Control system and method for mobile manipulation robot

Abstract

A control system and method for a mobile manipulation robot. The control system for a mobile manipulation robot comprises: a mobile manipulation robot controller, a locomotion control module, an integrated control module, a trajectory planning module, a trajectory decomposing and calculating module, an inverse kinematics model, and a decoupling and calculating module, wherein the locomotion control module is configured to control movement of a mobile manipulation robot body in a locomotion control mode; the integrated control module is configured to coordinate and control the movement of the mobile manipulation robot body and motion of a manipulator in an integrated control mode; the trajectory planning module is configured to receive a task instruction from the mobile manipulation robot controller and plan a trajectory; the trajectory decomposing and calculating module is configured to decompose the planned trajectory outputted by the trajectory planning module; the inverse kinematics model is configured to solve modeling descriptions of the locomotion control module and the integrated control module; and the decoupling and calculating module is configured to decouple an amount of output of the modeling description of the integrated control module solved by the inverse kinematics model into an amount of motion.

Description

A control system and method for a mobile operating robot 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 comply with 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 feature a mobile chassis with a robotic arm mounted on it. However, the internal control system still maintains separate control of the chassis and robotic arm. That is, the robot controller sends control instructions to the chassis controller and the robotic arm controller respectively. The underlying control logic is not based on the overall level of the mobile operating robot. Instead, it 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 can quickly develop products. However, its shortcomings are also very obvious. The main manifestation is that the robot movement and the robotic arm movement are not synchronized and coordinated in an integrated manner. The robot's operating movements are poorly coordinated and inefficient. In some scenarios, it cannot meet the beat requirements of automated production lines. Summary of the Invention

[0004] In order to achieve integrated control of the entire machine and improve the motion coordination and operating efficiency of the mobile operating robot, the present invention discloses a control system and method for the mobile operating 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 robotic arm in the integrated control mode;

[0008] The trajectory planning module is used to receive 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 a motion quantity.

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

[0013] The scenario operation algorithm library includes n executor 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 joint and the movement amount of each joint of the actual robot arm 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 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 control modes based on the planned trajectory, the current position of the mobile robot, and the position of the work object. These control modes include walking control mode and integrated control mode. For example, when the distance between the mobile robot and the work object exceeds a certain range, the controller switches to walking control mode; when the distance between the mobile robot and the work object falls within a certain range, the controller switches to 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, generate 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 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 of the mobile robot body and the movement of each joint of the manipulator arm;

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

[0036] Preferably, the method further includes 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 actual movement amount of each joint of the robotic arm;

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

[0039] S6.A3, apply kinematics calculation to the learning and training results 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, the S6 includes 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 employs two control modes, depending on the distance between the robot and the target object. Outside the specified distance range, the robot's main body is controlled solely; within the range, the robot's main body and the manipulator are coordinated and controlled simultaneously. This control loop, based on coupling and decoupling mechanisms, utilizes different compensation algorithms to achieve integrated coordinated control and improved 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0049] FIG1 is a schematic diagram of a control mode of a control method for a mobile operating robot according to Embodiment 1;

[0050] FIG2 is a control architecture of the mobile operating robot in Example 1;

[0051] FIG3 is a control architecture of the mobile operating robot in Example 2;

[0052] FIG4 is a flow chart of error compensation of the control method for the mobile operating robot in Example 2;

[0053] FIG5 is a control architecture of the mobile operating robot in Example 3;

[0054] FIG6 is a control structure of the mobile operation robot in Example 4. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention. Example

[0056] 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 quantity of the modeling description of the integrated control module solved by the inverse kinematics model into a motion quantity.

[0057] In this embodiment, the control modes are divided into two types: walking control mode and integrated control mode according to the distance between the mobile operating robot and the target object.

[0058] As shown in Figure 1, assuming that the robot wants to move from starting point A to target point C, when the robot moves to the switching point B and the distance between the robot and the target point C is L0, this point is defined as the control mode switching point B.

[0059] During the journey from point A to point B, the walking control module only controls the movement of the mobile robot body, and the robotic arm is in a stationary state;

[0060] During the period from point B to point C, the integrated control module controls the movement of the mobile robot's body and the movement of the robotic arm to work synchronously.

[0061] The control switching point B can be set according to the moving distance, working conditions, actual needs of the mobile operation robot or can be set by itself.

[0062] 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.

[0063] The main advantages of this embodiment are: when the mobile operating robot is relatively close to the target point C, the coordination and synchronization of the robot's arm movement and chassis movement are improved (the chassis moves and the robot arm moves at the same time), the speed and efficiency of the operation are improved, and the smoothness of the operation is improved, so that it can better meet the requirements of the automated production line.

[0064] As shown in FIG2 , the specific control method of this embodiment includes the following steps:

[0065] S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module;

[0066] S2, the trajectory planning module performs trajectory planning after receiving the task instruction;

[0067] 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;

[0068] Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation trajectory;

[0069] 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;

[0070] 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 walking control mode and integrated control mode;

[0071] In this embodiment, when the mobile robot controller determines that the mobile robot is located between point A and point B, it switches to the walking control mode:

[0072] S5A, walking control mode:

[0073] S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system;

[0074] S5A.2, generate navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model;

[0075] S5A.3, input navigation travel instructions to the travel wheel driver;

[0076] S5A.4, the travel wheel driver performs PID-based closed-loop control internally;

[0077] When the mobile robot controller determines that the mobile robot is between point B and point C, it switches to the integrated control mode:

[0078] S5B, integrated control mode;

[0079] S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in Cartesian coordinate space;

[0080] S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module;

[0081] S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement of the mobile robot body and the movement of each joint of the manipulator arm;

[0082] S5B.4, the body movement motion is transformed into the world coordinate system to perform walking control of the mobile operation robot; the arm joint motion is used to perform motion control of each joint of the arm of the mobile operation robot. Example

[0083] In a preferred embodiment 2, a control system for a mobile robot includes a mobile 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. The walking control module is used to control the movement of the mobile robot body in a walking control mode; the integrated control module is used to coordinate the movement of the mobile robot body and the movement of the manipulator arm in the integrated control mode; the trajectory planning module is used to receive task instructions from the mobile 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 quantities; the error calculation module calculates the error between the motion quantity of the body movement and the actual motion quantity of the body movement; and the artificial neural network algorithm module is used to calculate the error between the motion quantity of the manipulator arm joints and the actual motion quantity of each manipulator arm joint or to learn and train actuator compensation parameters to obtain actuator compensation data.

[0084] As shown in FIG3 , the specific control method of this embodiment includes the following steps:

[0085] S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module;

[0086] S2, the trajectory planning module performs trajectory planning after receiving the task instruction;

[0087] 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;

[0088] Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation trajectory;

[0089] 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;

[0090] 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;

[0091] The control modes include walking control mode and integrated control mode;

[0092] S5A, walking control mode:

[0093] S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system;

[0094] S5A.2, generate navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model;

[0095] S5A.3, input navigation travel instructions to the travel wheel driver;

[0096] S5A.4, the travel wheel driver performs PID-based closed-loop control internally;

[0097] S5B, integrated control mode;

[0098] S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in Cartesian coordinate space;

[0099] S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module;

[0100] S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement of the mobile robot body and the movement of each joint of the manipulator arm;

[0101] S5B.4, the body movement motion is transformed into the world coordinate system to perform walking control of the mobile operation robot; the arm joint motion is used to perform motion control of each joint of the arm of the mobile operation robot.

[0102] S6, motion compensation;

[0103] 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 actual movement amount of each joint of the robotic arm;

[0104] S6.A2, input the error value in S6.A1 into the artificial neural network algorithm module for learning and training;

[0105] S6.A3, apply kinematics calculation to the learning and training results to extract the change law component values;

[0106] S6.A4 extracts the change law component value into S5.1 as compensation for the output data.

[0107] This embodiment 2 is a further optimization of embodiment 1. As shown in FIG4 , this embodiment calculates the respective error amounts by inputting the respective motion control amounts (instructions) and the actual motion control amounts (feedback) into 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 couples the change law component value into the trajectory decomposition calculation module at the front end to output the operation motion trajectory data and the collaborative walking trajectory data, so as to compensate for the final error.

[0108] In Figure 4, the physical meanings of the symbols in the figure are as follows:

[0109] : Robot walking instruction parameters, robot arm end joint actuator action instruction parameters;

[0110] : Actual parameters of robot walking and actual parameters of the actuator movement of the end joint of the robotic arm;

[0111] : robot walking error parameters, robot arm end joint actuator motion error parameters;

[0112] : Output compensation amount.

[0113] Compared with Example 1, the beneficial effect of Example 2 is that the operating accuracy and operating stability of the end of the robotic arm will be improved. Example

[0114] 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 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 a motion quantity.

[0115] 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.

[0116] As shown in FIG5 , the specific control method of this embodiment includes the following steps:

[0117] S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module;

[0118] S2, the trajectory planning module performs trajectory planning after receiving the task instruction;

[0119] 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;

[0120] Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation trajectory;

[0121] 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;

[0122] 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;

[0123] The control modes include walking control mode and integrated control mode;

[0124] S5A, walking control mode:

[0125] S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system;

[0126] S5A.2, generate navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model;

[0127] S5A.3, input navigation travel instructions to the travel wheel driver;

[0128] S5A.4, the travel wheel driver performs PID-based closed-loop control internally;

[0129] S5B, integrated control mode;

[0130] S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in Cartesian coordinate space;

[0131] S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module;

[0132] S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement of the mobile robot body and the movement of each joint of the manipulator arm;

[0133] S5B.4, the body movement motion is transformed into the world coordinate system to perform walking control of the mobile operation robot; the arm joint motion is used to perform motion control of each joint of the arm of the mobile operation robot.

[0134] S6, motion compensation;

[0135] S6.B1, the coupling unit extracts the actuator compensation parameters of the actuator algorithm library in the scene operation algorithm library;

[0136] S6.B2, the actuator compensation parameters are then input into the decoupling calculation module as actuator compensation data.

[0137] This embodiment is a further improvement on Embodiment 1. To enable the mobile operating robot to better adapt to the operational requirements of its scenario, this embodiment establishes a scenario operation algorithm library. After selecting an actuator algorithm library from the scenario operation algorithm library, the coupling unit couples the parameters of the actuator algorithm library into the decoupling calculation module, thereby enhancing the robot's motion execution efficiency and performance and reducing the computing resource requirements of the control system.

[0138] 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-effector execution operation action more in line with the operation requirements.

[0139] The following table shows examples of the scenario operation algorithm library.

[0140]

[0141] The table lists four actuator algorithm libraries: module_hand1, module_hand2, module_hand3, and module_hand4. Each actuator algorithm library corresponds to a different end-effector type, with its own operational characteristics and four parameters: the robot's body velocity and acceleration; and the end-effector's velocity and acceleration. The type and number of corresponding parameters in the algorithm library modules can be increased or decreased based on control requirements.

[0142] 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. They are coupled with their original control quantities to become new control quantities, thereby correcting the actual control effect.

[0143] 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 operation results and performance when performing corresponding operations. It should be noted that the 4 parameters are just examples. In actual operation, more compensation parameters can be set to further improve the control accuracy of the mobile operation robot. Example

[0144] 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 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 quantity of the modeling description of the integrated control module solved by the inverse kinematics model into a motion quantity.

[0145] The scenario operation algorithm library includes n actuator algorithm libraries, each of which is configured with actuator compensation parameters. The coupling unit outputs actuator compensation data to the decoupling calculation module, where n is the total number of actuators on the mobile controller robot. The artificial neural network algorithm module is used to train the actuator compensation parameters to obtain actuator compensation data.

[0146] As shown in FIG6 , the specific control method of this embodiment includes the following steps:

[0147] S1, after the robot controller receives the task instruction from the scheduling system, it performs trajectory planning calculation through the trajectory planning module;

[0148] S2, the trajectory planning module performs trajectory planning after receiving the task instruction;

[0149] 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;

[0150] Among them, the integrated control trajectory includes the collaborative walking trajectory and the robot arm operation trajectory;

[0151] 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;

[0152] 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;

[0153] The control modes include walking control mode and integrated control mode;

[0154] S5A, walking control mode:

[0155] S5A.1, the walking control module models and describes the walking control trajectory data in the robot world coordinate system;

[0156] S5A.2, generate navigation walking instructions for the walking device of the mobile operation robot by solving the inverse kinematics model;

[0157] S5A.3, input navigation travel instructions to the travel wheel driver;

[0158] S5A.4, the travel wheel driver performs PID-based closed-loop control internally;

[0159] S5B, integrated control mode;

[0160] S5B.1, the integrated control trajectory module models and describes the robot arm operation motion trajectory data and collaborative walking trajectory data in Cartesian coordinate space;

[0161] S5B.2, after solving the inverse kinematics model, enter the decoupling calculation module;

[0162] S5B.3, the decoupling calculation module decouples the output of the inverse kinematics model into the movement of the mobile robot body and the movement of each joint of the manipulator arm;

[0163] S5B.4, the body movement motion is transformed into the world coordinate system to perform walking control of the mobile operation robot; the arm joint motion is used to perform motion control of each joint of the arm of the mobile operation robot.

[0164] S6, motion compensation;

[0165] 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;

[0166] 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.

[0167] This embodiment is a further optimization of Embodiment 3. In this embodiment, actual operating parameters are input into the terminal execution type and operation feature determination module, and characteristic data calibration or characteristic data filtering, or expression correction, are performed to obtain characteristic data corresponding to a certain scenario operation algorithm library. The data is then input into the artificial neural network learning module. The four parameters of the module algorithm library serve as weight factors in the neural network algorithm. Data training, iteration, and calculation are performed on the actual operating data, and finally coupling compensation data is obtained. The data enters the main control loop through the coupling unit for compensation, thereby achieving the purpose of enhancing the control effect.

[0168] This embodiment enhances the operational stability and accuracy of integrated control, and at the same time, makes the walking of the mobile operating robot and the operation 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 operation target, as in traditional non-integrated control methods, in order to more smoothly execute the end action.

[0169] 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 scope of protection 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 for solving the inverse kinematics model into the motion quantity.

2. A control system for a mobile operating robot according to claim 1, characterized in that: It also includes a scene algorithm library and a 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 method for a mobile operating robot, characterized in that: Using a control system of a mobile operating robot as claimed in claim 3; the specific steps of the method are as follows: 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.

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

6. A control method for a mobile operating robot according to claim 5, 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.

7. A control method for a mobile operating robot according to claim 5, 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.

8. The control method of a mobile operating robot according to claim 5, 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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