobile operating robots are being used more and more widely. In most cases, as part of the production process, the operating response speed, operating efficiency, and movement specifications of mobile operating robots must conform to the production rhythm and requirements of the automated production system in order to achieve continuous production. This places higher demands on the performance of the robots.

[0003] Most mobile operating robots are in the form of a mobile chassis with a robotic arm installed on it. However, the internal control still maintains the situation where the chassis control and the robotic arm control are controlled separately, that is, the robot controller sends control instructions to the chassis controller and the robotic arm controller respectively. The underlying control logic is not controlled from the overall level of the mobile operating robot, but first controls the mobile chassis to move to the destination and stop, and then controls the robotic arm to operate the target object. This control method is simple to implement and the product development speed is fast. However, its shortcomings are also very obvious, mainly manifested in the fact that the robot movement and the robotic arm movement are not synchronized and coordinated in an integrated manner, the robot operation movement is poorly coordinated, and the efficiency is low. In some scenarios, it cannot adapt to the beat requirements of the automated production line. Summary of the invention

[0004] In order to realize the integrated control of the whole machine and improve the motion coordination and working efficiency of the mobile operation robot, the present invention discloses a control system and method of the mobile operation robot. The technical solution of the present invention is implemented as follows:

[0005] A control system for a mobile operating robot, comprising a mobile operating robot controller, a walking control module, an integrated control module, a trajectory decomposition calculation module, an inverse kinematics model and a decoupling calculation module;

[0006] Wherein, the walking control module is used to control the movement of the mobile operation robot body in the walking control mode;

[0007] The integrated control module is used to coordinate and control the movement of the mobile operation robot body and the movement of the mechanical arm in the integrated control mode;

[0008] The trajectory planning module is used to accept task instructions from the mobile operation robot controller and perform trajectory planning;

[0009] The trajectory decomposition calculation module is used to decompose the planned trajectory output by the trajectory planning module;

[0010] The inverse kinematics model is used to solve the modeling description of the walking control module and the integrated control module;

[0011] The decoupling calculation module is used to decouple the output quantity of the modeling description of the integrated control module solved by the inverse kinematics model into the motion quantity.

[0012] Preferably, it also includes a scene operation algorithm library and a coupling unit;

[0013] The scene operation algorithm library includes n actuator algorithm libraries;

[0014] The actuator algorithm library is provided with actuator compensation parameters;

[0015] The coupling unit outputs the actuator compensation data to the decoupling calculation module.

[0016] Preferably, it also includes an error calculation module and an artificial neural network algorithm module;

[0017] The error calculation module calculates the error between the movement amount of the main body and the actual movement amount of the main body;

[0018] The artificial neural network algorithm module is used to calculate the error between the movement amount of the robot arm joints and the movement amount of each actual robot arm joint or to learn and train the actuator compensation parameters to obtain actuator compensation data.

[0019] A control method for a mobile operating robot uses a control system for the mobile operating robot; the specific steps of the method are as follows:

[0020] S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module;

[0021] S2, the trajectory planning module performs trajectory planning after receiving the task instruction;

[0022] S3, the trajectory decomposition calculation module decomposes the trajectory planned by the trajectory planning module into a walking control trajectory and an integrated control trajectory;

[0023] Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation motion trajectory;

[0024] S4, the trajectory decomposition calculation module inputs the walking control trajectory into the walking control module; and at the same time inputs the integrated control trajectory into the integrated control module;

[0025] S5, the mobile robot controller automatically switches the control mode based on the planned trajectory, the current position of the mobile robot, and the position of the work object; the control mode includes a walking control mode and an integrated control mode. For example, when the distance between the mobile robot and the work object is greater than a certain range, it switches to the walking control mode; and when the distance between the mobile robot and the work object is less than a certain range, it switches to the integrated control mode.

[0026] S5A, walking control mode:

[0027] S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system;

[0028] S5A.2, generating navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model;

[0029] S5A.3, input navigation travel instructions to the travel wheel driver;

[0030] S5A.4, the travel wheel driver performs PID-based closed-loop control internally;

[0031] S5B, integrated control mode;

[0032] S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in the Cartesian coordinate space;

[0033] S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module;

[0034] S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement amount of the mobile operation robot body and the movement amount of each joint of the robot arm;

[0035] S5B.4, the body movement amount is transformed into the world coordinate system to control the walking of the mobile operation robot; the robot arm joint movement amount is used to control the movement of each joint of the robot arm of the mobile operation robot.

[0036] Preferably, the method further comprises S6, motion compensation;

[0037] S6.A1, the error calculation module calculates the error between the movement amount of the main body and the actual movement amount of the main body; at the same time, the human error calculation module calculates the error between the movement amount of the joints of the robotic arm and the movement amount of each joint of the actual robotic arm;

[0038] S6.A2, input the error amount in S6.A1 into the artificial neural network algorithm module for learning and training;

[0039] S6.A3, subjecting the learning and training results to kinematics calculations to extract the change law component values;

[0040] S6.A4, extracts the change law component value into S5B.1 as compensation for the output data.

[0041] Preferably, S6 comprises the following steps:

[0042] S6.B1, the coupling unit extracts the actuator compensation parameters of the actuator algorithm library in the scene operation algorithm library;

[0043] S6.B2, the actuator compensation parameters are then input into the decoupling calculation module as actuator compensation data.

[0044] Preferably, the specific steps of S6 are:

[0045] S6.C1, the artificial neural network algorithm module learns and trains the actuator compensation parameters in the scene operation algorithm library to obtain actuator compensation data;

[0046] S6.C2, the coupling unit extracts the actuator compensation data obtained by training the artificial neural network algorithm module and inputs it into the decoupling calculation module as the motion compensation of the mobile operating robot.

[0047] The present invention is divided into two control modes according to the distance between the robot and the target object. Outside the specified distance range, the control method of only controlling the movement of the robot body is adopted; within the range, the control method of executing the coordinated control of the robot body and the mechanical arm at the same time is implemented. Based on the control loop of the coupling and decoupling mechanism, different compensation algorithms are used to achieve the integrated coordinated control of the robot and the improvement of its performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A control mode diagram of the control method of the mobile operation robot in Example 1;

[0050] Figure 2 The control architecture of the mobile operation robot in Example 1;

[0051] Figure 3 The control architecture of the mobile operation robot in Example 2;

[0052] Figure 4is a flow chart of error compensation of the control method of the mobile operation robot in Example 2;

[0053] Figure 5 The control architecture of the mobile operation robot in Example 3;

[0054] Figure 6 This is the control architecture of the mobile operating robot in Example 4. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example

[0057] In a specific embodiment 1, a control system of a mobile operating robot includes a mobile operating robot controller, a walking control module, an integrated control module, a trajectory planning module, a trajectory decomposition calculation module, an inverse kinematics model and a decoupling calculation module; wherein the walking control module is used to control the movement of the mobile operating robot body under the walking control mode; the integrated control module is used to coordinate the control of the movement of the mobile operating robot body and the movement of the robotic arm under the integrated control mode; the trajectory planning module is used to accept task instructions from the mobile operating robot controller and perform trajectory planning; the trajectory decomposition calculation module is used to decompose the planned trajectory output by the trajectory planning module; the inverse kinematics model is used to solve the modeling description of the walking control module and the integrated control module; the decoupling calculation module is used to decouple the output of the modeling description of the integrated control module solved by the inverse kinematics model into motion.

[0058] In this embodiment, the control mode is divided into two types: walking control mode and integrated control mode according to the distance between the mobile operating robot and the target object.

[0059] like Figure 1 As shown, assuming that the robot wants to move from starting point A to target point C, when the robot moves to switching point B and the distance between the robot and target point C is L0, this point is defined as control mode switching point B.

[0060] During the journey from point A to point B, the walking control module only controls the movement of the mobile robot, and the robot arm is in a stationary state;

[0061] During the period from point B to point C, the integrated control module controls the body movement and the mechanical arm movement of the mobile operation robot to work synchronously.

[0062] The control switching point B can be set according to the moving distance, working conditions, actual needs or self-setting of the mobile operation robot.

[0063] The value of distance L0 can be set, depending on the obstacles around the target point C and the operating requirements, such as the robot's driving speed, the robot's operating speed and accuracy, etc.

[0064] The main advantages of this embodiment are: when the mobile operating robot is closer to the target point C, the coordination and synchronization of the mobile operating robot's mechanical arm movement and chassis movement are improved (the chassis moves and the mechanical arm moves at the same time), as well as the speed and efficiency of the operation, and the smoothness of the operation is improved, so that it can better meet the requirements of the automated production line.

[0065] like Figure 2 As shown, the specific control method of this embodiment includes the following steps:

[0066] S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module;

[0067] S2, the trajectory planning module performs trajectory planning after receiving the task instruction;

[0068] S3, the trajectory decomposition calculation module decomposes the trajectory planned by the trajectory planning module into a walking control trajectory and an integrated control trajectory;

[0069] Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation motion trajectory;

[0070] S4, the trajectory decomposition calculation module inputs the walking control trajectory into the walking control module; and at the same time inputs the integrated control trajectory into the integrated control module;

[0071] S5, the mobile operation robot controller automatically switches the control mode based on the planned trajectory, the current position of the mobile operation robot and the position of the work object; the control mode includes a walking control mode and an integrated control mode;

[0072] In this embodiment, when the mobile operation robot controller determines that the mobile operation robot is located between point A and point B, it switches to the walking control mode:

[0073] S5A, walking control mode:

[0074] S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system;

[0075] S5A.2, generating navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model;

[0076] S5A.3, input navigation travel instructions to the travel wheel driver;

[0077] S5A.4, the travel wheel driver performs PID-based closed-loop control internally;

[0078] When the mobile robot controller determines that the mobile robot is between point B and point C, it switches to the integrated control mode:

[0079] S5B, integrated control mode;

[0080] S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in the Cartesian coordinate space;

[0081] S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module;

[0082] S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement amount of the mobile operation robot body and the movement amount of each joint of the robot arm;

[0083] S5B.4, the body movement amount is transformed into the world coordinate system to control the walking of the mobile operation robot; the robot arm joint movement amount is used to control the movement of each joint of the robot arm of the mobile operation robot.

[0084] Example 2

[0085] In a preferred embodiment 2, a control system of a mobile operation robot includes a mobile operation robot controller, a walking control module, an integrated control module, a trajectory planning module, a trajectory decomposition calculation module, an inverse kinematics model, a decoupling calculation module, an error calculation module and an artificial neural network algorithm module; wherein the walking control module is used to control the movement of the mobile operation robot body in the walking control mode; the integrated control module is used to coordinate the movement of the mobile operation robot body and the movement of the mechanical arm in the integrated control mode; the trajectory planning module is used to receive the task instructions of the mobile operation robot controller and perform trajectory planning; the trajectory decomposition calculation module is used to decompose the planned trajectory output by the trajectory planning module; the inverse kinematics model is used to solve the modeling description of the walking control module and the integrated control module; the decoupling calculation module is used to decouple the output of the modeling description of the integrated control module solved by the inverse kinematics model into a motion amount. The error calculation module calculates the error between the motion amount of the body movement and the actual motion amount of the body; the artificial neural network algorithm module is used to calculate the error between the motion amount of the mechanical arm joint and the actual motion amount of each joint of the mechanical arm or to learn and train the actuator compensation parameters to obtain the actuator compensation data.

[0086] like Figure 3 As shown, the specific control method of this embodiment includes the following steps:

[0087] S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module;

[0088] S2, the trajectory planning module performs trajectory planning after receiving the task instruction;

[0089] S3, the trajectory decomposition calculation module decomposes the trajectory planned by the trajectory planning module into a walking control trajectory and an integrated control trajectory;

[0090] Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation motion trajectory;

[0091] S4, the trajectory decomposition calculation module inputs the walking control trajectory into the walking control module; and at the same time inputs the integrated control trajectory into the integrated control module;

[0092] S5, the mobile operation robot controller automatically switches the control mode based on the planned trajectory, the current position of the mobile operation robot, and the position of the work object;

[0093] The control modes include walking control mode and integrated control mode;

[0094] S5A, walking control mode:

[0095] S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system;

[0096] S5A.2, generating navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model;

[0097] S5A.3, input navigation travel instructions to the travel wheel driver;

[0098] S5A.4, the travel wheel driver performs PID-based closed-loop control internally;

[0099] S5B, integrated control mode;

[0100] S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in the Cartesian coordinate space;

[0101] S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module;

[0102] S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement amount of the mobile operation robot body and the movement amount of each joint of the robot arm;

[0103] S5B.4, the body movement amount is transformed into the world coordinate system to control the walking of the mobile operation robot; the robot arm joint movement amount is used to control the movement of each joint of the robot arm of the mobile operation robot.

[0104] S6, motion compensation;

[0105] S6.A1, the error calculation module calculates the error between the movement amount of the main body and the actual movement amount of the main body; at the same time, the human error calculation module calculates the error between the movement amount of the joints of the robotic arm and the movement amount of each joint of the actual robotic arm;

[0106] S6.A2, input the error amount in S6.A1 into the artificial neural network algorithm module for learning and training;

[0107] S6.A3, subjecting the learning and training results to kinematics calculations to extract the change law component values;

[0108] S6.A4, extracts the change law component value into S5.1 as compensation for the output data.

[0109] This embodiment 2 is a further optimization of embodiment 1. Figure 4 As shown, this embodiment calculates the respective error amounts by inputting each motion control amount (instruction) and the actual motion control amount (feedback) in the error calculation module; performs artificial neural network algorithm learning on the error amount data and extracts the change law component value through kinematic solution, and the change law component value is coupled to the trajectory decomposition calculation module at the front end to output the operation action trajectory data and the collaborative walking trajectory data, so as to compensate for the final error.

[0110] Figure 4 In the figure, the physical meanings of the symbols are as follows:

[0111] Robot walking command parameters, robot end joint actuator action command parameters;

[0112] Actual parameters of robot walking and actual parameters of actuator movement at the end joint of the robot arm;

[0113] Robot walking error parameters, robot end joint actuator motion error parameters;

[0114] The amount of compensation for the output.

[0115] Compared with Example 1, the beneficial effect of Example 2 is that the operating accuracy and operating stability of the end of the robot arm will be improved.

[0116] Example 3

[0117] In a preferred embodiment 3, a control system of a mobile operating robot includes a mobile operating robot controller, a walking control module, an integrated control module, a trajectory planning module, a trajectory decomposition calculation module, an inverse kinematics model, a decoupling calculation module, a scene operation algorithm library and a coupling unit; wherein the walking control module is used to control the movement of the mobile operating robot body under the walking control mode; the integrated control module is used to coordinate the control of the movement of the mobile operating robot body and the movement of the robotic arm under the integrated control mode; the trajectory planning module is used to accept task instructions from the mobile operating robot controller and perform trajectory planning; the trajectory decomposition calculation module is used to decompose the planned trajectory output by the trajectory planning module; the inverse kinematics model is used to solve the modeling description of the walking control module and the integrated control module; the decoupling calculation module is used to decouple the output quantity of the modeling description of the integrated control module solved by the inverse kinematics model into motion quantity.

[0118] The scene operation algorithm library includes n actuator algorithm libraries; the actuator algorithm library is set with actuator compensation parameters; the coupling unit outputs the actuator compensation data to the decoupling calculation module; n is the total number of all actuators of the mobile controller robot.

[0119] like Figure 5 As shown, the specific control method of this embodiment includes the following steps:

[0120] S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module;

[0121] S2, the trajectory planning module performs trajectory planning after receiving the task instruction;

[0122] S3, the trajectory decomposition calculation module decomposes the trajectory planned by the trajectory planning module into a walking control trajectory and an integrated control trajectory;

[0123] Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation motion trajectory;

[0124] S4, the trajectory decomposition calculation module inputs the walking control trajectory into the walking control module; and at the same time inputs the integrated control trajectory into the integrated control module;

[0125] S5, the mobile operation robot controller automatically switches the control mode based on the planned trajectory, the current position of the mobile operation robot, and the position of the work object;

[0126] The control modes include walking control mode and integrated control mode;

[0127] S5A, walking control mode:

[0128] S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system;

[0129] S5A.2, generating navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model;

[0130] S5A.3, input navigation travel instructions to the travel wheel driver;

[0131] S5A.4, the travel wheel driver performs PID-based closed-loop control internally;

[0132] S5B, integrated control mode;

[0133] S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in the Cartesian coordinate space;

[0134] S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module;

[0135] S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement amount of the mobile operation robot body and the movement amount of each joint of the robot arm;

[0136] S5B.4, the body movement amount is transformed into the world coordinate system to control the walking of the mobile operation robot; the robot arm joint movement amount is used to control the movement of each joint of the robot arm of the mobile operation robot.

[0137] S6, motion compensation;

[0138] S6.B1, the coupling unit extracts the actuator compensation parameters of the actuator algorithm library in the scene operation algorithm library;

[0139] S6.B2, the actuator compensation parameters are then input into the decoupling calculation module as actuator compensation data.

[0140] This embodiment is a further improvement on Embodiment 1. In order to make the mobile operation robot more adaptable to the operation requirements of the scene, this embodiment establishes a scene operation algorithm library. After selecting a certain actuator algorithm library in the scene operation algorithm library, the coupling unit couples the parameters of the actuator algorithm library into the decoupling calculation module, thereby enhancing the robot's action execution efficiency and performance, and reducing the requirements for the computing resources of the control system.

[0141] The scenario operation algorithm library is mainly determined by the type of robot end effector and the operation characteristics. A better coupling control quantity is designed to make the final end-execution operation action more in line with the operation requirements.

[0142] The following table shows examples of scenario job algorithm libraries.

[0143]

[0144]

[0145] The table lists four actuator algorithm libraries, namely module_hand1, module_hand2, module_hand3, and module_hand4. Each actuator algorithm library corresponds to a different end effector type, has its own operating characteristics, and has four parameters, namely, the robot moving body speed and acceleration; the end effector speed and acceleration; the corresponding parameter types and quantities in the algorithm library module can be increased or decreased according to the control requirements.

[0146] After the actuator algorithm library is selected, the four parameters of the actuator algorithm library are coupled into the decoupling calculation module. In the decoupling calculation module, the parameters of the actuator algorithm library related to the movement of the mobile operation robot body are decoupled into the actuator control quantity of the body movement, and the parameters of the actuator algorithm library related to the robotic arm are decoupled into the actuator control quantity of the body robotic arm, and are coupled with their original control quantities to become new control quantities, thereby correcting the actual control effect.

[0147] Under different operation characteristics, the requirements for the movement speed, acceleration, etc. of the robot body and the end effector of the manipulator are different; each module outputs 4 parameters, which are the corresponding compensation amounts. The compensation amounts are coupled into the decoupling calculation unit to affect the corresponding body movement performance and the end execution performance of the manipulator, thereby adaptively correcting the control performance. This design reduces the requirements for advanced algorithms for robot movements, and can quickly adapt to and improve the robot's operating effects and performance when performing corresponding operations. It should be noted that the 4 parameters are just examples. In actual operations, more compensation parameters can be set to further improve the control accuracy of the mobile operation robot.

[0148] Example 4

[0149] In a preferred embodiment 4, a control system of a mobile operating robot includes a mobile operating robot controller, a walking control module, an integrated control module, a trajectory decomposition calculation module, an inverse kinematics model, a decoupling calculation module, a scene operation algorithm library, a coupling unit and an artificial neural network algorithm module; wherein the walking control module is used to control the movement of the mobile operating robot body under the walking control mode; the integrated control module is used to coordinate the control of the movement of the mobile operating robot body and the movement of the robotic arm under the integrated control mode; the trajectory planning module is used to accept task instructions from the mobile operating robot controller and perform trajectory planning; the trajectory decomposition calculation module is used to decompose the planned trajectory output by the trajectory planning module; the inverse kinematics model is used to solve the modeling description of the walking control module and the integrated control module; the decoupling calculation module is used to decouple the output of the modeling description of the integrated control module solved by the inverse kinematics model into motion.

[0150] The scene operation algorithm library includes n actuator algorithm libraries; the actuator algorithm library is set with actuator compensation parameters; the coupling unit outputs actuator compensation data to the decoupling calculation module; n is the total number of all actuators of the mobile controller robot. The artificial neural network algorithm module is used to learn and train the actuator compensation parameters to obtain actuator compensation data.

[0151] like Figure 6 As shown, the specific control method of this embodiment includes the following steps:

[0152] S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module;

[0153] S2, the trajectory planning module performs trajectory planning after receiving the task instruction;

[0154] S3, the trajectory decomposition calculation module decomposes the trajectory planned by the trajectory planning module into a walking control trajectory and an integrated control trajectory;

[0155] Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation motion trajectory;

[0156] S4, the trajectory decomposition calculation module inputs the walking control trajectory into the walking control module; and at the same time inputs the integrated control trajectory into the integrated control module;

[0157] S5, the mobile operation robot controller automatically switches the control mode based on the planned trajectory, the current position of the mobile operation robot, and the position of the work object;

[0158] The control modes include walking control mode and integrated control mode;

[0159] S5A, walking control mode:

[0160] S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system;

[0161] S5A.2, generating navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model;

[0162] S5A.3, input navigation travel instructions to the travel wheel driver;

[0163] S5A.4, the travel wheel driver performs PID-based closed-loop control internally;

[0164] S5B, integrated control mode;

[0165] S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in the Cartesian coordinate space;

[0166] S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module;

[0167] S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement amount of the mobile operation robot body and the movement amount of each joint of the robot arm;

[0168] S5B.4, the body movement amount is transformed into the world coordinate system to control the walking of the mobile operation robot; the robot arm joint movement amount is used to control the movement of each joint of the robot arm of the mobile operation robot.

[0169] S6, motion compensation;

[0170] S6.C1, the artificial neural network algorithm module learns and trains the actuator compensation parameters in the scene operation algorithm library to obtain actuator compensation data;

[0171] S6.C2, the coupling unit extracts the actuator compensation data obtained by training the artificial neural network algorithm module and inputs it into the decoupling calculation module as the motion compensation of the mobile operating robot.

[0172] This embodiment is a further optimization of embodiment 3. In this embodiment, the actual operation parameters are input into the terminal execution type and operation feature determination module, and the characteristic data calibration or characteristic data filtering or expression correction is performed to obtain the characteristic data corresponding to a certain scene operation algorithm library, and enter the artificial neural network learning module. The four parameters of the module algorithm library are used as weight factors in the neural network algorithm to perform data training, iteration, and calculation on the actual operation data, and finally obtain the coupling compensation data, which enters the main control loop for compensation through the coupling unit, thereby achieving the purpose of enhancing the control effect.

[0173] This embodiment enhances the performance of operational stability and accuracy during integrated control. At the same time, it makes the walking of the mobile operating robot and the operating actions of the robotic arm smoother, and the walking and operating actions are completed in one go. There is no need to readjust or repeatedly adjust the robot's posture when it is about to reach or has reached the operating target in order to more smoothly perform the terminal action as in traditional non-integrated control methods.

[0174] It should be pointed out that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A control system for a mobile operating robot, characterized in that: It includes a mobile operation robot controller, a walking control module, an integrated control module, a trajectory planning module, a trajectory decomposition calculation module, an inverse kinematics model and a decoupling calculation module; Wherein, the walking control module is used to control the movement of the mobile operation robot body in the walking control mode; The integrated control module is used to coordinate and control the movement of the mobile operation robot body and the movement of the mechanical arm in the integrated control mode; The trajectory planning module is used to accept task instructions from the mobile operation robot controller and perform trajectory planning; The trajectory decomposition calculation module is used to decompose the planned trajectory output by the trajectory planning module; The inverse kinematics model is used to solve the modeling description of the walking control module and the integrated control module; The decoupling calculation module is used to decouple the output quantity of the modeling description of the integrated control module solved by the inverse kinematics model into the kinematic quantity; The control method of the control system of the mobile operation robot comprises the following steps: S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module; S2, the trajectory planning module performs trajectory planning after receiving the task instruction; S3, the trajectory decomposition calculation module decomposes the trajectory planned by the trajectory planning module into a walking control trajectory and an integrated control trajectory; Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation motion trajectory; S4, the trajectory decomposition calculation module inputs the walking control trajectory into the walking control module; and at the same time inputs the integrated control trajectory into the integrated control module; S5, the mobile operation robot controller automatically switches the control mode based on the planned trajectory, the current position of the mobile operation robot and the position of the work object; The control modes include walking control mode and integrated control mode; S5A, walking control mode: S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system; S5A.2, generating navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model; S5A.3, input navigation travel instructions to the travel wheel driver; S5A.4, the travel wheel driver performs PID-based closed-loop control internally; S5B, integrated control mode; S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in the Cartesian coordinate space; S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module; S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement amount of the mobile operation robot body and the movement amount of each joint of the robot arm; S5B.4, the body movement amount is transformed into the world coordinate system to control the walking of the mobile operation robot; the robot arm joint movement amount is used to control the movement of each joint of the robot arm of the mobile operation robot.

2. A control system for a mobile operating robot according to claim 1, characterized in that: It also includes scenario operation algorithm library and coupling unit; The scene operation algorithm library includes n actuator algorithm libraries; The actuator algorithm library is provided with actuator compensation parameters; The coupling unit outputs the actuator compensation data to the decoupling calculation module.

3. A control system for a mobile operating robot according to claim 1 or 2, characterized in that: It also includes an error calculation module and an artificial neural network algorithm module; The error calculation module calculates the error between the movement amount of the main body and the actual movement amount of the main body; The artificial neural network algorithm module is used to calculate the error between the movement amount of the robot arm joints and the movement amount of each actual robot arm joint or to learn and train the actuator compensation parameters to obtain actuator compensation data.

4. A control system for a mobile operating robot according to claim 1, characterized in that: Also includes S6, motion compensation.

5. A control system for a mobile operating robot according to claim 4, characterized in that: The S6 comprises the following steps: S6.A1, the error calculation module calculates the error between the movement amount of the main body and the actual movement amount of the main body; at the same time, the human error calculation module calculates the error between the movement amount of the joints of the robotic arm and the movement amount of each joint of the actual robotic arm; S6.A2, input the error amount in S6.A1 into the artificial neural network algorithm module for learning and training; S6.A3, subjecting the learning and training results to kinematics calculations to extract the change law component values; S6.A4, extracts the change law component value into S5B.1 as compensation for the output data.

6. A control system for a mobile operating robot according to claim 4, characterized in that: The S6 comprises the following steps: S6.B1, the coupling unit extracts the actuator compensation parameters of the actuator algorithm library in the scene operation algorithm library; S6.B2, the actuator compensation parameters are then input into the decoupling calculation module as actuator compensation data.

7. A control system for a mobile operating robot according to claim 4, characterized in that: The S6 comprises the following steps: S6.C1, the artificial neural network algorithm module learns and trains the actuator compensation parameters in the scene operation algorithm library to obtain actuator compensation data; S6.C2, the coupling unit extracts the actuator compensation data obtained by training the artificial neural network algorithm module and inputs it into the decoupling calculation module as the motion compensation of the mobile operating robot.

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